Methods and systems for optimizing the performance of a vessel

Abstract

Methods and server systems for optimizing the performance of a vessel are described herein. The method performed by a server system includes accessing a set of vessel operating parameters recorded at predefined intervals from a vessel and an energy consumption prediction for at least one performance condition from a database. The method further includes determining an actual energy consumption of the vessel for the at least one performance condition based, at least in part, on the set of vessel operating parameters. The method further includes computing a deviation between the actual energy consumption and the energy consumption prediction. The method further includes in response to determining that the deviation is equal to or greater than a threshold deviation value, generating at least one alert indicating the presence of the deviation in the vessel.

Description

METHODS AND SYSTEMS FOR OPTIMIZING THE PERFORMANCE OF A VESSELTECHNICAL FIELD

[0001] The present disclosure relates to optimizing the performance of a vessel and, more particularly, to electronic methods and complex processing systems for generating an early alert to the vessel and a recommendation to improve the performance of the vessel.BACKGROUND

[0002] The shipping industry is known as the backbone of global trade or commerce. This global trade is quite volatile and relies on the shipping industry to maintain prices for various commodities across the globe. Therefore, the shipping industry must operate efficiently through cost-effective operations to ensure stability in global commodity prices. An important aspect of maintaining this efficiency is to ensure that the energy consumption of a vessel remains ideal during its journey. As may be understood, energy consumption in the form of fuel consumption is one of the key metrics used by the shipping industry in monitoring their maritime operations. Generally, in the shipping industry, energy consumption is computed in terms of Specific Fuel Oil Consumption (SFOC). The term ‘Specific Fuel Oil Consumption’ or ‘SFOC’ can be defined as the amount of fuel consumed per unit of power output from an engine such as the main or auxiliary engine of the vessel {e.g., a maritime vessel). Generally, vessel operators perform predictions to estimate the energy consumption of their vessel for monitoring the operation of the said vessel.

[0003] However, the existing system is unable to provide timely alerts or triggers when machinery performance ( / .e., deviation in SFOC) deviates from established baselines. The absence of early alerts may result in delayed responses to critical performance issues, and impact fuel efficiency, and operational costs. The delay hinders a holistic assessment of the efficiency of the vessel and limits the ability of the system to handle a growing volume of data. Further, there is a lack of comprehensive monitoring tools for assessing machinery performance, especially in terms of auxiliary engines, auxiliary boilers, and waste heat recovery systems.

[0004] In addition, conventionally, the communication between the vessels and the shore operators is conducted primarily through E-mail and phone calls. This may result in delays in resolving issues and making decisions. The absence of direct communication channels between the vessels and the shore operators poses challenges for shore operators in delivering timely guidance and support to the vessel operators ( / .e., crews), particularly during critical performance deviations.P24-056PCT1

[0005] Thus, it is desirable to find technological solutions for early alert of the vessel upon detecting a deviation in the energy consumption (i.e., SFOC) and sending recommendations to the vessel to resolve the cause of the deviation in the vessel.SUMMARY

[0006] There exists a need for techniques to overcome one or more limitations stated above such as the adverse impact on the performance of the vessel due to lack of early alert mechanism, absence of comprehensive monitoring, limited communication channels, fuel wastage, and so on.

[0007] Various embodiments of the present disclosure provide methods and systems for monitoring the performance of the vessel and providing an early alert to the vessel upon detecting a deviation in the vessel's performance. Further, at least one operational recommendation and / or at least one technical recommendation are provided to the operator(s) of the vessel and shore for improving the performance of the vessel by addressing the cause of the said deviation.

[0008] To achieve the above and other objectives of the present disclosure, in one aspect, a computer-implemented method for predicting energy consumption of a vessel is disclosed. The method may be performed by a server system located onboard or off board the vessel. The method includes accessing a set of vessel operating parameters recorded at predefined intervals from a vessel and an energy consumption prediction for at least one performance condition from a database. The method further includes determining an actual energy consumption of the vessel for the at least one performance condition based, at least in part, on the set of vessel operating parameters. The method further includes computing a deviation between the actual energy consumption and the energy consumption prediction. The method further includes in response to determining that the deviation is equal to or greater than a threshold deviation value, generating at least one alert indicating the presence of the deviation in the vessel.

[0009] An advantage of some embodiments is that the at least one alert indicating the presence of the deviation can be generated to provide an early alert to the vessel operator. The early alert is rendered to the vessel operator so that the vessel operator can take the necessary steps to resolve the presence of deviation. To that end, the early alert reduces the delay in response to performance issues and positively impacts fuel efficiency while reducing operational costs.

[0010] In an aspect, the at least one alert is generated in response to determining that the deviation is equal to or greater than a threshold deviation value for a predefined timeP24-056PCT1period. The threshold deviation value represents a reference value over which the deviation is considered to be affecting the performance of the vessel 104. For instance, the threshold deviation value may be set as 5% from the energy consumption prediction.

[0011] An advantage of some embodiments is that by determining the deviation for the predetermined time period, only valid alerts are generated to indicate the deviation to the vessel operator. For instance, the set of vessel operating parameters is recorded for the predefined intervals ( / .e., every 10 minutes). The deviation is determined for the predefined time period ( / .e., every 10 minutes within 1 hour) and the at least one alert is generated only when the deviation exists for the predefined time period ( / .e., 1 hour). This prevents the generation of false alerts.

[0012] In an aspect, the method further includes determining at least one cause of the deviation based, at least in part, on the set of vessel operating parameters. The method further includes generating at least one recommendation for resolving the at least one cause based, at least in part, on a set of predefined rules.

[0013] An advantage of some embodiments is that by determining at least one cause of the deviation, the at least one recommendation related to the cause of the deviation can be generated. Another advantage of some embodiments is that generating at least one recommendation for resolving the at least one cause, allows the vessel operator to rectify the at least one cause of the deviation. The at least one recommendation can help to improve the fuel efficiency of the vessel and reduce the deviation in energy consumption of the vessel. As the at least one recommendation is timely communicated to the vessel, impacts on the fuel efficiency and operational costs of the vessel can be reduced.

[0014] In an aspect, the step of determining the at least one cause of the deviation includes identifying at least one operational cause, at least one technical cause or a combination thereof based, at least in part, on the set of vessel operating parameters.

[0015] An advantage of some embodiments is that by identifying the type of cause for the deviation, specific corrective actions can be taken to effectively and quickly resolve the cause of deviation.

[0016] In an aspect, the step of generating the at least one recommendation includes accessing a vessel performance report associated with the vessel. The step further includes in response to determining that the at least one cause is a technical cause, identifying a subset of non-standard vessel parameters from the set of vessel operating parameters based, at least in part, on the vessel performance report and a set of technical performance thresholds. The step further includes determining a set of corrective actions to be performed to addressP24-056PCT1the technical cause based, at least in part, on the subset of non-standard vessel parameters and the set of predefined rules. The step further includes determining the at least one recommendation for resolving the technical cause based, at least in part, on the set of corrective actions.

[0017] An advantage of some embodiments is that identifying the subset of nonstandard parameters helps to identify or detect faulty equipment or parts within the vessel. Using the subset of non-standard vessel parameters and the set of predefined rules, the set of corrective actions for resolving the at least one technical cause can be efficiently determined, thereby reducing the deviation in energy consumption and improving the fuel efficiency of the vessel.

[0018] In an aspect, the method further includes in response to determining that no corrective action can be performed to address the technical cause, requesting an operator of the vessel to regenerate the vessel performance report.

[0019] An advantage of some embodiments is that in case, if the at least one operational cause and the at least one technical cause are not resolved by the at least one technical recommendation and the at least one operational recommendation, the cause of the deviation in energy consumption is identified by conducting the vessel performance report. By regenerating the vessel performance report, the cause of deviation of the vessel performance may be identified. The vessel operator may perform one or more actions based on the one or more warnings indicated in the regenerated vessel performance report, to resolve the cause of the deviation.

[0020] In an aspect, the step of generating the at least one recommendation includes in response to determining that the at least one cause is an operational cause, identifying a subset of non-standard vessel parameters from the set of vessel operating parameters based, at least in part, on a set of operational performance thresholds. The step further includes determining a set of corrective actions to be performed to address the operational cause based, at least in part, on the subset of non-standard vessel parameters and the set of predefined rules. The step further includes determining the at least one recommendation for resolving the operational cause based, at least in part, on the set of corrective actions.

[0021] An advantage of some embodiments is that by using the subset of nonstandard vessel parameters and the set of predefined rules, the set of corrective actions for resolving the at least one operational cause can be efficiently determined, thereby reducing the deviation in energy consumption and improving the fuel efficiency of the vessel.

[0022] In an aspect, the step further includes facilitating the transmission of the leastP24-056PCT1one recommendation to an operator of the vessel.

[0023] An advantage of some embodiments is that transmitting the at least one recommendation to the operator of the vessel allows the vessel operator to take necessary corrective actions to overcome the deviation due to at least one technical cause and the at least one operational cause. This reduces the deviation in energy consumption and improves the fuel efficiency of the vessel. As the at least one recommendation is timely communicated to the vessel operator, impacts on the fuel efficiency and operational costs of the vessel can be reduced.

[0024] In an aspect, the method further includes receiving at least one status information from an operator of the vessel, the at least one status information indicating one or more actions performed by the operator in response to the receiving the at least one recommendation. The method further includes performing at least one of: in response to determining that the one or more actions comply with the at least one recommendation, changing a status of the at least one alert, or in response to determining that the one or more actions indicate a request for placing the alert on hold, setting an on-hold status to the at least one alert.

[0025] An advantage of some embodiments is that the status of the alert may be changed depending on compliance with the at least one recommendation by the operator of the vessel. If the date and time to perform the at least one recommendation is scheduled by the operator of the vessel, the status of the alert may be set as on-hold or awaiting. If the at least one recommendation is performed by the operator of the vessel, the status of the alert may be set as resolved or closed. Thus, changing the status of the alert upon compiling with the one or more actions associated with the vessel allows an operator at a shore to easily manage the maintenance of the vessel.

[0026] In an aspect, the step of changing the status of the at least one alert includes accessing an updated set of vessel operating parameters recorded at predefined intervals for a new cycle from the vessel and an updated energy consumption prediction for the at least one performance condition for the new cycle from the database. The step further includes determining an updated actual energy consumption for the at least one performance condition for the new cycle based, at least in part, on the set of vessel operating parameters. The step further includes computing an updated deviation between the updated actual energy consumption and the updated energy consumption prediction. The step further includes performing at least one of: in response to determining that the updated deviation is lower than the threshold deviation value, setting a resolved status to the at least one updated alert, or in response to determining that the updated deviation is equal to or greater than the thresholdP24-056PCT1deviation value, generating at least one updated recommendation.

[0027] An advantage of some embodiments is that the status of the alert may be changed depending on the updated deviation computed between the updated actual energy consumption and the energy consumption prediction. Determining the updated deviation allows continuous monitoring of the vessel. Further, at least one updated recommendation may be generated to overcome fuel wastage due to the updated deviation in energy consumption.

[0028] In an aspect, the step of accessing the set of vessel operating parameters includes recording a plurality of vessel operating parameters from at least one data source associated with the vessel at one or more frequencies. The step further includes aggregating the plurality of recorded vessel operating parameters at the predefined intervals. The step further includes extracting the set of vessel operating parameters from the plurality of aggregated vessel operating parameters based, at least in part, on stability criteria associated with the vessel, wherein the stability criteria define one or more stable operating conditions for the vessel.

[0029] An advantage of some embodiments is that aggregating the plurality of vessel operating parameters recorded at one or more frequencies into predefined intervals improves the data processing efficiency. Further, extracting vessel operating parameters ensures that the process for determining the energy consumption can be performed for the stable operating conditions vessel. This aspect ensures that temporary transients, weather conditions, etc., among other temporary unstable conditions, don’t affect the determination of deviation of energy consumption associated with the vessel.

[0030] In an aspect, the step of accessing the energy consumption prediction includes accessing the set of vessel operating parameters recorded at the predefined intervals from the database. The step further includes generating a set of features based, at least in part, on the set of vessel operating parameters. The step further includes generating and storing, by a prediction model, the energy consumption prediction for the at least one performance condition in the database based, at least in part, on applying the set of features on the prediction model.

[0031] An advantage of some embodiments is that the energy consumption prediction for the at least one performance condition provides an expected energy consumption of the vessel without significant deviation in the vessel. By comparing the energy consumption prediction with the actual energy consumption, the exact deviation of the vessel performance over the expected energy consumption can be determined. Another advantage of some embodiments is that the energy consumption prediction is accurately determined using theP24-056PCT1prediction model.

[0032] In an aspect, the step of accessing the energy consumption prediction includes accessing a set of baseline vessel operating parameters from the database. The step further includes computing and storing the energy consumption prediction for the at least one performance condition in the database based, at least in part, on the set of baseline vessel operating parameters.

[0033] An advantage of some embodiments is that the energy consumption prediction for the at least one performance condition is computed using the baseline vessel operating parameter provided by the manufacturer of the vessel or its components. The baseline vessel operating parameter represents a range of standard operating parameters of the vessel for achieving optimal energy consumption in the vessel for the at least one performance condition. Thus, the deviation can be computed accurately based on the expected baseline vessel operating parameter provided by the manufacturer or in the specification of the vessel.BRIEF DESCRIPTION OF FIGURES

[0034] For a more complete understanding of example embodiments of the present technology, reference is now made to the following descriptions taken in connection with the accompanying drawings in which:

[0035] FIG. 1 is an example representation of a maritime environment, in accordance with various embodiments of the present disclosure;

[0036] FIG. 2 illustrates a simplified block diagram of a server system, in accordance with an embodiment of the present disclosure;

[0037] FIG. 3 illustrates a schematic representation of a process of optimizing the performance of a vessel, in accordance with an embodiment of the present disclosure;

[0038] FIG. 4 illustrates a schematic representation of a process of generating at least one alert for resolving at least one cause of a deviation in the performance of the vessel, in accordance with an embodiment of the present disclosure;

[0039] FIG. 5A illustrates a schematic representation of a Graphical User Interface (GUI) for a vessel operator showing the deviation in the performance of the vessel, in accordance with an embodiment of the present disclosure;

[0040] FIG. 5B illustrates a schematic representation of a GUI for a shore operator depicting alert information of at least one operational cause associated with the vessel, in accordance with an embodiment of the present disclosure;P24-056PCT1

[0041] FIG. 50 illustrates a schematic representation of a GUI for the vessel operator showing at least one operational recommendation associated with the at least one operational cause of the vessel, in accordance with an embodiment of the present disclosure;

[0042] FIG. 6A illustrates a schematic representation of a GUI for the shore operator depicting alert information of at least one technical cause associated with the vessel, in accordance with an embodiment of the present disclosure;

[0043] FIG. 6B illustrates a schematic representation of a GUI for the vessel operator showing at least one technical recommendation associated with the at least one technical cause of the vessel, in accordance with an embodiment of the present disclosure;

[0044] FIG. 6C illustrates a schematic representation of a GUI for the shore operator depicting alert information of at least one technical cause associated with the vessel, in accordance with an embodiment of the present disclosure;

[0045] FIG. 6D illustrates a schematic representation of a GUI for the vessel operator configured to allow the vessel operator to schedule a performance test, in accordance with an embodiment of the present disclosure; and

[0046] FIG. 7 illustrates a flow diagram of a method of optimizing the performance of the vessel, in accordance with an embodiment of the present disclosure.

[0047] The drawings referred to in this description are not to be understood as being drawn to scale except if specifically noted, and such drawings are only exemplary in nature.DETAILED DESCRIPTION

[0048] In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. It will be apparent, however, to one skilled in the art that the present disclosure can be practiced without these specific details. Descriptions of well-known components and processing techniques are omitted to not obscure the embodiments herein unnecessarily. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.

[0049] References in this specification to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. The appearances of the phrase “in an embodiment” in various places in the specification are notP24-056PCT1necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Moreover, various features are described which may be exhibited by some embodiments and not by others. Similarly, various requirements are described which may be requirements for some embodiments but not for other embodiments.

[0050] Moreover, although the following description contains many specifics for the purposes of illustration, anyone skilled in the art will appreciate that many variations and / or alterations to said details are within the scope of the present disclosure. Similarly, although many of the features of the present disclosure are described in terms of each other, or in conjunction with each other, one skilled in the art will appreciate that many of these features can be provided independently of other features. Accordingly, this description of the present disclosure is set forth without any loss of generality to, and without imposing limitations upon, the present disclosure.

[0051] Conditional language such as, among others, “can,” “could,” “might” or “may,” unless specifically stated otherwise, are otherwise understood within the context as used in general to convey that certain embodiments include, while other embodiments do not include, certain features, elements and / or steps. Thus, such conditional language is not generally intended to imply that features, elements, and / or steps are in any way required for one or more embodiments or that one or more embodiments necessarily include logic for deciding, with or without user input or prompting, whether these features, elements and / or steps are included or are to be performed in any particular embodiment.

[0052] Disjunctive languages such as the phrase “at least one of X, Y, or Z” unless specifically stated otherwise, is otherwise understood with the context as used in general to present that an item, term, etc., may be either X, Y, or Z, or any combination thereof (e.g., X, Y, and / or Z). Thus, such disjunctive language is not generally intended to, and should not, imply that certain embodiments require at least one of X, at least one of Y, or at least one of Z to each be present.

[0053] Unless otherwise explicitly stated, articles such as “a” or “an” should generally be interpreted to include one or more described items. Accordingly, phrases such as “a server system configured to” are intended to include one or more recited server systems / processors. Such one or more recited devices can also be collectively configured to carry out the stated recitations. For example, “a processor configured to carry out recitations A, B, and C” can include a first processor configured to carry out recitation A working in conjunction with a second processor configured to carry out recitations B and C. The same holds true for the use of definite articles used to introduce embodiment recitations. In addition, even if a specificP24-056PCT1number of an introduced embodiment recitation is explicitly recited, those skilled in the art will recognize that such recitation should typically be interpreted to mean at least the recited number (e.g., the bare recitation of “two recitations,” without other modifiers, typically means at least two recitations or two or more recitations).

[0054] It will be understood by those within the art that, in general, terms used herein, are generally intended as “open” terms (e.g., the term “including” or “comprising” should be interpreted as “including / comprising but not limited to,” the term “having” should be interpreted as “having at least,” the term “includes” or “comprises” should be interpreted as “includes / comprises but is not limited to,” etc.).

[0055] FIG. 1 is an example representation of a maritime environment 100, in accordance with various embodiments of the present disclosure. The maritime environment 100 includes a server system 102, a vessel 104, a shore system 106, and one or more data sources 108, each coupled to, and in communication with (and / or with access to) a network 110. The vessel 104 may be, but is not limited to, a maritime vessel, an aircraft, a boat, a ship, a yacht, a commercial cargo ship, and so on. The vessel 104 may be operated and maintained by one or more vessel operators (i.e., crew members) (e.g., a vessel operator 112). The shore system 106 can be, but is not limited to, a control system, and a data center, located at a shore station in the maritime environment 100.

[0056] In a non-limiting implementation, each of the one or more vessel operators (e.g., the vessel operator 112) is associated with a respective electronic device (e.g., an electronic device 114). Similarly, the shore system 106 is operated by one or more shore operators ( / .e., managers) (e.g., a shore operator 116). Each of the one or more shore operators (e.g., the shore operator 116) is associated with a respective electronic device (e.g., an electronic device 118).

[0057] In another non-limiting implementation, the vessel operator 112 and the shore operator 116 may use their corresponding electronic devices ( / .e., the electronic device 114 and the electronic device 118 respectively) to access a mobile application or a website for monitoring and operating the vessel 104, or any third-party application to perform a monitoring of the vessel 104. In various non-limiting examples, the electronic devices ( / .e., the electronic device 114 and the electronic device 118) may refer to any electronic devices, such as but not limited to, Personal Computers (PCs), tablet devices, smart wearable devices, Personal Digital Assistants (PDAs), voice-activated assistants, Virtual Reality (VR) devices, smartphones, laptops, and the like. The electronic devices ( / .e., the electronic device 114 and the electronic device 118) may be installed with the specialized software for allowing communication between the vessel operator 112 and the shore operator 116. For instance,P24-056PCT1the electronic device 114 and the electronic device 118 are capable of operating specialized software and hardware for exchanging information between the vessel operator 112 and the shore operator 116.

[0058] The network 110 may include, without limitation, a Light Fidelity (Li- F i) network, a Local Area Network (LAN), a Wide Area Network (WAN), a Metropolitan Area Network (MAN), a satellite network, the Internet, a fiber optic network, a coaxial cable network, an Infrared (IR) network, a Radio Frequency (RF) network, a virtual network, and / or another suitable public and / or private network capable of supporting communication among two or more of the parts or components illustrated in FIG. 1, or any combination thereof.

[0059] Various entities in the maritime environment 100 may connect to the network 110 in accordance with various wired and wireless communication protocols, such as Transmission Control Protocol / lnternet Protocol (TCP / IP), User Datagram Protocol (UDP), 2nd Generation (2G), 3rd Generation (3G), 4th Generation (4G), 5th Generation (5G) communication protocols, Long Term Evolution (LTE) communication protocols, future communication protocols or any combination thereof. For example, the network 110 may include multiple different networks, such as a private network made accessible by the server system 102 and a public network (e.g., the Internet, etc.) through which the server system 102, the vessel 104, the shore system 106, and the one or more data sources 108 may communicate.

[0060] The vessel 104 may be operating in a sea following a predetermined route. The vessel 104 may be enabled with the Internet of Things (loT). In other words, the vessel 104 may be associated with the one or more data sources 108 that collect, transmit, and analyze data in real time. The integration of loT technology in the vessel 104 allows for seamless communication between various components and provides actionable insights to the vessel operator 112 of the vessel 104.

[0061] Examples of the one or more data sources 108 include, but are not limited to, engine and machinery sensors, navigation systems, environmental sensors / systems, hull monitoring systems, fuel management systems, communication systems, and so on. For example, the engine and machinery sensors may be responsible for recording / monitoring parameters such as engine temperature, fuel consumption, Revolutions Per Minute (RPM), oil pressure, coolant levels, and so on. The Navigation systems may be responsible for recording / monitoring parameters such as Global Positioning System (GPS), Radio Detection And Ranging (RADAR), Sound Navigation and Ranging (SONAR), and Automatic Identification Systems (AIS) to track the vessel’s location, speed, heading, rudder angle, surrounding marine traffic, and so on. The environmental sensors / systems may beP24-056PCT1responsible for recording / monitoring parameters such as external environmental conditions, including sea state (wave height and frequency), wind speed and direction, air and water temperature, humidity, and barometric pressure, among other weather conditions. In some instances, the environmental systems access weather-related information from different meteorological departments or the internet as well. The hull monitoring systems may include strain gauges and accelerometers placed on the hull of the vessel 104 to record / monitor parameters such as stress, strain, and vibrations of the hull. The fuel management systems may be responsible for recording / monitoring parameters such as fuel levels, consumption rates, fuel mix type, fuel quality, and so on. The Satellite and radio communication systems may be responsible for collecting and transmitting data between the vessel 104 and the shore system 106.

[0062] In an embodiment, the one or more data sources 108 are responsible for collecting or recording a plurality of vessel operating parameters of the vessel 104. The one or more data sources 108 may include a combination of sensors, onboard systems, and external data feeds, all integrated to provide comprehensive monitoring and data collection of the various vessel operating parameters.

[0063] In an instance, the one or more data sources 108 are configured to record a plurality of vessel operating parameters at one or more frequencies at predefined intervals. For instance, a set of vessel operating parameters may be recorded at a predefined interval such as every few milliseconds, seconds, minutes, or so on. In another instance, the data recording process for a few vessel operating parameters may take place using mediumfrequency recording ( / .e., every few minutes to hours) or low-frequency recording ( / .e., every few hours or days) as well. Examples of the plurality vessel operating parameters include, but are not limited to, engine power, shaft RPM, engine load, engine room temperature, pressure within engine room, power generated by the waste heat recovery system’s steam and power turbines, inlet and outlet temperatures of the exhaust gas for turbo charger, temperature and pressure in the scavenging air receiver of main engine and / or auxiliary engine, pressure in the exhaust receiver of main engine and / or auxiliary engine, back pressure of exhaust gas in turbo charger, temperature and viscosity of the fuel oil, average temperature of the exhaust gas, maximum continuous rating, maximum shaft RPM, whether the main engine and / or auxiliary engine has a turbocharger cut-out, number of turbochargers, number of cylinders, diameter of each of the cylinders, number of piston strokes, length of the piston stroke, hull capacity for refrigerated cargo and dimensions, fuel consumption, Speed Over Ground (SOG), true heading, rudder angle, data on weather conditions, sea state, data on cargo conditions, ballast tank levels, overall fuel levels, fuel type, fuel mix (of mixed fuel batch), hull integrity, machinery condition, distance traveled, estimated time of arrival, etc., amongP24-056PCT1other suitable vessel operating parameters. Since these vessel operating parameters are recorded at different frequencies (or the same high frequency), these parameters are aggregated at predefined intervals to enable simplified processing. Examples of the predefined intervals include 5 minutes (min.), 10 min., 15 min., and so on.

[0064] As described earlier, the existing vessel optimization techniques fail to provide comprehensive performance improvement recommendations to the vessel operator 112 based on the current performance of the vessel 104. Another problem with the existing vessel optimization techniques is that the communication between the vessel 104 and the shore operator 116 is performed primarily through email and phone calls. Communicating an issue to the shore operator 116 and getting an appropriate reply via an email or a phone call may not be efficient and takes more time to resolve each issue. A delay in communicating the recommendations to the vessel 104 would result in fuel wastage, which in turn impacts the fuel efficiency and operational costs of the vessel 104.

[0065] To overcome this problem, an approach for optimizing the performance of the vessel 104 is required. The approach proposed in the present disclosure optimizes the performance of the vessel 104, at least by monitoring the performance of the machinery, such as the main or auxiliary engine of the vessel 104, and generating at least one to the vessel 104 upon detecting a deviation in the performance of the vessel 104. The approach proposed in the present provides comprehensive performance improvement recommendations to the vessel 104 upon detecting at least one cause of the deviation. The at least one cause of the deviation may include at least one operational cause, at least one technical cause, or a combination thereof. Transmitting the one or more recommendations via the network 110 to the vessel 104 allows the vessel operator 112 of the vessel 104 to resolve the at least one cause of the deviation effectively and within a limited time. To that end, to address the above- mentioned limitation, the present disclosure describes that the server system 102 may optimize performance of the vessel 104.

[0066] In one embodiment, the maritime environment 100 may further include a database 120 coupled with the server system 102. In an example, the server system 102 coupled with the database 120 is embodied within a central server (not shown) associated with the operator of the vessel 104, however, in other examples, the server system 102 can be a standalone component (acting as a hub) connected to the central server. The database 120 may be incorporated in the server system 102 or maybe an individual entity connected to the server system 102 or maybe a database stored in cloud storage. In one embodiment, the database 120 stores the vessel operating parameters recorded by the one or more data sources 108, a set of predefined rules, and other necessary machine instructions required for implementing the various functionalities of the server system 102 such as firmware data,P24-056PCT1operating system, and the like. It is noted that the set of predefined rules has been explained in detail later in the present disclosure. In addition, the database 120 provides a storage location for data and / or metadata obtained from various operations performed by the server system 102.

[0067] In an embodiment, the server system 102 is configured to access the set of vessel operating parameters from the plurality of vessel operating parameters of the vessel 104 at the predefined intervals. Herein, the set of vessel operating parameters is selected from the plurality of vessel operating parameters such that each of these parameters satisfies stability criteria. It is noted that the stability criteria may be predefined by an administrator (not shown) associated with the server system 102. In an instance, the stability criteria include a set of predefined operating conditions or a set of operating conditions for the vessel 104. In other words, the stability criteria define conditions during which the vessel operating parameters are considered stable thus, free of noisy, frozen, and / or invalid values.

[0068] In another embodiment, the server system 102 is configured to predict an energy consumption (also referred to as an energy consumption prediction) for the vessel 104 using a prediction model (not shown). In particular, the server system 102 uses an Artificial Intelligence (Al) or Machine Learning (ML) based model (such as a prediction model) that is configured to determine the energy consumption prediction for at least one performance condition based, at least in part, on the set of vessel operating parameters received at the predefined intervals {e.g., 10 minutes). In an implementation, the prediction model may be configured to predict a Specific Fuel Oil Consumption (SFOC) value for the main or auxiliary engine of the vessel 104. In such an implementation, the SFOC value may correspond to the energy consumption of the vessel 104. It is noted that even though the SFOC value is used to describe the energy consumption of the vessel 104, the same should not be construed as a limitation of the present disclosure. For instance, instead of the SFOC value, the server system 102 may be configured to predict Engine Fuel Efficiency (EFE), Engine Power Output (EPO), Total Fuel Consumption (TFC), Energy Consumption per Nautical Mile (kWh / NM), and so on to ascertain the energy consumption of the vessel 104 as well. A detailed explanation of various operations required for predicting the energy consumption of the vessel 104 by the server system 102 is described later with reference to FIG. 2. In another embodiment, the server system 102 is configured to determine the energy consumption prediction for at least one performance condition based, at least in part, on a set of baseline vessel operating parameters. The at least one performance condition of the vessel 104 includes engine load, fuel consumption, shaft power associated with the vessel 104, and so on. The set of baseline vessel operating parameters indicates a range of parameters provided by the manufacturer of the vessel 104.P24-056PCT1

[0069] In an instance, the set of baseline vessel operating parameters accessed from the database 204 e.g., baseline energy consumption) may be derived at least from the specification of the engine provided by the manufacturer, based on industry standards. The baseline energy consumption serves as a reference point for evaluating the current performance of the engine. The reference energy consumption of the vessel 104 may be set as 5% more than the baseline energy consumption of the vessel 104. For expository purposes, the term ‘vessel’, ‘boat’, ‘ship’, or ‘carrier’ (used interchangeably herein) refers to any type of vehicle or craft that is designed to navigate or operate in a fluid such as but not limited to, water. Examples of vessels include commercial vessels, recreational vessels, special purpose vessels, and so on.

[0070] In yet another embodiment, the server system 102 is configured to determine an actual energy consumption based, at least in part, on the set of vessel operating parameters. The actual energy consumption represents the actual energy consumed by the vessel 104. In an instance, the set of vessel operating parameters includes information related to the determination of the actual energy consumption based on the set of vessel operating parameters received at the predefined intervals {e.g., 10 minutes).

[0071] In yet another embodiment, the server system 102 is further configured to compute a deviation between the actual energy consumption and the energy consumption prediction. Then, the server system 102 is further configured to generate at least one alert indicating the presence of the deviation in the vessel 104, in response to determining that the deviation is equal to or greater than a threshold deviation value. This aspect has been described in detail with reference to FIG. 2

[0072] Although in FIG. 1 , the server system 102 is shown to be incorporated within the maritime environment 100, in some embodiments, the server system 102 may be external to and in communication with the maritime environment 100, for example, via the network 110. In some examples, the server system 102 may be implemented in third-party external servers to perform the various operations described herein.

[0073] The number and arrangement of systems, devices, and / or networks shown in FIG. 1 are provided as an example. There may be additional systems, devices, and / or networks; fewer systems, devices, and / or networks; different systems, devices, and / or networks; and / or differently arranged systems, devices, and / or networks than those shown in FIG. 1. Furthermore, two or more systems or devices shown in FIG. 1 may be implemented within a single system or device, or a single system or device is shown in FIG. 1 may be implemented as multiple, distributed systems or devices. In addition, the server system 102 should be understood to be embodied in at least one computing device in communication withP24-056PCT1the network 110, which may be specifically configured, via executable instructions, to perform steps as described herein, and / or embodied in at least one non-transitory computer-readable media.

[0074] FIG. 2 illustrates a simplified block diagram of a server system 200, in accordance with an embodiment of the present disclosure. It is noted that the server system 200 may be similar to the server system 102 of FIG. 1. In one embodiment, the server system 200 is a part of the internal server operated by an organization employing the operator or onboard personnel (not shown in FIG. 2) of the vessel 104. In some embodiments, the server system 200 is embodied as a cloud-based and / or Software as a Service (SaaS) based architecture.

[0075] The server system 200 includes a computer system 202 and a database 204. It is noted that the database 204 is identical to the database 120 of FIG. 1. The computer system 202 includes at least one processor 206 (herein, referred to interchangeably as ‘processor 206’) for executing instructions, a memory 208, a communication interface 210, a user interface 212 and a storage interface 214 that communicates with each other via a bus 216.

[0076] In some embodiments, the database 204 is integrated into the computer system 202. For example, the computer system 202 may include one or more hard disk drives as the database 204. A storage interface 214 is any component capable of providing the processor 206 with access to the database 204. The storage interface 214 may include, for example, an Advanced Technology Attachment (ATA) adapter, a Serial ATA (SATA) adapter, a Small Computer System Interface (SCSI) adapter, a Redundant Array of Independent Disc (RAID) controller, a Storage Area Network (SAN) adapter, a network adapter, and / or any component providing the processor 206 with access to the database 204. In one non-limiting example, the database 204 is configured to store a vessel performance dataset 218, the set of predefined rules 220, and the like.

[0077] In an example, the vessel performance dataset 218 includes a set of vessel operating parameters associated with the vessel 104. Various examples of vessel operating parameters for the vessel 104 include, but are not limited to, at least one of engine power, shaft RPM, engine load, engine room temperature, pressure within engine room, power generated by the waste heat recovery system's steam and power turbines, inlet and outlet temperatures of the exhaust gas for turbo charger, temperature and pressure in the scavenging air receiver of main engine and / or auxiliary engine, pressure in the exhaust receiver of main engine and / or auxiliary engine, back pressure of exhaust gas in turbo charger, temperature and viscosity of the fuel oil, average temperature of the exhaust gas,P24-056PCT1maximum continuous rating, maximum shaft RPM, whether the main engine and / or auxiliary engine has a turbocharger cut-out, number of turbochargers, number of cylinders, diameter of each of the cylinders, number of piston strokes, length of the piston stroke, hull capacity for refrigerated cargo and dimensions, fuel consumption, Speed over ground (SOG), true heading, rudder angle, data on weather conditions, sea state, data on cargo conditions, ballast tank levels, overall fuel levels, fuel type, fuel mix (of mixed fuel batch), hull integrity, machinery condition, distance traveled, estimated time of arrival, actual energy consumption, etc., among other suitable vessel operating parameters.

[0078] The set of predefined rules 220 may include rules related to at least one of stability criteria, conditions for generating the at least one alert, determining at least one operational cause, determining at least one technical cause, generation the at least one technical recommendation related to the at least one technical cause, generating the at least one recommendation, conditions for regeneration of a performance report associated with the vessel 104, changing status of the at least one alert, and so on. The set of predefined rules 220 is explained in detail later with respect to the different modules of the processor 206 of FIG. 2 in the present disclosure.

[0079] The user interface 212 is an interface such as a Human Machine Interface (HMI) or a software application that allows users such as an administrator (not shown in FIG. 2) to interact with and control the server system 200 or one or more parameters associated with the server system 200. It may be noted that the user interface 212 may be composed of several components that vary based on the complexity and purpose of the application. Examples of components of the user interface 212 may include visual elements, controls, navigation, feedback and alerts, user input and interaction, responsive design, user assistance and help, accessibility features, and the like. More specifically these components may correspond to icons, layout, color schemes, buttons, sliders, dropdown menus, tabs, links, error / success messages, mouse and touch interactions, keyboard shortcuts, tooltips, screen readers, and the like.

[0080] The processor 206 includes suitable logic, circuitry, and / or interfaces to execute operations for determining the deviation in the energy consumption, generating the at least one alert, determining the at least one technical recommendation, determining the at least one operational recommendation, and the like. Examples of the processor 206 include, but are not limited to, an Application-Specific Integrated Circuit (ASIC) processor, a Reduced Instruction Set Computing (RISC) processor, a Graphical Processing Unit (GPU), a Complex Instruction Set Computing (CISC) processor, a Field-Programmable Gate Array (FPGA), and the like.P24-056PCT1

[0081] The memory 208 includes suitable logic, circuitry, and / or interfaces to store a set of computer-readable instructions for performing the various operations described herein. Examples of the memory 208 include a random-access memory (RAM), a read-only memory (ROM), a removable storage drive, a hard disk drive (HDD), and the like. It will be apparent to a person skilled in the art that the scope of the disclosure is not limited to, realizing the memory 208 in the server system 200, as described herein. In another embodiment, the memory 208 may be realized in the form of a database server or a cloud storage working in conjunction with the server system 200, without departing from the scope of the present disclosure.

[0082] The processor 206 is operatively coupled to the communication interface 210, such that the processor 206 is capable of communicating with a remote device ( / .e., to / from a remote device 222) such as third-party servers or with the vessel 104, the one or more data sources 108, or communicating with any entity connected to the network 110 (as shown in FIG. 1).

[0083] It is noted that the server system 200 as illustrated and hereinafter described is merely illustrative of an apparatus that could benefit from embodiments of the present disclosure and, therefore, should not be taken to limit the scope of the present disclosure. It is noted that the server system 200 may include fewer or more components than those depicted in FIG. 2.

[0084] In one implementation, the processor 206 includes a data pre-processing module 224, a data processing module 226, a disparity analysis module 228, a notification module 230, and a recommendation module 232. It should be noted that components, described herein, such as the data pre-processing module 224, the data processing module 226, the disparity analysis module 228, the notification module 230, and the recommendation module 232 can be configured in a variety of ways, including electronic circuitries, digital arithmetic, and logic blocks, and memory systems in combination with software, firmware, and embedded technologies.

[0085] In an embodiment, the data pre-processing module 224 includes suitable logic and / or interfaces for recording the plurality of vessel operating parameters from at least one data source associated with the vessel 104 at one or more frequencies. In particular, the data pre-processing module 224 may utilize the one or more data sources 108 to record or access the vessel operating parameters for the vessel 104. As may be understood, vessel operating parameters are dynamic in nature, therefore they have to be recorded at various frequencies ( / .e., one or more frequencies). For instance, a few vessel operating parameters have to be recorded at a higher frequency such as every few milliseconds, seconds, minutes, or so on,P24-056PCT1while others may be recorded at a medium frequency such as every few minutes, hours, and so on, or lower frequency such as every few hours, days, and so on as well. The decision to record different vessel operating parameters at different frequencies may be made based on the type of each vessel operating parameter. For instance, the shaft RPM may be recorded at a high frequency while the weather-related data may be recorded at a medium frequency.

[0086] Further, the data pre-processing module 224 may be configured to aggregate the plurality of recorded vessel operating parameters at the predefined intervals. This aggregation process generates a set of aggregated vessel operating parameters. As may be appreciated, since the vessel operating parameters are recorded at different frequencies (or the same high frequency), there exists a huge amount of values or data that needs to be processed by the server system 200 to obtain an understanding of these parameters. Therefore, by aggregating these parameters over predefined intervals such as 5 min., 10 min., 15 min., and so on, the complexity of understanding these parameters is significantly reduced. Further, few computational resources may be required for analyzing this aggregated data. In an instance, the duration of the predefined interval can be defined by the administrator of the server system 200 or the operator of the vessel 104.

[0087] In another embodiment, the data pre-processing module 224 includes suitable logic and / or interfaces for extracting a set of vessel operating parameters from the plurality of aggregated vessel operating parameters based, at least in part, on the stability criteria associated with the vessel 104. Herein, the stability criteria may define a set of operating conditions or predetermined operating conditions for the vessel 104. In an instance, the stability criteria can be defined by the administrator of the server system 200 or the operator of the vessel 104 as the set of predefined rules 220. The set of vessel operating parameters is selected to ensure the engine consumption (determined later) is determined during performance conditions for the vessel 104. It is noted that due to the unpredictable nature of the unstable operating conditions, the energy consumption in such conditions becomes complex to predict. Therefore, to improve the determination of the energy consumption prediction only the set of vessel operating parameters that are stable are used. Since, these vessel operating parameters are free of disturbance due to temporary transients, weather conditions, etc., among other temporary unstable conditions, they don’t affect the determination of the expected energy prediction.

[0088] In particular, for extracting the set of vessel operating parameters ( / .e., stable vessel operating parameters), the data pre-processing module 224 is configured to identify one or more invalid vessel operating parameters from the plurality of vessel operating parameters based, at least in part, on first filtering criteria within the stability criteria. Herein, the invalid vessel operating parameters indicate that if the one or more stability conditions inP24-056PCT1the first filtering criteria are not met, then the payload corresponding with the vessel operating parameters would be invalid. The first filtering criteria may be defined based on at least one of a stable engine power range, a stable shaft RPM range, a stable engine load range, a stable fuel consumption range, or an SOG range. The ‘stable engine power range’ may define an acceptable range of Maximum Continuous Rating (MCR) outside which any value is invalid. The ‘stable shaft RPM range’ may define a stable RPM range outside which any value is invalid. The ‘stable engine load range’ may define a stable range of engine load (for either the main or auxiliary engine) outside which any value is invalid. The ‘stable fuel consumption range’ may define a stable range of fuel consumption outside which any value is invalid. In an instance, the fuel consumption may be in Metric Tons per hour (MT / hr) or Kilowatt per hour (kWh). The SOG range may define a stable range SOG GPS outside of which any value is invalid. In an instance, the SOG is measured in Knots.

[0089] Then, the data pre-processing module 224 may be configured to identify one or more frozen vessel operating parameters from the plurality of vessel operating parameters based, at least in part, on the second filtering criteria within the stability criteria. Herein, the frozen vessel operating parameters indicate that if the one or more stability conditions in the second filtering criteria are not met, then the sensors associated with the one or more data sources 108 may be frozen.

[0090] The second filtering criteria may be defined based on at least one of a stagnant engine power factor, a stagnant shaft RPM factor, or a stagnant fuel consumption factor. The stagnant engine power factor indicates that if the absolute difference between payloads is zero, then the sensor is frozen and the corresponding value should not be considered. Similarly, the stagnant shaft RPM factor and the stagnant fuel consumption factor respectively indicate that if the absolute difference between payloads is zero, then the sensor is frozen and the corresponding value should not be considered as well.

[0091] Further, the data pre-processing module 224 may eliminate the one or more invalid vessel operating parameters and the one or more frozen vessel operating parameters from the plurality of vessel operating parameters to determine a set of remaining vessel operating parameters. Thereafter, the data pre-processing module 224 is configured to filter the set of vessel operating parameters from the set of remaining vessel operating parameters based, at least in part, on the third filtering criteria within the stability criteria. Herein, the vessel operating parameters indicate that if the one or more stability conditions in the third filtering criteria are met over a specified time interval (such as 30 min., or so on), then the vessel operating parameters may be called stable.

[0092] The third filtering criteria may be defined based on at least one of a stableP24-056PCT1engine load range, at least one stable shaft RPM operating range, a stable true heading range, a stable rudder angle range, or a stable SOG operating range. The stable engine load range indicates the difference in the payload for each predefined interval within the specified time interval should be lower than a predefined percentage (such as 5%, 10%, etc.). In an instance, the stable shaft RPM operating range may indicate that the shaft RPM should be greater than a predefined RPM over a rolling window. In another instance, the stable shaft RPM operating range may indicate that the difference between the shaft RPM for each predefined interval should be within the specified time interval and should be lower than another predefined RPM. The stable true heading range may indicate that the difference between the true heading of each predefined interval should be less than a predefined heading angle. The stable rudder angle range may indicate that the difference between the rudder angles of each predefined interval should be less than a predefined rudder angle. The stable SOG operating range may indicate that the SOG should fall within a specific range (e.g., greater than X knots but less than Y knots, herein X and Y are non-zero natural numbers such that X<Y).

[0093] It should be noted that the first filtering criteria, the second filtering criteria, and the third filtering criteria are predefined in the database 204 as the set of predefined rules 220. The data pre-processing module 224 may use the set of predefined rules 220 for determining the set of vessel operating parameters from a plurality of vessel operating parameters.

[0094] In another embodiment, the data pre-processing module 224 is configured to determine the set of vessel operating parameters from the plurality of vessel operating parameters. Herein, each vessel operating parameter in the plurality of vessel operating parameters satisfies a performance threshold. In an implementation, the performance threshold is defined by the administrator of the server system 200 or the operator of the vessel 104. For instance, a performance threshold of 10% selects the top 10% of vessel operating parameters to form the set of vessel operating parameters. In particular, the performance threshold indicates a selection quantile that has to be accessed or extracted from the set of vessel operating parameters. The selection quantile may be predefined in the database 204 by the administrator. Then, the set of vessel operating parameters present within the selection quantile is identified and extracted from the plurality of vessel operating parameters.

[0095] In another embodiment, the data pre-processing module 224 may be configured to access a set of baseline vessel operating parameters from the database 204. The baseline vessel operating parameters may be stored in the database 204 under the vessel performance dataset 218. The baseline vessel operating parameter represents a range of standard operating parameters of the vessel 104 for achieving optimal energy consumption in the vessel 104 for the at least one performance condition.P24-056PCT1

[0096] In one embodiment, the data processing module 226 includes suitable logic and / or interfaces for computing the actual energy consumption for the at least one performance condition based, at least in part, on the set of vessel operating parameters. The actual energy consumption represents the actual energy consumed by the vessel 104 for the at least one performance condition.

[0097] In an embodiment, the data processing module 226 is further configured to generate a set of features based, at least in part, on the set of vessel operating parameters. The data processing module 226 is further configured to generate and store, by a prediction model (not shown), the energy consumption prediction for the at least one performance condition in the database 204 based, at least in part, on applying the set of features on the prediction model. In various instances, the energy consumption for the at least one performance condition {e.g., a specified engine load) of the vessel 104 may be predicted in terms of SFOC, EFE, EPO, TFC, kWh / NM, and so on may be predicted to ascertain the energy.

[0098] It is noted that the prediction model is trained before its operation during deployment based on the historical vessel performance dataset. In an instance, the prediction model is trained on the historical vessel performance dataset to learn patterns, relationships, and trends in the input data during deployment. In various examples, the predicted energy consumption may be at least one of an SFOC value, EFE value, EPO value, TFC value, energy consumption per nautical mile (kWh / NM) value, and / or so on.

[0099] In one specific implementation, the Al or ML based model may be used as the prediction model. In a nomliming implementation, the prediction model may be a Light Gradient Boosting Machine (LightGBM) with quantile regression with a quantile parameter set to the performance threshold. In other instances, various other types of models used are Random Forest (RF), Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), Bootstrap Aggregating (Bagging), Gradient Boosting Machine (GBM), Voting Classifier, Stacked Generalization (Stacking), Multiple Additive Regression Trees (MART), Gradient Boosted Regression Trees (GBRT), and so on.

[0100] An advantage of some embodiments is that using the performance threshold allows the prediction model to predict the energy consumption for the top or best quantile. It is noted that the performance threshold may be predefined by an administrator (not shown). In other words, the performance threshold is configurable by an administrator based, at least in part, on different requirements.

[0101] In an embodiment, the data processing module 226 is further configured to compute and store the energy consumption prediction for the at least one performanceP24-056PCT1condition in the database 204 based, at least in part, on the set of baseline vessel operating parameters. In some embodiments, the energy consumption prediction for the at least one performance condition is computed using the baseline vessel operating parameter provided by the manufacturer of the vessel 104 or its components.

[0102] In an embodiment, the disparity analysis module 228 includes suitable logic and / or interfaces for computing a deviation between the actual energy consumption and the energy consumption prediction. When the deviation is equal to or greater than the threshold deviation value for a predefined time period {e.g., 1 hour). In a non-limiting example, the predefined time period {e.g., 1 hour). is greater than the predefined intervals e.g., each of 10 mint). In an instance, the vessel operating parameters are aggregated over the predefined interval {e.g., 10 min), and the deviation in the energy consumption is computed for the predefined time period {e.g., 1 hour). In such instances, the disparity analysis module 228 is configured to compute the deviation between the actual energy consumption and the energy consumption prediction for every 1 hour time {i.e., 12 predefined intervals each of 5 minutes), irrespective of the vessel operating parameters accessed by the data pre-processing module 224 from the vessel 104. It should be noted that the disparity analysis module 228 may use the set of predefined rules 220 for determining the deviation in the energy consumption may be stored in the set of predefined rules 220 in the database 204.

[0103] In one embodiment, the disparity analysis module 228 is configured to determine at least one cause of the deviation based, at least in part, on the set of vessel operating parameters. In another embodiment, the disparity analysis module 228 is configured to identify at least one operational cause, at least one technical cause or a combination thereof based, at least in part, on the set of vessel operating parameters. In another embodiment, the identification of the at least one operational cause, the at least one technical cause or the combination thereof can be performed either by the Al or ML based model or using the set of predefined rules 220. Identifying whether the cause is the at least one operational cause, the at least one technical cause, or a combination thereof, allows the server system 200 to provide appropriate recommendations to the vessel operator 112.

[0104] In some instances, the at least one operational cause includes but is not limited to, engagement or non-engagement of a sufficient number of turbochargers of the vessel 104 based on the at least one performance condition of the vessel 104. The set of predefined rules 220 may include rules related to engagement or non-engagement of the turbochargers based, at least in part, on the at least one performance conditions. In an instance, the at least one performance condition includes an operating load of the vessel 104. In another embodiment, the disparity analysis module 228 is configured to verify whether the Turbo Charger Cut-Out (TCCO) configuration is appropriate according to the optimal configuration set along in the setP24-056PCT1of predefined rules 220. The optimal configuration set in the set of predefined rules 220 includes reference information indicating the number of turbochargers that need to be engaged depending on the at least one performance condition {e.g., the specified engine load) of the vessel 104.

[0105] In another embodiment, the disparity analysis module 228 is configured to utilize the set of predefined rules 220 for determining the at least one operational cause. The at least one operational cause is determined by verifying whether the turbochargers of the vessel 104 are engaged based on the current operating load of the vessel 104. In an instance, the set of predefined rules 220 includes that when the current operating load of the vessel 104 is less than 35%, there exists no need for engaging the turbochargers associated with the engine. In such instances, at least one operational recommendation is not sent to the vessel operator 112. In another instance, when the engine operating load is equal to or greater than 35%%, there exists a need for engaging the turbochargers associated with the engine for better performance. In such instances, the at least one operational recommendation is sent to the vessel operator 112. Engaging the turbocharger depending on the operating load of the vessel 104 increases the fuel efficiency in case of deviation in the energy consumption. Thus, the disparity analysis module 228 is configured to determine whether the turbochargers of the vessel 104 need to be engaged based on the current operating load of the vessel 104.

[0106] In another embodiment, in response to determining that the at least one cause is the operational cause, the disparity analysis module 228 is configured to identify a subset of non-standard vessel parameters from the set of vessel operating parameters based, at least in part, on a set of operational performance thresholds. The disparity analysis module 228 is further configured to determine a set of corrective actions to be performed to address the operational cause based, at least in part, on the subset of non-standard vessel parameters and the set of predefined rules. The subset of non-standard parameters represents the vessel parameters that lead to faulty equipment or parts within the vessel 104.

[0107] In another embodiment, the disparity analysis module 228 is configured to utilize the set of predefined rules 220 for determining the at least one technical cause, by verifying issues related to the calibration of one or more meters associated with an engine of the vessel 104 within a time period, and one or more warnings in the latest performance report associated with the vessel 104. In an instance, the calibration of the one or more meters includes the calibration of a shaft power meter associated with the engine of the vessel 104, and the calibration of a mass flow meter associated with the engine of the vessel 104.

[0108] In another embodiment, in response to determining that the at least one cause is a technical cause, the disparity analysis module 228 is configured to identify a subset ofP24-056PCT1non-standard vessel parameters from the set of vessel operating parameters based, at least in part, on the vessel performance report and a set of technical performance thresholds. The step further includes determining a set of corrective actions to be performed to address the technical cause based, at least in part, on the subset of non-standard vessel parameters and the set of predefined rules.

[0109] In response to determining that the at least one cause is a technical cause, the disparity analysis module 228 is configured to identify the subset of non-standard vessel parameters from the set of vessel operating parameters based, at least in part, on the vessel performance report and a set of technical performance thresholds. The disparity analysis module 228 is further configured to utilize the set of predefined rules 220 for retrieving a set of parameters from the set of vessel operating parameters that deviates from normal operating parameters. Such a set of parameters is referred to as the non-standard vessel parameters. In an embodiment, the set of predefined rules 220 technical performance thresholds indicating the normal operating values for the vessel parameters, that provide optimal performance of the vessel 104. The disparity analysis module 228 is further configured to utilize the set of predefined rules 220 for identifying the subset of the non-standard vessel parameters based on the vessel performance report and the technical performance thresholds. The set of operational performance thresholds is predefined by the administrator (not shown) associated with the server system 200 and is stored in the database 204. The set of technical performance thresholds represents a set of optimal performance values of the vessel 104 in relation to the performance of the vessel 104. In an instance, pressure in the scavenging air receiver of the main engine and / or auxiliary engine of more than 3.5 bar is considered one of the technical performance thresholds.

[0110] In an instance, the one or more warnings in the latest performance report associated with the vessel 104 include but are not limited to, low mechanical efficiency of the engine, high SFOC, a variation (high or low value) in compression pressure (PComP) of the engine, a variation (high or low value) in maximum combustion pressure (Pmax) of the engine, a raise ( / .e., Pcomp-Pmax) in pressure in the engine, a variation (high or low value) in scavenging air pressure, a variation (high or low value) in temperature before the turbocharger, a drop in pressure of an air filter, a drop in pressure of a cooler filter, a variation in RPM of the turbocharger, a variation in the total efficiency of the vessel 104, a variation in compressor efficiency of the vessel 104, a variation in turbine efficiency of the vessel 104, and so on.

[0111] In an embodiment, the recommendation module 232 includes suitable logic and / or interfaces for determining the at least one operational recommendation for resolving the operational cause based, at least in part, on the set of corrective actions associated with the technical cause. In some instances, the at least one operational recommendation mayP24-056PCT1indicate various actions that the operator or onboard personnel (i.e., the vessel operator 112) may perform to improve the energy consumption of the vessel 104. In another example, the operational recommendation can instruct the vessel operator 112 to run or shut down engine components at different time instances during the route to reduce the SFOC of the vessel 104. The at least one operational recommendation includes recommendations related to engaging the turbocharger when the current vessel load is greater than or equal to 35% vessel load.

[0112] In another embodiment, the recommendation module 232 is configured to determine the at least one technical recommendation for resolving the technical cause based, at least in part, on the set of corrective actions associated with the technical cause. In some instances, the at least one technical recommendation may indicate various actions that the operator or onboard personnel (i.e., the vessel operator 112) may perform to improve the energy consumption of the vessel 104. In another example, the technical recommendation can instruct the onboard personnel (i.e., the vessel operator 112) to conduct maintenance of one or more parts of the vessel 104 to reduce the SFOC of the vessel 104. In yet another embodiment, the recommendation module 232 is configured to generate the at least one technical recommendation related to the one or more warnings in the latest performance report. The at least one technical recommendation is determined based, at least in part, on the set of corrective actions to resolve the technical cause related to the one or more warnings in the latest performance report. This improves the performance of the vessel 104, and reduces the fuel consumption and the deviation in the performance of the vessel 104. The at least one recommendation may include advising or instructing the vessel operator 112 to perform corrective actions provided in a vessel manual for rectifying the warnings and calibration of the one or more meters in the vessel 104.

[0113] In one embodiment, the notification module 230 includes suitable logic and / or interfaces to generate the at least one alert, when the deviation is equal to or greater than the threshold deviation value. In a specific embodiment, the at least one alert is transmitted by the server system 200 to the vessel 104 and / or the shore system 106. In one embodiment, the electronic device 114 associated with the vessel 104 is configured to receive the at least one alert from the server system 200. Similarly, the electronic device 114 associated with the vessel 104 is configured to receive the at least one alert from the server system 200. The at least one alert may be given by the electronic device (i.e., the electronic device 114 and the electronic device 118) in the form of visual (e.g., written text, graphs, and so on), audio (buzzers, announcements, and so on) or audio-visual (i.e., a combination of audio and visual alert). In an embodiment, a GUI can be generated to render the at least one alert in the form of visual, audio, or audio-visual alert.P24-056PCT1

[0114] In an instance, if the deviation is equal to or greater than the threshold deviation value for the predefined time period (i.e., 1 hour), the notification module 230 is configured to generate and transmit the at least one alert indicating the presence of the deviation to the vessel 104. In another instance, if the deviation is greater than the threshold deviation value for at least one predefined interval (i.e., 5 min) within the predefined time period (i.e., 1 hour), the notification module 230 is configured to not generate and transmit the at least one alert to the vessel 104.

[0115] In an embodiment, the notification module 230 includes suitable logic and / or interfaces to set the status of the at least one alert based, at least in part, on the deviation in the performance of the vessel 104, and the at least one cause of deviation. More specifically, the disparity analysis module 228 utilizes the notification module 230 to set and update the status of the at least one alert. The notification module 230 may be configured to set at least one status namely, active, await ( / .e., hold status), and resolved status. In an instance, upon detecting the deviation of the performance of the vessel 104, the notification module 230 may be configured to set the status of the at least one alert as active. The active status of the at least one alert indicates that the at least one alert is generated and transmitted to the vessel 104 indicating the deviation in the performance of the vessel 104. In another embodiment, upon detecting by the disparity analysis module 228, the at least one operational cause of the vessel 104, the status of the at least one alert remains active. In another embodiment, when the vessel operator 112 has scheduled the performance of the set of corrective actions suggested in the at least one operational recommendation due to the at least one operational cause, the notification module 230 may be configured to set the status of the at least one alert as awaiting. In another embodiment, the notification module 230 may be configured to set the status of the at least one alert as awaiting based, at least in part, on the status changed or set by the shore operator 116. The awaiting status indicates that the at least one operational recommendation to resolve the at least one operational cause is scheduled and the shore operator 116 is waiting for further action by the vessel operator 112 on the scheduled date.

[0116] In another embodiment, upon detecting by the disparity analysis module 228, the at least one technical cause of the vessel 104, the status of the at least one alert remains active. In another embodiment, when the vessel operator 112 has scheduled the performance of the set of corrective actions suggested in the at least one technical recommendation due to the at least one technical cause, the notification module 230 may be configured to set the status of the at least one alert as awaiting. In another embodiment, the notification module 230 may be configured to set the status of the at least one alert as awaiting based, at least in part, on the status changed or set by the shore operator 116. The awaiting status indicates that the at least one technical recommendation to resolve the at least one technical cause isP24-056PCT1scheduled and the shore operator 116 is waiting for further action by the vessel operator 112 on the scheduled date. In some embodiments, upon performing the schedule of the set of corrective actions related to at least one operational recommendation and the at least one technical recommendation, the deviation of the performance of the vessel 104 may still exist. In such situations, the notification module 230 may be configured to set the status of the at least one alert is again set as active.

[0117] In some embodiments, the notification module 230 is configured to receive at least one feedback information from the vessel operator 112 and / or the shore operator 116. The feedback information includes one or more actions performed by the vessel operator 112 and / or the shore operator 116 in their respective GUIs, corresponding to the at least one operational recommendation and the at least one technical recommendation. For example, one or more actions include, after engaging the turbocharger, the vessel operator 112 may change the status of engaging the turbocharger from a “non-engaged” state to an “engaged” state. Such information is considered as feedback information. The notification module 230 is configured to change the status of the at least one alert based, at least in part, on the feedback information.

[0118] In another embodiment, the notification module 230 is configured to change the status of the at least one alert. The data pre-processing module 224 is configured to access an updated set of vessel operating parameters recorded at predefined intervals for a new cycle from the vessel 104. The data pre-processing module 224 is configured to access an updated energy consumption prediction for the at least one performance condition for the new cycle from the database 204. Then, the data processing module 226 is configured to determine an updated actual energy consumption for the at least one performance condition for the new cycle based, at least in part, on the set of vessel operating parameters. The disparity analysis module 228 is configured to compute an updated deviation between the updated actual energy consumption and the updated energy consumption prediction. For instance, in response to determining that the updated deviation is lower than the threshold deviation value, the notification module 230 is configured to set a resolved status to the at least one updated alert. In response to determining that the updated deviation is equal to or greater than the threshold deviation value, the notification module 230 is configured to generate at least one updated recommendation.

[0119] It should be noted that the disparity analysis module 228 is configured to schedule the date and time to perform the set of corrective actions suggested in the at least one operational recommendation and / or the at least one technical recommendation based on the input received from the vessel operator 112 using the notification module 230. In an instance, the at least one operational recommendation and / or the at least one technicalP24-056PCT1recommendation are displayed on the GUI on the electronic device 114 associated with the vessel operator 112. When the vessel operator 112 is engaged in other activities, for example, preparing the vessel 104 towards a port, all the crew members of the vessel 104 will be currently engaged in one or more vessel-related activities. In such instances, the set of corrective actions suggested in the at least one operational recommendation and / or the at least one technical recommendation may not be performed by the vessel operator 112 at the time of receiving the recommendation. In such a situation, the vessel operator 112 may schedule a date and time to perform the set of corrective actions.

[0120] In an instance, the notification module 230 is configured to receive the at least one input from the vessel operator 112 regarding when the set of corrective actions suggested in the at least one operational recommendation and / or the at least one technical recommendation can be performed. In an instance, the at least one input includes the date and time at which the vessel operator 112 may perform the set of corrective actions. In an embodiment, the GUI may display at least one calendar along with a time selection option, for scheduling when the set of corrective actions suggested in the at least one operational recommendation and / or the at least one technical recommendation can be performed. To that end, after performing the set of corrective actions, if the deviation of the vessel performance still exists further investigation of the deviation is performed by the disparity analysis module 228.

[0121] In another embodiment, the disparity analysis module 228 is configured to generate the performance report of the vessel 104 based on the set of tests performed on the vessel 104. The latest and the older performance reports associated with the vessel 104 is stored in the database 204. The disparity analysis module 228 may access the latest performance report from the database 204 and investigate the cause of the deviation of the performance of the vessel 104 after performing the set of corrective actions suggested in the at least one recommendation for resolving the at least one operational cause and / or the at least one technical cause. In one embodiment, the disparity analysis module 228 is configured to investigate the latest performance report, based on the one or more warnings and other variations in the latest performance report. The disparity analysis module 228 may be configured to generate the at least one technical recommendation based on the latest performance report. The disparity analysis module 228 may update the status of the at least one alert as resolved if the deviation of the performance of the vessel 104 is within the threshold deviation value.

[0122] In one embodiment, the notification module 230 is configured to facilitate communication {e.g., messages, chats, emails) between the electronic device 114 of the vessel operator 112 and the electronic device 118 of the shore operator 116. In anP24-056PCT1embodiment, the communication is facilitated using a real-time two-way messaging service. In an instance, the messaging service includes a chat service within the GUI of the respective electronic devices (i.e., the electronic devices 114, 118) of the vessel operator 112 and the shore operator 116. The real-time messaging between the shore operator 116 and the vessel operator 112 provides an effective way of resolving the deviation of the performance of the vessel 104 in real-time.

[0123] In one embodiment, the notification module 230 is configured to generate various GUIs for facilitating the various modules of the server system 200 to perform the various operations described herein. For instance, the notification module 230 may generate a GUI and facilitate a visualization of the comparison between the actual energy consumption with the predicted energy consumption on an electronic device of the vessel operator 112.

[0124] FIG. 3 illustrates a schematic representation of a process 300 for optimizing performance of a vessel 302, in accordance with an embodiment of the present disclosure. It is noted that vessel 302 and one or more data sources 304 of FIG. 3 is identical to the vessel 104 and the one or more data sources 108 of FIG. 1 , respectively. Similarly, the set of predefined rules 306 of FIG. 3 is identical to the set of predefined rules 220 of FIG. 2. It is noted that various aspects of FIG. 3 have already been explained earlier with reference to FIG.2 therefore the same is not explained again for the sake of brevity.

[0125] The data pre-processing module 224 of the server system 200 is configured to operate at least one data source of one or more data sources 304 to record the vessel operating parameters in real time. Then, the data processing module 226 of the server system 200 is configured to extract the set of the vessel operating parameters that are stable from the vessel operating parameters based, at least in part, on the performance threshold.

[0126] Thereafter, the data processing module 226 of the server system 200 is configured to utilize the prediction model or the set of baseline vessel operating parameters, to determine the energy consumption prediction for the at least one operating condition based, at least in part, on the set operating vessel parameters. In various examples, the energy consumption prediction may be at least one of an SFOC value, an EFE value, an EPO value, a TFC value, a kWh / NM value, and / or so on The data processing module 226 of the server system 200 is further configured to determine an actual energy consumption of the vessel 302 for the at least one performance condition based, at least in part, on the set of vessel operating parameters.

[0127] Upon determination of the energy consumption prediction, the notification module 230 of the server system 200 may be configured to perform one or more additional operations for providing crucial information to the operator of the vessel 302. In one example,P24-056PCT1the notification module 230 is configured to utilize a GUI 308 for facilitating a visualization of the comparison between the actual energy consumption with the predicted energy consumption for predefined intervals on the electronic device 114 associated with the vessel operator 112. The GUI 308 may facilitate the vessel operator 112 in interacting with the various parameters and results described herein. In some instances, the notification module 230 may utilize external services called via an Application Programming Interface (API) for facilitating the generation of the said visualization.

[0128] The disparity analysis module 228 of the server system 200 is configured to determine the deviation in the performance of the vessel 302, by comparing the expected energy consumption and the actual energy consumption. It should be noted that the deviation is determined for the predefined time period. If the deviation exceeds the threshold deviation value, the disparity analysis module 228 of the server system 200 is configured to generate at least one alert (see, alert 314) (hereinafter referred to as the at least one alert 314) to the vessel 302. The disparity analysis module 228 is configured to determine the operational cause and the technical cause associated with the deviation in the performance of the vessel 302. The disparity analysis module 228 is further configured to determine the at least one technical cause associated with the deviation in performance of the vessel 302. The recommendation module 232 is configured to generate the at least one recommendation. The at least one recommendation includes at least one operational recommendation 310 based on the at least one operational cause and the at least one technical recommendation 312 based on the at least one technical cause. The vessel operator 112 may perform the set of corrective actions to improve the performance of the vessel 302. This may improve the energy performance of the vessel 302. It should be noted that a shore system 316 also includes a GUI 318 configured to set the status of the at least one alert 314 based on the set of corrective actions performed by the vessel operator 112.

[0129] In an instance, the notification module 230 is configured for generating natural language messages indicating the set of corrective activities that the vessel operator 112 may perform to improve the engine performance, based at least in part, on the at least one operational recommendation and the at least one technical recommendation. In an instance, the server system 200 may be coupled with a Large Language Model (LLM) for generating natural language messages indicating one or more actions that the operator may perform to improve the engine performance. In one instance, the LLM may be a transformer-based model. In an instance, the LLM may be trained on open source datasets including EnglishCommonCrawl®, C4®, Github®, Wikipedia®, Gutenburg®, ArXiv®, Stack Exchange® and the like. As may be appreciated, the LLM may be fine-tuned on a datasheet describing the impact of changes in different vessel operating parameters on the overall performance ofP24-056PCT1the vessel 302. Once the LLM is fine-tuned, the said LLM may become capable of harnessing its understanding to determine the one or more activities that the operator may perform to improve the engine performance. It is also noted that the shore system 316 of FIG. 3 is identical to the shore system 106 of FIG. 1.

[0130] FIG. 4 illustrates a schematic representation of a process 400 of generating at least one alert 314 for resolving at least one cause of the deviation in the performance of the vessel 104, in accordance with an embodiment of the present disclosure. The notification module 230 along with various other modules in the processor 206, is configured to perform the process 400 depicted in FIG. 4 for generating and managing the at least one alert 314. The notification module 230 receives information including but not limited to, voyage information 402, performance information 404, and operational information 406 from various other modules in the processor 206 of the server system 200. In an embodiment, the disparity analysis module 228 is configured to determine the voyage information 402 based on a planned route of the vessel 104. The voyage information 402 is then stored in the database 204.

[0131] As depicted in FIG. 4, the notification module 230 is configured to access the voyage information 402 from the database 204. The voyage information 402 includes but is not limited to, voyage plans 408 of the vessel 104 and their corresponding schedule information 410. The notification module 230 is further configured to generate the least one alert 314 indicating a requirement for optimization of the performance of the machinery to the vessel operator 112 based on the voyage plans 408 and the schedule information 410. It should be noted that the disparity analysis module 228 of FIG. 2 is configured to generate the voyage plans 408 of the vessel 104 and their corresponding schedule information 410 to reach one or more destinations, based on the current location information and the set of vessel operating parameters of the vessel 104. In case the level of the fuel in vessel 104 is low, the disparity analysis module 228 is configured to generate an optimal voyage plan and store the optimal plan in the database 204.

[0132] The notification module 230 is further configured to generate at least one alert 314, in response to accessing the performance information 404 from the database 204. The performance information 404 includes but is not limited to, the actual energy consumption 412, the energy consumption prediction 414, and the deviation 416. The data processing module 226 is configured to determine the actual energy consumption 412 of the vessel 104, based, at least in part, on the set of vessel operating parameters. The data processing module 226 is also configured to access the energy consumption prediction 414 of the vessel 104 from the database 204. In real-time, the disparity analysis module 228 is configured to compute the deviation 416 between the actual energy consumption 412 and the energyP24-056PCT1consumption prediction 414. In an embodiment, in real-time, the actual energy consumption 412, the energy consumption prediction 414, and deviation 416 are stored in the database 204. The disparity analysis module 228 may access the performance information 404 from the database 204.

[0133] The notification module 230 is further configured to generate the at least one alert 314, in response to accessing the operational information 406 from the database 204. The operational information 406 includes but is not limited to, the at least one technical cause 418 and the at least one operational cause 420 associated with the vessel 104. In particular, the disparity analysis module 228 is configured to verify the at least one operational cause 420 ( / .e., turbocharger engagement and non-engagement based on the at least one performance condition) associated with the vessel 104. The disparity analysis module 228 is also configured to identify the at least one technical cause 418 ( / .e., one or more warnings in the latest performance report and calibrations of one or more meters in the vessel 104) associated with the vessel 104. The recommendation module 232 is configured to generate the at least one recommendation ( / .e., the at least one operational recommendation and the at least one technical recommendation) based on the at least one technical cause 418 and the at least one operational cause 420.

[0134] The notification module 230 is further configured to set a status to the at least one alert 314. For example, upon detecting the deviation 416 by the disparity analysis module 228, the notification module 230 generates the at least one alert 314 and set the status of the at least one alert 314 as active. The at least one recommendation ( / .e., the at least one operational recommendation and the at least one technical recommendation) is transmitted to the vessel operator 112 for resolving the at least one technical cause 418 and the at least one operational cause 420 associated with the vessel 104. Based on the set of corrective actions performed by the vessel operator 112 for the at least one operational recommendation and the at least one technical recommendation), the status of the at least one alert 314 is updated as active or hold. Upon resolving the at least one technical cause 418 and the at least one operational cause 420 associated with the vessel 104, the status of the at least one alert is set as closed or resolved state.

[0135] The GUI 308 of the vessel operator 112 is configured to facilitate the rendering of the deviation 416 in the energy consumption from the disparity analysis module 228, in the form of a text message, a sound alert, a graphical representation, and so on. The notification module 230 is configured to change the status of the at least one alert 314 based on the at least one feedback information received from the vessel operator 112 and / or the shore operator 116. In one embodiment, the notification module 230 is configured to facilitate the rendering of the set of corrective actions to be performed by the vessel operator 112. InP24-056PCT1another embodiment, the notification module 230 is configured to facilitate the rendering of the set of corrective actions to be performed by the shore operator 116. The at least one feedback information is received from the vessel operator 112 and / or the shore operator 116, based, at least in part, on the set of corrective actions performed by the vessel operator 112.

[0136] It should be noted that the at least one alert 314 is transmitted to the vessel operator 112 through a vessel Application Programming Protocol (API) 422 and / or to the shore operator 116 through a shore API 424. The vessel API 422 and the shore API 424 act as an interface for vessel 104 and the shore system 106 to communicate with each other. In an instance, the shore operator 116 may dismiss the at least one alert 314, if the at least one alert 314 is no longer required to be monitored. In another instance, the shore operator 116 may re-activate the at least one alert 314, that is, set on hold, if the holding time for the state of the at least one alert 314 has lapsed and the status of the at least one alert needs to be activated. For example, in case the performance test of the vessel 104 is scheduled on a particular date and time by the vessel operator 112, the status of the at least one alert 314 may be set as on hold by the shore operator 116. Upon performing the performance test by the vessel operator 112 on the particular date and time, the status of the at least one alert 314 is updated as active or closed. The status of the at least one alert 314 is updated as active by the shore operator 116 when there exists the deviation 416 in the performance of the vessel 104. The status of the at least one alert 314 is updated as closed by the shore operator 116 when there exists no deviation 416 in the performance of the vessel 104.

[0137] FIG. 5A illustrates a schematic representation of a GUI 500 for the vessel operator 112 showing the deviation information in the performance of the vessel 104, in accordance with an embodiment of the present disclosure. The GUI 500 renders an alert message 502, a RPM meter 504 shows the RPM ( / .e., 47.3 RPM) of the engine, a power meter 506 shows the current power ( / .e., 11 MW) of the engine, and an energy distribution graph 508 showing the deviation 510 of the energy consumption.

[0138] Herein, the alert message 502 includes an alert text indicating a date and time of deviation of the energy consumption, an amount of deviation of the energy consumption, the current load of the vessel 104, and the excess fuel consumption or fuel wastage due to the deviation 510. It should be noted that the disparity analysis module 228 of the server system 200 may be configured to determine the fuel wastage by the vessel 104 during its journey through the planned route. Then, the fuel wastage information is stored in the database 204.

[0139] Referring to the energy distribution graph 508, in an embodiment, the energy consumption prediction (see, 512) indicates the set of baseline vessel operating parametersP24-056PCT1stored in the database 204. In another embodiment, the energy consumption prediction (see, 512) indicates the energy consumption predicted for the vessel 104 for the at least one performance condition based, at least in part, on the set operating vessel parameters. The deviation 510 shows the energy consumption that is equal to or greater than the threshold deviation value (e.g., 5% from the energy consumption prediction) (see, 512)). The energy consumption that is less (see, 514) than the threshold deviation value is shown in the energy distribution graph 508.

[0140] FIG. 5B illustrates a schematic representation of a GUI 520 for the shore operator 116 depicting alert information 522 of at least one operational cause 524 associated with the vessel 104, in accordance with an embodiment of the present disclosure. The disparity analysis module 228 is configured to provide the voyage information (see, 526) indicating the starting point and destination information to the shore system 106. The disparity analysis module 228 is further configured to provide information related to the remaining voyage time (see, 528) based on the voyage plan decided by the disparity analysis module 228.

[0141] In an embodiment, the alert information 522 includes information related to the at least one operational cause 524 associated with the vessel 104. To reduce the fuel consumption and to optimize the performance of the vessel 104, the at least one operational recommendations generated by the disparity analysis module 228 are rendered on the GUI 520 for the shore operator 116. In particular, the disparity analysis module 228 is configured to determine the at least one operational cause 524, by verifying whether the turbochargers of the vessel 104 are engaged based on the current operating load of the vessel 104.

[0142] The GUI 520 also includes the expected fuel wastage (see, 530) by the vessel 104 due to the deviation 510 in the performance of the vessel 104. In an embodiment, the server system 200 is configured to provide a communication interface between the vessel operator 112 and the shore operator 116. More specifically, the server system 200 provides the GUI 520 that allows messaging between the shore operator 116 and the vessel operator 112. The GUI 520 shows a history (see, 532) of communication messages between the shore operator 116 and the vessel operator 112. For instance, the GUI 520 depicts a message: “24 AUG 2024 09:37 UTC: ALERT CREATED FOR OPERATIONAL CAUSES" in the communication history (see, 532).

[0143] FIG. 5C illustrates a schematic representation of a GUI 560 for the vessel operator 112 showing at least one operational recommendation 562 associated with the at least one operational cause (see, 564) of the vessel 104, in accordance with an embodiment of the present disclosure. The operational flow 566 shows a process of resolving the at leastP24-056PCT1one operational cause (see, 564) and the at least one technical cause (not shown) associated with the vessel 104. Upon resolving the at least one operational cause (see, 564) and the at least one technical cause, if the deviation in the performance of the vessel 104 still exists, the performance test of the vessel 104 needs to be performed (see, the operational flow 566).

[0144] In an embodiment, the GUI 560 rendered with alert information 568 includes information related to the at least one operational cause associated with the vessel 104. In response to determining that the at least one cause is an operational cause (see, 564), the disparity analysis module 228 is configured to determine the set of corrective actions to be performed to address the operational cause (see, 564) based, at least in part, on the subset of non-standard vessel parameters and the set of predefined rules. In particular, the disparity analysis module 228 is configured to determine the at least one operational cause (see, 564), by verifying whether the turbochargers of the vessel 104 are engaged based on the current operating load of the vessel 104. The disparity analysis module 228 is configured to determine the at least one recommendation (see, 562) for resolving the operational cause (see, 564) based, at least in part, on the set of corrective actions. To reduce the fuel consumption and to optimize the performance of the vessel 104, the at least one operational recommendations (see, 562) generated by the disparity analysis module 228 are rendered on the GUI 560 for the vessel operator 112. The GUI 560 is also rendered with a calendar (see, 570) to allow the vessel operator 112 to schedule a date and time to perform the engagement or disengagement of the turbochargers of vessel 104. The at least one operational cause (see, 564) associated with the vessel 104 may be resolved by the vessel operator 112 by performing the at least one operational recommendation (see, 562) rendered on the GUI 560.

[0145] FIG. 6A illustrates a schematic representation of a GUI 600 for the shore operator 116 depicting alert information 602 of at least one technical cause 604 associated with the vessel 104, in accordance with an embodiment of the present disclosure. Upon resolution of the at least one operational cause (see, 564) shown in the FIG. 5C by the vessel operator 112, the at least one technical cause 604 associated with the vessel 104 is rendered on the GUI 600 by the server system 200. The disparity analysis module 228 is configured to provide the voyage information (see, 606) indicating the starting point and destination information and information related to the remaining voyage time (see, 608) of the vessel 104 to the shore system 106. . In an embodiment, the GUI 600 is rendered with an alert message 610 representing an alert text indicating the deviation of the energy consumption.

[0146] In an embodiment, the alert information 602 includes information related to the at least one technical cause 604 associated with the vessel 104. To reduce the fuel consumption and to optimize the performance of the vessel 104, the at least one technical recommendation (see, 612, 614) is rendered on the GUI 600 for the shore operator 116.P24-056PCT1

[0147] In response to determining that the at least one cause is the at least one technical cause, the disparity analysis module 228 is configured to determine the set of corrective actions to be performed to address the at least one technical cause based, at least in part, on the subset of non-standard vessel parameters and the set of predefined rules. In particular, the disparity analysis module 228 is configured to determine the at least one technical cause 604, by verifying whether the one or more warnings (see, 612) in the latest performance report are rectified by the vessel operator 112. In an instance, the disparity analysis module 228 is configured to check whether the average value of the Pmax is high and / or whether the scavenging air pressure is high. The disparity analysis module 228 is further configured to determine the quality of data (see, 614) associated with the performance of the vessel 104. In particular, disparity analysis module 228 is configured to determine whether the one or more meters associated with the vessel 104 are calibrated correctly within the predetermined time {e.g., 30 days). In an instance, the calibration of the one or more meters includes the calibration of the shaft power meter associated with the engine of the vessel 104, and the calibration of the mass flow meter associated with the engine of the vessel 104.

[0148] In an embodiment, the GUI 600 also displays the expected date (see, 616) for resolving the at least one technical cause regarding the scheduling (see, 618) of the performance test and the expected date (see, 618) for resolving the issues, when the vessel operator 112 has inputted the corresponding information in the respective GUI (see, the GUI 308 of FIG. 4). The GUI 600 also includes the expected fuel waste (see, 620) by the vessel 104 due to the deviation 510 in the performance of the vessel 104. The GUI 600 also allows messaging ( / .e., chat) between the shore operator 116 and the vessel operator 112. The history (see, 622) of messaging between the shore operator 116 and the vessel operator 112 is shown in the GUI 600. For instance, the GUI 600 depicts a message: “24 AUG 2024 09:37 UTC: ALERT CREATED FOR TECHNICAL CAUSES” in the communication history (see, 622).

[0149] FIG. 6B illustrates a schematic representation of a GUI 630 for the vessel operator 112 showing at least one technical recommendation associated with the at least one technical cause (see, 632, 634) of the vessel 104, in accordance with an embodiment of the present disclosure. The operational flow 636 shows the resolved at least one operational cause (see, 564) and the unresolved at least one technical cause (see, 632, 634) associated with the vessel 104.

[0150] In an embodiment, the GUI 630 is rendered with alert information 638 including detailed information related to the at least one technical cause (see, 632, 634) associated withP24-056PCT1the vessel 104. To reduce the fuel consumption and to optimize the performance of the vessel 104, the at least one technical recommendation generated by the disparity analysis module 228 is rendered on the GUI 630 for the vessel operator 112. In particular, the disparity analysis module 228 is configured to determine the at least one technical cause (see, 632, 634), by verifying whether the one or more warnings in the latest performance report are rectified by the vessel operator 112. In an instance, the disparity analysis module 228 is configured to check whether the average value of the Pmax is high and / or whether the scavenging air pressure is high. The disparity analysis module 228 is further configured to determine whether the one or more meters (see, 634) associated with the vessel 104 are calibrated correctly. In an instance, the calibration of the one or more meters includes the calibration of the shaft power meter associated with the engine of the vessel 104, and the calibration of the mass flow meter associated with the engine of the vessel 104. The vessel operator 112 may schedule (see, 640) the date and time to perform one or more actions associated with the vessel 104 for resolving the at least one technical cause (see, 632, 634).

[0151] FIG. 6C illustrates a schematic representation of a GUI 650 for the shore operator 116 depicting alert information 652 of at least one technical cause 654 associated with the vessel 104, in accordance with an embodiment of the present disclosure. The disparity analysis module 228 is configured to provide the voyage information (see, 656) including indicating the starting point and destination information to the shore system 106. The notification module 230 is further configured to provide information related to the remaining voyage time (see, 658) based on the voyage plan decided by the disparity analysis module 228. The GUI 650 includes the expected fuel wastage (see, 660) by the vessel 104 due to the deviation 510 in the performance of the vessel 104. The GUI 650 also allows communication messages between the shore operator 116 and the vessel operator 112. The history (see, 662) of communication messages between the shore operator 116 and the vessel operator 112 is shown in the GUI 650. For instance, the GUI 650 depicts the messages rendered to the shore operator 116: “24 AUG 2024 09:37 UTC: ALERT CREATED FOR TECHNICAL CAUSES", “22 AUG 2024 11:45 UTC: ALERT CHANGED STATUS TO AWAITING ACTION FOR TECHNICAL CAUSES", and “21 AUG 2024 18:11 UTC: ALERT CREATED FOR TECHNICAL CAUSES” in the communication history (see, 662). For instance, the GUI 650 also depicts the additional information related to the availability of certain vessel components to the shore operator 116. In an instance, the additional message shown to the shore operator in the communication history (see, 622) is: “DRAIN TANK FLOWMETER IS OUT OF ORDER".

[0152] In an embodiment, the alert information 652 includes information related to the at least one technical cause 654 associated with the vessel 104. To reduce the fuelP24-056PCT1consumption and to optimize the performance of the vessel 104, the at least one technical recommendation (see, 664, 666) generated by the disparity analysis module 228 is rendered on the GUI 650 for the shore operator 116. In particular, the disparity analysis module 228 is configured to determine the at least one technical cause 654, by verifying whether the one or more warnings (see, 664) in the latest performance report are rectified by the vessel operator 112. The one or more warnings (see, 664) include but are not limited to, checking for a high average value of the Pmax, a high value for PComP, a low value for scavenging air pressure, a high value for an inlet temperature of the turbocharger, and a high value of an air filter pressure drip. The disparity analysis module 228 is further configured to determine the quality of data (see, 666) whether the calibration of the shaft power meter associated with the engine of the vessel 104, and the calibration of the mass flow meter associated with the engine of the vessel 104 are performed within a time period ( / .e., within 30 days). In an embodiment, the GUI 600 also displays information on the expected date (see, 668) of resolving the issues by the vessel operator 112. The date and time of the performance test (see, 670) of the vessel 104 scheduled by the vessel operator 112 is rendered on the GUI 650.

[0153] The GUI 650 includes a messaging text box 672 that allows the shore operator 116 to enter at least one message to the vessel operator 112. Upon receiving messages from the vessel operator 112, the GUI 650 also allows the shore operator 116 to acknowledge the received message using the acknowledgment button 678 on the GUI 650. In an embodiment, the shore operator 116 may close (see, 674) the at least one alert 314 as resolved or followed, when the vessel operator 112 has performed the at least one technical recommendation for the at least one technical cause 654 associated with the vessel 104. In an embodiment, the shore operator 116 may reactivate (see, 676) the at least one alert 314 as active, when the vessel operator 112 has not performed the at least one technical recommendation on the scheduled date and time.

[0154] FIG. 6D illustrates a schematic representation of a GUI 680 for the vessel operator 112 configured to allow the vessel operator 112 to schedule a performance test, in accordance with an embodiment of the present disclosure. The GUI 680 is rendered with alert information 682 including information related to the deviation in the performance of the vessel 104 even after resolving the at least one operational recommendation (see, the GUI 560) and the at least one technical recommendation (see, the GUI 630). To reduce fuel consumption and to optimize the performance of the vessel 104, the performance test associated with the working of the vessel 104 needs to be performed by the vessel operator 112. The operational flow 684 shows the resolved (see, 686) at least one operational cause and the resolved at least one technical cause associated with the vessel 104. If the deviation in the performance of the vessel 104 still exists even after addressing the at least one operationalP24-056PCT1recommendation (see, the GUI 560) and the at least one technical recommendations (see, the GUI 630), the vessel operator 112, need to conduct the performance test (see, 688). The date and time of the performance test of the vessel 104 may be scheduled by the vessel operator 112 using the calendar and timer (see, 690). The GUI 680 also allows the vessel operator 112 to send a message to the shore operator 116 using the messaging text box 692.

[0155] FIG. 7 illustrates a flow diagram of a method of optimizing the performance of the vessel 104, in accordance with an embodiment of the present disclosure. The method 700 depicted in the flow diagram may be executed by a server system, for example, the server system 200. The sequence of operations of the method 700 may not be necessarily executed in the same order as they are presented. Further, one or more operations may be grouped and performed in the form of a single step, or one operation may have several sub-steps that may be performed in parallel or in a sequential manner. Operations of the method 700, and combinations of operations in the method 700 may be implemented by, for example, hardware, firmware, a processor, circuitry, and / or a different device associated with the execution of software that includes one or more computer program instructions. The plurality of operations is depicted in the process flow of the method 700. The process flow starts at operation 702.

[0156] At 702, the method 700 includes accessing a set of vessel operating parameters recorded at predefined intervals from a vessel {e.g., the vessel 104) and an energy consumption prediction {e.g., the energy consumption prediction 414) for at least one performance condition from a database e.g., the database 204). As described earlier, the set of vessel operating parameters refers to the vessel operating parameters that have been recorded from one or more data sources (or at least one data source of the one or more data sources) such as one or more data sources 108 associated with different vessels at one or more frequencies. For instance, the set of vessel operating parameters may be recorded at predefined intervals, that is, every few milliseconds, seconds, minutes, or so on. In another implementation, the data recording process for a few vessel operating parameters may take place using medium-frequency recording (every few minutes to hours) or low-frequency recording (every few hours or days) as well. In an embodiment, the vessel operating parameters represent the real-time vessel operating parameters received at predefined intervals from the vessel 104 for a specific performance condition such as a specified load. In another embodiment, the set of vessel operating parameters is selected from the plurality of vessel operating parameters such that each of these parameters satisfies the stability criteria described earlier.

[0157] In an embodiment, the energy consumption prediction 414 for at least one performance condition is predicted by the prediction model of a server system, such as theP24-056PCT1server system 200. In another embodiment, the data processing module 226 is configured to compute the energy consumption prediction 414 for the vessel 104 in the database 204 based, at least in part, on the set of baseline vessel operating parameters.

[0158] At 704, the method 700 includes determining an actual energy consumption {e.g., the actual energy consumption 412) of the vessel 104 for the at least one performance condition based, at least in part, on the set of vessel operating parameters. As described earlier, the actual energy consumption 412 is determined by the data processing module 226 of the server system 200.

[0159] At 706, the method 700 includes computing a deviation between the actual energy consumption 412 and the energy consumption prediction 414. As described earlier, a disparity analysis module, such as the disparity analysis module 228 of the server system 200 is configured to compute the deviation between the actual energy consumption 412 and the energy consumption prediction 414.

[0160] At 708, the method 700 includes in response to determining that the deviation is equal to or greater than a threshold deviation value, generating at least one alert indicating the presence of the deviation in the vessel 104. For instance, if the deviation is equal to or greater than the threshold deviation value for a predefined time period ( / .e., 1 hour), a notification module, such as the notification module 230, is configured to generate an alert message indicating the presence of the deviation in the vessel 104. The alert can be transmitted to the operator of the vessel 104 as well.

[0161] The disclosed method 700 with reference to FIG. 7, or one or more operations of the server system 200 may be implemented using software including computer-executable instructions stored on one or more computer-readable media (e.g., non-transitory computer- readable media, such as one or more optical media discs, volatile memory components (e.g., DRAM or SRAM), or nonvolatile memory or storage components (e.g., hard drives or solid- state nonvolatile memory components, such as Flash memory components) and executed on a computer (e.g., any suitable computer, such as a laptop computer, netbook, Web book, tablet computing device, smartphone, or other mobile computing devices). Such software may be executed, for example, on a single local computer or in a network environment (e.g., via the Internet, a wide-area network, a local-area network, a remote web-based server, a clientserver network (such as a cloud computing network), or other such networks) using one or more network computers.

[0162] Additionally, any of the intermediate or final data created and used during the implementation of the disclosed methods or systems may also be stored on one or more computer-readable media (e.g., non-transitory computer-readable media) and are consideredP24-056PCT1to be within the scope of the disclosed technology. Furthermore, any of the software- based embodiments may be uploaded, downloaded, or remotely accessed through a suitable communication means. Such suitable communication means include, for example, the Internet, the World Wide Web (WWW), an intranet, software applications, cable (including fiber optic cable), magnetic communications, electromagnetic communications (including RF, microwave, and infrared communications), electronic communications, or other such communication means.

[0163] Although the invention has been described with reference to specific exemplary embodiments, it is noted that various modifications and changes may be made to these embodiments without departing from the broad scope of the invention. For example, the various operations, blocks, etc., described herein may be enabled and operated using hardware circuitry (for example, Complementary Metal Oxide Semiconductor (CMOS) based logic circuitry), firmware, software, and / or any combination of hardware, firmware, and / or software (for example, embodied in a machine-readable medium). For example, the apparatuses and methods may be embodied using transistors, logic gates, and electrical circuits (for example, Application Specific Integrated Circuit (ASIC) circuitry and / or Digital Signal Processor (DSP) circuitry).

[0164] Particularly, the server system 200 and its various components may be enabled using software and / or using transistors, logic gates, and electrical circuits (for example, integrated circuit circuitry such as ASIC circuitry). Various embodiments of the invention may include one or more computer programs stored or otherwise embodied on a computer-readable medium, wherein the computer programs are configured to cause the processor or the computer to perform one or more operations. A computer-readable medium storing, embodying, or encoded with a computer program, or similar language, may be embodied as a tangible data storage device storing one or more software programs that are configured to cause the processor or computer to perform one or more operations. Such operations may be, for example, any of the steps or operations described herein. In some embodiments, the computer programs may be stored and provided to a computer using any type of non-transitory computer-readable media. Non-transitory computer-readable media includes any type of tangible storage media.

[0165] Examples of non-transitory computer-readable media include magnetic storage media (such as floppy disks, magnetic tapes, hard disk drives, etc.), optical magnetic storage media (e.g. magneto-optical disks), Compact Disc Read-Only Memory (CD-ROM), Compact Disc Recordable (CD-R), compact disc rewritable (CD-R / W), Digital Versatile Disc (DVD), BLU-RAY® Disc (BD), and semiconductor memories (such as mask ROM, programmable ROM (PROM), (erasable PROM), flash memory, Random Access MemoryP24-056PCT1(RAM), etc.). Additionally, a tangible data storage device may be embodied as one or more volatile memory devices, one or more non-volatile memory devices, and / or a combination of one or more volatile memory devices and non-volatile memory devices. In some embodiments, the computer programs may be provided to a computer using any type of transitory computer-readable media. Examples of transitory computer-readable media include electric signals, optical signals, and electromagnetic waves. Transitory computer-readable media can provide the program to a computer via a wired communication line (e.g., electric wires, and optical fibers) or a wireless communication line.

[0166] Various embodiments of the invention, as discussed above, may be practiced with steps and / or operations in a different order, and / or with hardware elements in configurations, which are different than those which, are disclosed. Therefore, although the invention has been described based on these exemplary embodiments, it is noted that certain modifications, variations, and alternative constructions may be apparent and well within the scope of the invention.

[0167] Although various exemplary embodiments of the invention are described herein in a language specific to structural features and / or methodological acts, the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as exemplary forms of implementing the claims.P24-056PCT1

Claims

44CLAIMS:

1. A computer-implemented method, comprising: accessing a set of vessel operating parameters recorded at predefined intervals from a vessel and an energy consumption prediction for at least one performance condition from a database; determining an actual energy consumption of the vessel for the at least one performance condition based, at least in part, on the set of vessel operating parameters; computing a deviation between the actual energy consumption and the energy consumption prediction; and in response to determining that the deviation is equal to or greater than a threshold deviation value, generating at least one alert indicating presence of the deviation in the vessel.

2. The computer-implemented method as claimed in claim 1, wherein the at least one alert is generated in response to determining that the deviation is equal to or greater than a threshold deviation value for a predefined time period.

3. The computer-implemented method as claimed in claim 2, further comprising: determining at least one cause of the deviation based, at least in part, on the set of vessel operating parameters; and generating at least one recommendation for resolving the at least one cause based, at least in part, on a set of predefined rules.

4. The computer-implemented method as claimed in claim 3, wherein determining the at least one cause of the deviation comprises: identifying at least one operational cause, at least one technical cause or a combination thereof based, at least in part, on the set of vessel operating parameters.

5. The computer-implemented method as claimed in claim 3, wherein generating the at least one recommendation comprises: accessing a vessel performance report associated with the vessel; in response to determining that the at least one cause is a technical cause, identifying a subset of non-standard vessel parameters from the set of vessel operating parameters based, at least in part, on the vessel performance report and a set of technical performance thresholds; determining a set of corrective actions to be performed to address the technical causeP24-056PCT145 based, at least in part, on the subset of non-standard vessel parameters and the set of predefined rules; and determining the at least one recommendation for resolving the technical cause based, at least in part, on the set of corrective actions.

6. The computer-implemented method as claimed in claim 5, further comprising: in response to determining that no corrective action can be performed to address the technical cause, requesting an operator of the vessel to regenerate the vessel performance report.

7. The computer-implemented method as claimed in claim 3, wherein generating the at least one recommendation comprises: in response to determining that the at least one cause is an operational cause, identifying a subset of non-standard vessel parameters from the set of vessel operating parameters based, at least in part, on a set of operational performance thresholds; determining a set of corrective actions to be performed to address the operational cause based, at least in part, on the subset of non-standard vessel parameters and the set of predefined rules; and determining the at least one recommendation for resolving the operational cause based, at least in part, on the set of corrective actions.

8. The computer-implemented method as claimed in claim 3, further comprising: facilitating transmission of the least one recommendation to an operator of the vessel.

9. The computer-implemented method as claimed in claim 3, further comprising: receiving at least one status information from an operator of the vessel, the at least one status information indicating one or more actions performed by the operator in response to the receiving the at least one recommendation; and performing at least one of: in response to determining that the one or more actions comply with the at least one recommendation, changing a status of the at least one alert; or in response to determining that the one or more actions indicate a request for placing the alert on hold, setting an on-hold status to the at least one alert.

10. The computer-implemented method as claimed in claim 9, wherein changing the status of the at least one alert comprises: accessing an updated set of vessel operating parameters recorded at predefinedP24-056PCT146 intervals for a new cycle from the vessel and an updated energy consumption prediction for the at least one performance condition for the new cycle from the database; determining an updated actual energy consumption for the at least one performance condition for the new cycle based, at least in part, on the set of vessel operating parameters; computing an updated deviation between the updated actual energy consumption and the updated energy consumption prediction; and performing at least one of: in response to determining that the updated deviation is lower than the threshold deviation value, setting a resolved status to the at least one updated alert; or in response to determining that the updated deviation is equal to or greater than the threshold deviation value, generating at least one updated recommendation.

11. The computer-implemented method as claimed in claim 1 , wherein accessing the set of vessel operating parameters comprises: recording a plurality of vessel operating parameters from at least one data source associated with the vessel at one or more frequencies; aggregating the plurality of recorded vessel operating parameters at the predefined intervals; and extracting the set of vessel operating parameters from the plurality of aggregated vessel operating parameters based, at least in part, on stability criteria associated with the vessel, wherein the stability criteria define one or more stable operating conditions for the vessel.

12. The computer-implemented method as claimed in claim 1 , wherein accessing the energy consumption prediction comprises: accessing the set of vessel operating parameters recorded at the predefined intervals from the database; generating a set of features based, at least in part, on the set of vessel operating parameters; and generating and storing, by a prediction model, the energy consumption prediction for the at least one performance condition in the database based, at least in part, on applying the set of features on the prediction model.

13. The computer-implemented method as claimed in claim 1 , wherein accessing the energy consumption prediction comprises: accessing a set of baseline vessel operating parameters from the database; andP24-056PCT1computing and storing the energy consumption prediction for the at least one performance condition in the database based, at least in part, on the set of baseline vessel operating parameters.

14. A server system, comprising: a communication interface; a memory configured to store instructions; and a processor in communication with the communication interface and the memory, the processor configured to execute the instructions stored in the memory and thereby cause the server system to perform at least in part to: access a set of vessel operating parameters recorded at predefined intervals from a vessel and an energy consumption prediction for at least one performance condition from a database; determine an actual energy consumption of the vessel for the at least one performance condition based, at least in part, on the set of vessel operating parameters; compute a deviation between the actual energy consumption and the energy consumption prediction; and in response to determining that the deviation is equal to or greater than a threshold deviation value, generate at least one alert indicating presence of the deviation in the vessel.

15. A non-transitory computer-readable storage medium comprising computer-executable instructions that, when executed by at least a processor of a server system, cause the server system to perform a method comprising: accessing a set of vessel operating parameters recorded at predefined intervals from a vessel and an energy consumption prediction for at least one performance condition from a database; determining an actual energy consumption of the vessel for the at least one performance condition based, at least in part, on the set of vessel operating parameters; computing a deviation between the actual energy consumption and the energy consumption prediction; and in response to determining that the deviation is equal to or greater than a threshold deviation value, generating at least one alert indicating presence of the deviation in the vessel.P24-056PCT1

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