SYSTEM AND METHOD FOR FACILITATING SMART ENERGY MANAGEMENT IN A NETWORK - Patent application

Abstract

The present disclosure provides systems and methods for facilitating smart energy management services in a network. In particular, the system provides smart energy management services to users, businesses, etc. The system may collect information related to the operation of one or more computing devices in the network, extract one or more parameters related to one or more energy sources based on the collected information, and provide personalized energy management services to each of the one or more computing devices in the network through network slicing. The energy management services may include one or more recommendations executed on the one or more computing devices.

Description

[Technical field]

[0001] Portions of the disclosure of this patent document contain material that is subject to intellectual property rights, such as, but not limited to, copyright, designs, trademarks, integrated circuit (IC) layout designs, and / or trade dress protection, owned by Jio Platforms Limited (JPL) or its affiliates (hereinafter referred to as the Owner). The Owner has no objection to the complete reproduction by anyone of the patent document or patent disclosure as it appears in the Patent and Trademark Office files and records, but otherwise reserves all rights whatsoever. All rights to such intellectual property are fully reserved by the Owner.

[0002] FIELD OF THE DISCLOSURE Embodiments of the present disclosure relate generally to energy management services in a network, and more particularly to managing and predicting energy consumption in a network. [Background technology]

[0003] The following description of related art is intended to provide background information regarding the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be recognized that this section is used only to enhance the reader's understanding of the present disclosure and is not to be used as an admission of prior art.

[0004] Economic growth and increasing consumption patterns are resulting in increased demand for energy. There are growing concerns about the costs and / or environmental impacts of energy production and consumption.

[0005] At the same time, the deployment of more advanced metering systems, such as "interval" or "time of use" meters, and more sophisticated "smart meters", is increasing around the world. While traditional meters simply record total consumption without keeping any record of time of use, these more advanced meters are able to record energy consumption in more detail, for example on an hourly basis or more frequently.

[0006] However, the benefits of such technologies can only be fully realized if users have access to the information they need about their energy usage and are empowered to act effectively on that information.

[0007] Thus, while technological infrastructure is becoming available that allows users to improve their behavior, there remains a need to provide users with effective tools and facilities to review and act on the information available to them in an effective manner.

[0008] Therefore, there is a need in the art to provide a system and method for facilitating smart monitoring of energy consumption in a network and providing smart energy management services to overcome the shortcomings of existing systems and methods. Summary of the Invention

[0009] This Summary is provided to introduce certain objectives and aspects of the disclosure in a simplified form that are further described in the Detailed Description below. This Summary is not intended to identify key features or the scope of the claimed subject matter.

[0010] In one aspect, the present disclosure relates to a system for facilitating energy management services. The system includes one or more processors and a memory operably coupled to the one or more processors, the memory including processor-executable instructions that, when executed, cause the one or more processors to record information corresponding to operation of one or more computing devices in a network, extract one or more parameters corresponding to one or more energy sources in the network based on the recorded information, and provide a personalized energy management service to each of the one or more computing devices over at least one slice of the network based on the extracted one or more parameters and the recorded information of the one or more computing devices. In one embodiment, the personalized energy management service may include one or more recommendations executed on the one or more computing devices.

[0011] In one embodiment, the information corresponding to operation of the one or more computing devices includes at least one of energy usage by the one or more computing devices over a period of time and connectivity of the one or more computing devices to the one or more energy sources.

[0012] In one embodiment, information corresponding to the operation of one or more computing devices is collected by one or more sensors associated with one or more computing devices in a network.

[0013] In one embodiment, the one or more parameters corresponding to the one or more energy sources include at least one of an amount of renewable energy generated by the one or more energy sources, a battery (charging) storage capacity, and dynamic energy pricing.

[0014] In one embodiment, the one or more recommendations correspond to at least one of: consumption of renewable energy generated by the one or more energy sources; and feedback of excess energy to the one or more energy sources based on dynamic energy pricing.

[0015] In one embodiment, the one or more processors are configured to predict expected energy consumption by the one or more computing devices for a predetermined period of time based on the recorded information and the extracted one or more parameters, thereby providing the one or more recommendations.

[0016] In an embodiment, a network includes the at least one slice for facilitating communication between one or more computing devices and the system.

[0017] In one embodiment, information corresponding to operation of one or more computing devices is collected from one or more sensors through a massive machine type communication (mMTC) network slice.

[0018] In an embodiment, the system may execute a smart contract to provide personalized energy management services to each of the one or more computing devices via the at least one slice of the network.

[0019] In one embodiment, the system may be implemented as a blockchain-based platform that executes smart contracts.

[0020] In some embodiments, the system may be implemented in a hierarchical network, which may include a layer 2 network in which smart contracts are layered on top of a layer 1 network of blockchain nodes, and aggregated data is submitted from the layer 2 network to the layer 1 network in the hierarchical network.

[0021] In another aspect, the disclosure relates to a method for providing energy management services, the method including: recording, by one or more processors, information corresponding to operation of one or more computing devices in a network; extracting, by the one or more processors, based on the recorded information, one or more parameters corresponding to one or more energy sources in the network; and providing, by the one or more processors, a personalized energy management service to each of the one or more computing devices over at least one slice of the network based on the extracted one or more parameters and the recorded information of the one or more computing devices. The personalized energy management service includes one or more recommendations executed on the one or more computing devices.

[0022] In one embodiment, the information corresponding to operation of the one or more computing devices includes at least one of energy usage by the one or more computing devices over a period of time and connectivity of the one or more computing devices to the one or more energy sources.

[0023] In one embodiment, information corresponding to the operation of one or more computing devices is recorded by one or more sensors associated with one or more computing devices in a network.

[0024] In one embodiment, the one or more parameters corresponding to the one or more energy sources include at least one of an amount of renewable energy generated by the one or more energy sources, a battery (charging) storage capacity, and dynamic energy pricing.

[0025] In one embodiment, the one or more recommendations correspond to at least one of: consumption of renewable energy generated by the one or more energy sources; and feedback of excess energy to the one or more energy sources based on dynamic energy pricing.

[0026] In one embodiment, the method includes predicting, by the one or more processors, expected energy consumption by the one or more computing devices for a predetermined period of time based on the recorded information and the extracted one or more parameters.

[0027] In one embodiment, information corresponding to the operation of one or more computing devices is collected from one or more sensors through an mMTC network slice.

[0028] In an embodiment, the method may include executing, by one or more processors, a smart contract to provide personalized energy management services to each of the one or more computing devices via the at least one slice of the network.

[0029] In one embodiment, the method may include implementing, by one or more processors, a blockchain-based platform that executes smart contracts.

[0030] In another aspect, the present disclosure relates to a user equipment. The user equipment includes one or more processors and a memory operably coupled to the one or more processors, the memory including processor-executable instructions that, when executed, cause the one or more processors to: transmit information corresponding to an operation of the user equipment to a system of a network; receive one or more recommendations from the system via at least one slice of the network; and execute the one or more recommendations for a predetermined period of time. The one or more recommendations are based on at least the information corresponding to the operation of the user equipment and one or more parameters corresponding to one or more energy sources in the network.

[0031] In an embodiment, information corresponding to operation of user equipment may be recorded by one or more sensors associated with the user equipment in the network.

[0032] In an embodiment, the one or more parameters corresponding to the one or more energy sources may include at least one of an amount of renewable energy generated by the one or more energy sources, a battery (charging) storage capacity, and dynamic energy pricing.

[0033] In an embodiment, the one or more recommendations may correspond to at least one of: consumption of renewable energy generated by the one or more energy sources; and feedback of excess energy to the one or more energy sources based on dynamic energy pricing.

[0034] In one embodiment, the memory includes processor-executable instructions that, when executed, cause the one or more processors to receive one or more recommendations from the system based on a smart contract executed on the system.

[0035] In one embodiment, the memory includes processor-executable instructions that, when executed, cause the one or more processors to receive one or more recommendations from a system implemented as a blockchain-based platform.

[0036] In one aspect, the disclosure relates to a non-transitory computer-readable medium including processor-executable instructions that cause a processor to record information corresponding to operation of one or more computing devices in a network; extract one or more parameters corresponding to one or more energy sources in the network based on the recorded information; and provide a personalized energy management service to each of the one or more computing devices over at least one slice of the network based on the extracted one or more parameters and the recorded information of the one or more computing devices, the personalized energy management service including one or more recommendations executed on the one or more computing devices.

[0037] Some of the objectives of the present disclosure that are satisfied by at least one embodiment herein are set forth herein below.

[0038] It is an object of the present disclosure to provide a system and method for facilitating smart energy management in a network.

[0039] The objective of the present disclosure is to predict and provide personalized smart energy management services to users or businesses.

[0040] An objective of the present disclosure is to facilitate network slicing based on knowledge of the time-criticality or network performance requirements of services, renewable energy availability, stored energy availability, and dynamic grid pricing.

[0041] An objective of the present disclosure is to provide a blockchain-based platform for executing smart contracts to provide smart energy management services.

[0042] The accompanying drawings, which are incorporated herein and constitute a part of the present invention, illustrate exemplary embodiments of the disclosed methods and systems, with like reference numerals referring to the same parts throughout the different drawings. The components in the drawings are not necessarily drawn to scale, emphasis instead being placed on clearly illustrating the principles of the invention. Some drawings may use block diagrams to illustrate components and may not represent the internal circuitry of each component. It will be recognized by those skilled in the art that the inventions in such drawings include inventions in electrical components, electronic components, or circuits commonly used to implement such components. [Brief description of the drawings]

[0043] [Figure 1] FIG. 1 illustrates an example network architecture in which embodiments of the present disclosure may be implemented. [Diagram 2]FIG. 1 illustrates an example network architecture for managing energy consumption associated with one or more computing device(s), in accordance with an embodiment of the present disclosure. [Diagram 3] FIG. 1 illustrates an example network architecture in which embodiments of the present disclosure may be implemented. [Figure 4] FIG. 1 illustrates an exemplary computer system in which embodiments of the present invention may be utilized, in accordance with embodiments of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0044] The foregoing will become more apparent from the following more detailed description of the present disclosure.

[0045] In the following description, for purposes of explanation, various specific details are set forth to provide a thorough understanding of the embodiments of the present disclosure. However, it will be apparent that the embodiments of the present disclosure may be practiced without these specific details. Some features described below may be used independently of each other or with any combination of other features. Individual features may not address all of the problems described above, or may address only some of the problems described above. Some of the problems described above may not be fully addressed by any of the features described herein.

[0046] The following description provides only exemplary embodiments and is not intended to limit the scope, applicability, or configuration of the present disclosure. Rather, the following description of exemplary embodiments will provide those skilled in the art with a helpful description for implementing the exemplary embodiments. It will be understood that various changes can be made in the function and arrangement of elements without departing from the spirit and scope of the present disclosure as set forth.

[0047] In the following description, specific details are given to provide a thorough understanding of the embodiments. However, it will be understood by those skilled in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form so as not to obscure the embodiments in unnecessary detail. In other examples, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail so as to avoid obscuring the embodiments.

[0048] It should also be noted that the individual embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe operations as a sequential process, many operations may occur in parallel or concurrently. Additionally, the order of operations may be rearranged. A process terminates when an operation of a process is completed, but there may be additional steps not included in the diagram. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, or the like. When a process corresponds to a function, its termination may correspond to a return of the function to the calling function or to the main function.

[0049] The words "exemplary" and / or "demonstrative" are used herein in the sense of serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. Moreover, any aspect or design described herein as "exemplary" and / or "demonstrative" should not necessarily be construed as preferred or advantageous over other aspects or designs, and is not meant to exclude equivalent exemplary structures and approaches known to those skilled in the art. Moreover, to the extent that the terms "includes," "has," "contains," and other similar terms are used in either the embodiments or claims to describe the invention, such terms are intended to be inclusive in a manner similar to the term "comprising" as an open transition term, without excluding any additional or other elements.

[0050] References throughout this specification to "one embodiment" or "an embodiment" or "an instance" or "one instance" mean that a particular feature, structure, or characteristic described in connection with an embodiment is included in at least one embodiment of the disclosure. Thus, the appearances of the phrase "in one embodiment" or "in an embodiment" in various places throughout this specification do not necessarily all refer to the same embodiment. Furthermore, particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0051] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms unless the context clearly indicates otherwise. Furthermore, it is understood that the terms "comprise" and / or "comprising", as used herein, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0052] The present disclosure provides a robust and efficient solution for facilitating smart energy management services in a network. In particular, the disclosed solution provides for predicting energy requirements of one or more individual devices in the network and recommending smart energy management services (or actions). For example, smart recommendations may be provided during non-peak hours based on energy consumption and differential pricing to one or more individual devices. Furthermore, the present disclosure utilizes one or more machine learning models to monitor and learn the energy usage behavior of one or more individual devices, whereby the machine learning models assist in predicting future energy consumption and / or optimal energy consumption by one or more individual devices in the network. Furthermore, demand for energy management services may vary among one or more individual devices based on latency, criticality, and / or bandwidth requirements. Smart energy management services may be provided to meet such requirements.

[0053] Accordingly, embodiments of the present disclosure generally relate to facilitating smart energy management services for a network. In particular, the system may provide a system for dynamically providing personalized energy management services to one or more devices in a network, by providing one or more recommendations corresponding to optimal energy consumption by the one or more devices, without limitation. In an embodiment, the present disclosure relates to smart device environments. In an embodiment, the present disclosure relates to the Internet of Things (IoT), and more specifically, to communication between devices via the IoT.

[0054] According to embodiments described herein, information corresponding to one or more devices in a network may be collected. In some embodiments, one or more sensors, such as, but not limited to, smart meters, may be deployed to record information of one or more devices. Furthermore, using the collected information, an artificial intelligence (AI) trigger system may predict future energy consumption of one or more devices in the network. In some embodiments, one or more parameters corresponding to one or more energy sources may be extracted based on which the AI ​​trigger system may make a prediction. In some embodiments, the one or more parameters include, but are not limited to, renewable energy generated by one or more energy sources, battery (charging) storage capacity, etc. The AI ​​trigger system may utilize an appropriate machine learning model to make such a prediction.

[0055] Thus, the present disclosure provides new insights across one or more devices in a network. In particular, information of one or more devices collected and recorded across a network (e.g., a network ecosystem) provides insights regarding energy consumption. Other similar benefits and advantages are provided by the present disclosure and are described in detail throughout the present disclosure.

[0056] Certain terms and phrases are used throughout this disclosure and have the following meanings in the context of the disclosure proceeding:

[0057] The term "Internet of Things" may refer to a computing environment in which physical objects are embedded in devices that enable the physical objects to achieve greater value and services by exchanging data with other systems and / or other connected devices. Each physical object is uniquely identifiable by its embedded device(s) and is capable of interoperating within the Internet infrastructure. As used herein, the acronym "IoT" means "Internet of Things."

[0058] The term "blockchain" may refer to any of the various blockchain technology types and / or blockchain fabrics that utilize smart contracts. Smart contracts for blockchains may reside on Etherium, Hyperledger, or Quorum. This is because some of the possible blockchain technologies or technology fabrics and variants of these and other blockchain ecosystems continue to be created. This may include hierarchical structures such as a layer-2 Lightning network where smart contracts are layered on a layer-1 Bitcoin network of blockchain nodes, or another blockchain realization (such as a virtual distributed ledger technology (DLT) network layered on top of the Ethereum network). Aggregated transactions may be submitted in such a hierarchical network from the layer-2 network to the layer-1 network. However, the use of blockchain and smart contracts is for illustrative purposes only and is not intended to be limited in scope to any particular blockchain technology.

[0059] The term "network slicing" may refer to a network architecture that allows multiplexing of independent virtualized logical networks over the same physical network infrastructure. Each slice of the network may be an isolated end-to-end network tailored to meet the different requirements dictated by a particular application.

[0060] The term "smart meter" may refer to one or more sensors that provide detailed information about consumption (e.g., energy in the form of electricity) to enhance knowledge about the state of the power grid that improves performance and quality of service for end users.

[0061] The term "microgrid" can refer to a small network of energy / electricity users, usually connected to a centralized national grid, but with local energy sources capable of functioning independently.

[0062] Various embodiments throughout the present disclosure will now be described in more detail with reference to FIGS.

[0063] FIG. 1 illustrates an exemplary network architecture (100) in which or with which embodiments of the present disclosure may be implemented.

[0064] Referring to FIG. 1, a network architecture (100) may include one or more first computing devices (104-1, 104-2...104-N) associated with one or more entities (102-1, 102-2...102-N) deployed in an environment. Those skilled in the art will appreciate that the one or more entities may be individually referred to as entities (102) and collectively referred to as entities (102). In an embodiment, the entities (102) may include a user, a business, an organization, an industry, a smart home environment, a utility, etc. In an embodiment, the entities (102) may be referred to as a community that includes some of the one or more first computing devices (104-1, 104-2...104-N). Those skilled in the art will appreciate that each of the entities (102) may be in different geographic locations that may be proximate to each other or located remotely with respect to each other. Although three entities (102) are shown in FIG. 1, any number of entities (102) may be included without departing from the scope of the ongoing description.

[0065] Further, those skilled in the art will appreciate that one or more first computing devices may be individually referred to as a first computing device (104) and collectively referred to as a first computing device (104). In an embodiment, the first computing devices (104) may be referred to as user equipment (UE). Those skilled in the art will appreciate that the terms "first computing device(s)" and "UE" may be used interchangeably throughout this disclosure. Although three computing devices (104) are shown in FIG. 1, any number of computing devices (104) may be included without departing from the scope of the ongoing description.

[0066] In some embodiments, the first computing device (104) may include a smart device operating in a smart environment, such as an IoT system. In such embodiments, the first computing device (104) may include, but is not limited to, a smartphone, a smart watch, a smart sensor (e.g., mechanical, thermal, electrical, magnetic, etc.), a networked appliance, a networked peripheral device, a networked lighting system, a communication device, a networked vehicle accessory, a networked vehicle device, a smart accessory, a tablet, a smart television (TV), a computer, a smart security system, a smart home system, other devices for monitoring or interacting with the user and / or entity (102), or other devices for the user and / or entity (102), or any combination thereof. In an embodiment, the first computing device (104) may include one or more of the following components: sensors, radio frequency identification (RFID) technology, global positioning system (GPS) technology, mechanisms for real-time acquisition of data, passive or interactive interfaces, mechanisms for outputting and / or inputting sound, light, heat, electricity, mechanical power, chemical presence, biological presence, location, time, identity, other information, or any combination of these components.

[0067] Those skilled in the art will recognize that the first computing device (104) may include, but is not limited to, intelligent, multi-sensing, network-connected devices that may be seamlessly integrated with each other and / or with a central server, a cloud computing system, or any other network-connected device.

[0068] In some embodiments, the first computing device (104) may include, but is not limited to, a handheld wireless communication device (e.g., a mobile phone, a smartphone, a phablet device, etc.), a wearable computing device (e.g., a head mounted display computing device, a head mounted camera device, a wristwatch type computing device, etc.), a GPS device, a laptop computer, a tablet computer, or another type of portable computer, a media playback device, a portable gaming system, and / or any other type of computing device (104) with wireless communication capabilities. In one embodiment, the first computing device (104) may include any electrical, electronic, electromechanical device or equipment, or a combination of one or more of the above devices, such as, but not limited to, a virtual reality (VR) device, an augmented reality (AR) device, a laptop, a general purpose computer, a desktop, a personal digital assistant, a tablet computer, a mainframe computer, or any other computing device, and the first computing device (104) may include one or more built-in or externally coupled accessories, including, but not limited to, visual aids such as a camera, audio aids, microphones, keyboards, and input devices for receiving input from a user or entity (102), such as a touchpad, a touch-enabled screen, an electronic pen, etc.

[0069] Those skilled in the art will recognize that the first computing device or UE (104) may not be limited to the devices mentioned, and various other devices may be used.

[0070] Referring to FIG. 1 , a first computing device (104) may communicate with a system (110), such as an energy management system, via a network (106). In some embodiments, the network (106) may include at least one of a next generation (5G) network, etc. The network (106) may enable the first computing device (104) to communicate with other devices in the network architecture (100) and / or with the system (110). The network (106) may include a wireless card or some other transceiver connection to facilitate this communication. In other embodiments, the network (106) may be implemented as or include any of a variety of different communication technologies, such as a wide area network (WAN), a local area network (LAN), a wireless network, a mobile network, a virtual private network (VPN), the Internet, a public switched telephone network (PSTN), etc.

[0071] According to an embodiment of the present disclosure, the system (110) may be a smart energy management system. In particular, the system (110) may be designed and configured to manage energy consumption of a first computing device (104) associated with one or more entities (102). Thus, the system (110) has the ability to separately monitor and manage energy usage across some of the individual entities (102), as well as within the entirety of the entities (102). Alternatively or additionally, the system (110) may manage demand response of the first computing device (104)(s).

[0072] Referring to FIG. 1 , the system 110 may be communicatively coupled to a second computing device 108. The second computing device 108 may be, for example, a conventional personal computer (PC) or terminal interface, or a flat panel touch screen display, etc. In some embodiments, the second computing device 108 may be associated with a main entity 114, such as, but not limited to, an energy grid, a microgrid, etc. The system 110 may communicate with the main entity 114 via the second computing device 108. In some embodiments, a user of the main entity 114 may control and manage each of the entities 102 via the second computing device 108 without necessarily being on-site at any of the entities 102. Because the main entity (114) communicates with the entity (102) via the system (110) and the network (106), the main entity (114) may be located in any geographic location and provides complete control and management of all of the first computing devices (104) in the entity (102).

[0073] In some embodiments, the main entity (114) may include, but is not limited to, a microgrid of renewable energy producers that may cooperate in the network architecture (100) to optimize energy consumption. In some embodiments, the main entity (114) may include one or more energy storage facilities for storing dynamic surplus energy generated. Alternatively or additionally, the surplus energy may be sold back to the main entity (114) based on optimizing the supply and demand of energy consumption at the first computing devices (104). As described in more detail herein, the main entity (114) may provide energy to the various first computing devices (104) during certain times, such as, but not limited to, peak hours, non-peak hours, etc. One or more alternative energy sources may also be provided to provide energy to the first computing devices (104).

[0074] In all embodiments, it is not necessary for the main entity (114) to provide energy directly to the first computing device (104). For example, the main entity (114) may be coupled to an energy system of one or more entities (102) and energy would be provided to the first computing device (104) via that path.

[0075] In another embodiment, the first computing device (104) may represent an aggregate energy using node of a microgrid. The group of entities (102) may comprise various microgrid nodes that consume energy. Alternatively or additionally, the group of entities (102) may generate energy. In an exemplary embodiment, each of the entities (102) may represent an individual home in a smart city that generates renewable energy. In another exemplary embodiment, the first computing device (104) may represent an electric vehicle charging facility, a drone charging station, etc. In an exemplary embodiment, the first computing device (104), the entities (104), and / or the main entity (114) may refer to distributed energy resources (DERs) that may generate and / or consume energy.

[0076] Referring to FIG. 1, the system 110 may be operatively coupled to a server 112. In some embodiments, the first computing device 104 and / or the second computing device 108 may be capable of data communication and information sharing with the server 112 via the network 106. In some embodiments, the server 112 may be a centralized server or cloud computing system, or any other networked device. In some embodiments, the server 112 may include an AI trigger engine that utilizes one or more machine learning models and / or blockchain microservices. The AI ​​trigger engine may provide support for predictive models that may enable early detection of trending digital assets. An increase in viral trends related to interactions observed on social media related to the digital assets or an increase in interest in acquiring digital assets in a blockchain system may be used as a trigger. Different machine learning models, such as classification-based or regression-based approaches related to previous trending digital assets, may be used to enable predictions. The classification techniques may include machine learning techniques such as K-means clustering, decision tree-based classification, random forest techniques, deep learning-based classification systems, or ensemble learning systems. The regression-based techniques may include linear regression, or non-linear regression-based techniques, time series-based analysis, deep learning systems for time series analysis such as recurrent neural network (RNN) systems, long short-term memory (LSTM) systems, etc. The server (112) may also be used for temporary and long-term storage of data associated with the management of energy consumption within the entity (102). This may include predicted energy consumption data of the first computing devices (104) within the entity (102), actual recorded energy consumption data, and a history of priorities or preferences assigned to particular first computing devices (104) over time. For example, one or more users (not shown in FIG. 1) may assign priorities to one or more first computing devices (104).In an embodiment, the meaning of different levels of priority may be flexible and may change depending on the time of day, day of the week, or some other time factor. By providing the system (110) with a priority of each first computing device (104), the user(s) of the first computing device(s) (104) indicate a preferred sequence for disconnecting the first computing device(s) (104) (e.g., a power grid) from the main entity (114) or regulating energy to the first computing device (104). As a result, the system (110) may provide smart energy management services, such as, but not limited to, disconnecting or managing the first computing devices (104) in a predefined order, leaving a level of control to the user(s).

[0077] In an embodiment, the system (110) may facilitate prediction or forecasting of energy consumption of the first computing devices (104) in the network architecture (100) based on monitoring data corresponding to operation of the first computing devices (104) over a period of time, and provide smart energy management services to one or more of the first computing devices (104). According to an embodiment, the network architecture (100) may include one or more sensors (not shown in FIG. 1 ), which have at least the capability of collecting and / or recording time-dependent readings of energy consumption of the one or more computing devices (104), but may also have more advanced functionality operable under the control of the system (110). Thus, the one or more sensors may generate information corresponding to operation of the one or more first computing devices (104). In an exemplary embodiment, the one or more sensors may collect information, such as, but not limited to, energy consumption at the one or more first computing devices (104) over a period of time. In an embodiment, the one or more sensors may include, but are not limited to, smart meters, and the like. The one or more sensors may be programmable and controllable to remotely detect, monitor, calculate, and control energy consumption of one or more first computing devices (104). Alternatively, the first computing device (104) may be an aggregation node, such as a smart meter that aggregates energy information across different consuming devices, such as household or business entities (102). In particular, the first computing device (104) may be implemented as a smart meter in an exemplary embodiment.

[0078] In an embodiment, the one or more sensors may transmit collected information to a local aggregation node, such as the first computing device (104), which may forward the information to the system (110) for further analysis. In an exemplary embodiment, the one or more sensors may communicate with the local aggregation node through a suitable protocol, including, but not limited to, Wireless Fidelity (Wi-Fi), Bluetooth, Zigbee, cellular 4G / 5G / 6G device-to-device communications, and the like. In another exemplary embodiment, the one or more sensors may communicate over longer distances using a long-range protocol, such as, but not limited to, a long-range (LORA) protocol. Those skilled in the art will appreciate that LORA may refer to a physical layer protocol that uses spread spectrum modulation to support long-range communications at the expense of narrow bandwidth. In an embodiment, the local aggregation node may communicate with the system (110) using one or more communication technologies, such as, but not limited to, optical, cellular radio, satellite, and the like.

[0079] In addition to the energy usage information, the one or more sensors may provide additional information about the entity (102) associated with the first computing device (104), such as one or more of light, temperature, and inactivity levels of the first computing device (104) within the entity (102). This additional data may also be transmitted to the system (110), which may be used alone or in combination with other data to provide smart energy management services. In particular, the system (110) may identify energy consumption patterns for each of the first computing devices (104). In some embodiments, the system (110) may intelligently analyze the information to make decisions based on each device, such as whether and how much to charge for consumption of backup energy or generate demand response related commands for the first computing devices (104), and selectively reduce energy according to a number of rules.

[0080] In an embodiment, the system (110) receives one or more parameters from the server (112) to make decisions, i.e., provide smart energy management services. Accordingly, the server (112) stores one or more parameters related to the main entity (114). For example, the one or more parameters may include, but are not limited to, the amount of renewable energy produced, battery (charging) storage capacity, and dynamic energy pricing. In an embodiment, the server (112) may also store parameters related to pricing of provisioned backup energy based on a period of time during which energy from the main entity (114) is reduced. In an embodiment, different first computing devices (104) may apply differential pricing criteria such that different rates are charged based on a percentage (or other criteria) of the consumption of backup energy by the computing device relative to the total backup energy consumed within the entity.

[0081] In some embodiments, based on information corresponding to the one or more first computing devices (104) and the one or more parameters, the system (110) may intelligently predict energy consumption and / or optimal energy consumption at the one or more first computing devices (104). Based on such prediction, the system (110) may provide personalized energy management services to the one or more first computing devices (104) over the network (106). For example, the personalized energy management services may include one or more recommendations. In some embodiments, the one or more recommendations may correspond to, but are not limited to, consumption of renewable energy generated at the main entity (114), storage of surplus energy generated, and energy feedback to the main entity (114) based on dynamic energy pricing.

[0082] In an embodiment, data communication between the first computing device 104 and the system 110 and / or main entity 114 may be facilitated by enabling network slicing. For example, information of one or more first computing devices 104 may be conveyed to the system 110 via a Massive Machine Type Communication (mMTC) network slice. Those skilled in the art will appreciate that mMTC services are built to serve a large number of devices in a small area, and are expected to generate small amounts of data (on the order of tens of bytes per second) and tolerate high latency (up to 10 bytes round trip). Furthermore, the specifications require that little power be required to transmit and receive data, allowing for long battery life for devices.

[0083] As another example, a demand response request based on the current load of the main entity (114) can be sent through an Ultra-Reliable Low-Latency Communication (URLLC) network slice. Those skilled in the art will appreciate that URLLC uses 5G to provide secure communications with 1 millisecond (ms) latency and high reliability with low or even zero packet loss.

[0084] According to an embodiment of the present disclosure, the system (110) is configured to collect aggregate energy usage or consumption data from the first computing devices (104) within the entities (102) and monitor supply patterns coming from the main entity (114), and then perform one or more of the following functions (among others): (i) minor demand response control for the first computing devices (104) within the entities (102) to address power outage or brownout conditions (either by the entities (102) themselves or as directed by the main entity (114)), (ii) estimate backup power / energy requirements and consumption, as well as costs to charge each individual first computing device (104) for the consumed backup power / energy, etc.

[0085] Thus, the present disclosure of a smart energy management system optimizes energy consumption in a network architecture (100). Additionally, the present disclosure provides differential pricing of backup energy to one or more first computing devices (104). Additionally, the system (110) uses energy consumption patterns to more intelligently develop demand response rules that address various concerns, such as, but not limited to, a shortage of energy supply, or a higher level of demand response based on a higher than normal increase in demand. Additionally, according to various principles of the present disclosure, energy usage can be optimized to provide power in the most efficient manner, where efficiency is defined by the amount of energy used, pricing, or a balance of the two. In the present disclosure, various algorithms can be used by the system (110) to increase energy and cost savings by integrating supply side options with energy consumption choices.

[0086] Although Figure 1 illustrates exemplary components of network architecture (100), in other embodiments, network architecture (100) may include fewer components, different components, components in a different arrangement, or additional functional components than those illustrated in Figure 1. Additionally or alternatively, one or more components of network architecture (100) may perform functions described as being performed by one or more other components of network architecture (100).

[0087] Referring now to Figure 2, an exemplary network architecture (200) for managing energy consumption associated with one or more computing device(s) (202) is shown, according to an embodiment of the present disclosure. Those skilled in the art will appreciate that the computing device(s) (202) may be similar in their functionality to the first computing device(s) (104) of Figure 1. As such, it may not be described in detail again for the sake of brevity.

[0088] 2, the network architecture (200) includes one or more sensors (204) for collecting and / or recording information associated with the operation of the computing device(s) (202). For example, the information may include, but is not limited to, the current energy usage of the computing device(s) (202), energy consumption patterns over a period of time, connectivity of the computing device(s) (202) to one or more energy sources, etc. In an embodiment, the one or more sensors (204) may include, but is not limited to, smart meters, etc.

[0089] In one embodiment, information associated with the operation of the computing device(s) (202) is transmitted to a system (206), such as an energy management system, i.e., data collection (208). In particular, actual energy consumption is monitored and recorded using information provided to the system (206) by one or more sensors (204). It will be appreciated that maintaining historical usage data also allows an operator of the system (206) to review actual energy consumption over past periods, e.g., for comparison purposes. For this reason, historical usage data may be retained for relatively long periods of time, even for years.

[0090] Those skilled in the art will appreciate that the system (206) may be similar in functionality to the system (110) of FIG. 1 . Therefore, it may not be described again in detail for the sake of brevity. In an embodiment, the system (206) provides smart energy management services to the computing device(s) (202) in the network architecture (200) via a network, such as the network (106) of FIG. 1 . Alternatively or additionally, the system (206) provides smart energy management services to the computing device(s) (202) by network slicing, i.e., by slicing the network (106) into one or more appropriate slices.

[0091] In an embodiment, the system (206) performs data analysis (210) on the collected information. By way of example and not limitation, the system (206) may identify energy consumption patterns of the computing device(s) (202) over a period of time. The data analysis (210) may be performed to optimize energy consumption usage across the various computing devices (202) associated with one or more entities in the network (106).

[0092] Based on the data analysis (210), the system (206) performs a data prediction (212), i.e., the system (210) predicts future or expected energy consumption by the computing device(s) (202) for a given period (e.g., a specific time of day, day, week, and / or month). In an embodiment, the system (206) may include an AI trigger engine that utilizes one or more machine learning models to perform such data prediction. In an embodiment, the system (206) performs the data prediction based on the collected and analyzed information, i.e., based on the actual energy consumption over the relevant period. From the perspective of managing energy consumption, the prediction of expected energy usage is important because it allows the system (206) to identify deviations from the expected amount of energy consumption, which can then be used as a basis for generating relevant warnings, alerts, and / or apply controls to one or more computing device(s) (202) to optimize energy consumption.

[0093] Moreover, all data from the above steps, i.e., data collection (208), data analysis (210), and data prediction (212), are further transmitted for data storage (214) in a database (216). In an embodiment, the database (216) may be housed within the system (206). Alternatively or additionally, the database (216) may be similar to a server, such as the server (112) of FIG. 1. For example, the server (112) may be a centralized server, a cloud computing system, or any other networked system.

[0094] In some embodiments, database 216 may also be used for temporary and long-term storage of data associated with managing energy consumption within network architecture 200. This may include projected energy consumption data, actual recorded energy consumption data of computing device(s) 202, etc. Additionally, database 216 may include data parameter(s) corresponding to one or more energy sources. In some embodiments, data parameter(s) may include, but are not limited to, renewable energy produced by one or more energy sources, battery (charging) storage capacity, differential pricing, etc.

[0095] In some embodiments, the system (206) may make a data prediction (212) based on the collected and analyzed information and / or the data parameter(s) extracted from the database (216). Further, based on the analysis and prediction, the system (206) determines one or more recommendations for execution on the computing device(s) (202). By way of example and not limitation, a computing device (202), such as an electric vehicle, may be charged at night based on lower dynamic energy usage and pricing at night. However, if local solar panels are generating energy during the day, the recommendation to charge the electric vehicle at night may be invalidated. In general, charging of the electric vehicle may be shared proportionately between daytime and nighttime based on information associated with the operation of the electric vehicle and one or more data parameters corresponding to each source of energy, for example, based on the amount of renewable energy generated, smart grid energy pricing, and / or battery (charging) storage capacity. It will be appreciated that this is an exemplary embodiment and that other similar exemplary embodiments may exist in accordance with the present disclosure.

[0096] It should be appreciated that all of the individual entities, such as entity (102) in FIG. 1, may have different energy usage characteristics even if they are of the same general type and location. For example, even two identical entities with the same number of users associated with similar computing devices and located adjacent to each other will be different because different users will have different computing device usage patterns. According to an embodiment of the present disclosure, a key feature of the system (206) is its ability to recognize its operating context, i.e., the specific characteristics of the computing device(s) (202), in order to provide important management and control information. For example, it will be appreciated that the pattern of energy consumption will be quite different from a computing device (e.g., first computing device (104-1) in FIG. 1) associated with a first entity (e.g., entity (102-1)). This pattern is in contrast to another similar computing device (e.g., first computing device (104-2) in FIG. 1) associated with a second entity (e.g., entity (102-2) in FIG. 1).

[0097] Although Figure 2 illustrates example components / aspects of network architecture (200), in other embodiments, network architecture (200) may include fewer components, different components, components in a different arrangement, or additional functional components than those illustrated in Figure 2. Additionally or alternatively, one or more components of network architecture (200) may perform functions described as being performed by one or more other components of network architecture (200).

[0098] FIG. 3 illustrates an example network architecture (300) in which or with which embodiments of the present disclosure may be implemented.

[0099] As shown, the network architecture (300) may include one or more sensors, such as, but not limited to, one or more smart meters (304-1, 304-2, 304-3, 304-4). One skilled in the art will appreciate that one or more smart meters may be individually referred to as a smart meter (304) and collectively referred to as smart meters (304). Although four smart meters (304) are shown in FIG. 3, any number of smart meters (304) may be included in the network architecture (300) without departing from the scope of the ongoing description.

[0100] In an embodiment, the smart meter (304) has the capability of at least collecting and / or recording time-dependent readings of energy consumption of one or more computing devices, such as the computing device (104) of FIG. 1 or the computing device(s) (202) of FIG. 2, but the smart meter (304) may also have more advanced functionality operable under the control of the smart energy management system (302). It should be understood that the smart energy management system (302), which is interchangeably referred to as the system (302) throughout this disclosure, may be similar in functionality to the system (110) of FIG. 1 and the system (206) of FIG. 2, and thus may not be described in detail again for the sake of brevity. Furthermore, in an embodiment, the smart meter (304) may generate information corresponding to the operation of the computing device. In an exemplary embodiment, the smart meter (304) may collect information, such as, but not limited to, energy consumption at the computing device over a period of time. In an embodiment, the smart meter (304) may be programmable and controllable to remotely detect, monitor, calculate, and control the energy consumption of the computing device.

[0101] In some embodiments, the smart meter (304) may transmit the collected information to the system (302) over the network (306) for further analysis. In addition to the energy usage information, the smart meter (304) may provide additional information about the entity (e.g., entity (102) of FIG. 1) or facility associated with the computing device, such as one or more of light, temperature, and inactivity levels of the computing device within the entity. This additional data may also be transmitted to the system (302), which may be used alone or in combination with other data to provide smart energy management services.

[0102] In some embodiments, the network 306 may include at least one of a 5G network, etc. The network 306 enables the smart meter 304 to communicate with other components and / or systems 302 in the network architecture 300. The network 306 may include a wireless card or some other transceiver connection to facilitate this communication. In other embodiments, the network 306 may be implemented as or include any of a variety of different communication technologies, such as a WAN, a LAN, a wireless network, a mobile network, a VPN, the Internet, a PSTN, etc.

[0103] Referring to FIG. 3, the system (302) provides smart energy management services to different users associated with each computing device. In an embodiment, the system (302) collects information corresponding to the operation of the computing device from the smart meters (304), extracts a set of parameters from a database / server (not shown in FIG. 3) corresponding to one or more energy sources, and provides the smart energy management services to the computing device via the smart meters (304). In an embodiment, the system may predict a cost of optimizing energy consumption by the computing device associated with each of the smart meters (304), and based on such prediction, the system (302) may provide one or more recommendations. In an example, the one or more recommendations may correspond to instructing the computing device to consume dynamic renewable energy generated by one or more energy sources for a predetermined period of time, to feed back surplus energy to the system, etc.

[0104] In another embodiment, the system (302) may provide smart energy management services to the smart meter (304) and thus to the computing device through network slicing. Those skilled in the art will appreciate that network slicing is a key feature of 5G. For example, 5G network operators may slice a single network or combine the capacity of multiple networks to slice the pooled resources. In an embodiment, information of one or more computing devices may be conveyed from the smart meter (304) to the system (302) via an mMTC network slice. Those skilled in the art will appreciate that the mMTC service is built to serve a large number of devices in a small area, and it is expected that the mMTC service will generate small amounts of data (on the order of tens of bytes per second) and can tolerate high latency (up to 10 bytes round trip). Furthermore, the specifications require that little power be required to transmit and receive data, allowing for long battery life for devices.

[0105] As another example, a demand response request based on the current load of an energy source can be transmitted through a URLLC network slice. Those skilled in the art will appreciate that URLLC uses 5G to provide secure communications with 1 millisecond (ms) latency and high reliability with low or even zero packet loss.

[0106] As another example, if an operator of the system (302) wishes to interact directly with users of a computing device(s), an enhanced mobile broadband (eMBB) network slice can be required in the 5G network (306) (e.g., for video conferencing) combined with a URLLC network slice (e.g., for text data).Those skilled in the art will appreciate that eMBB provides mobile data access in one of three ways: for a dense collection of users, for highly mobile users, and for users spread across a wide area.

[0107] In an embodiment, a system (302) that relies on a 5G network (306) may contract (i.e., smart contract) for network slicing in accordance with an embodiment of the present disclosure. Alternatively or additionally, smart contract microservices may be executed and corresponding transactions related to energy consumption and / or management may be recorded on a blockchain ledger, providing greater trust and reliability to such energy management systems. The convergence of blockchain technology with 5G, IoT, and cloud services will facilitate many new services that were not possible before. The convergence of these unique technologies will facilitate a neutral host environment and network slicing.

[0108] Because blockchain smart contracts are immutable, they can be used to more effectively distribute and validate contracts that may also include software code and / or configurations of computing devices (i.e., IoT devices). Blockchain provides a shared, distributed ledger that provides a history or record of all previous actions that are immutable due to the unique cryptographic security that blockchain uses. Blockchain also enables smart contracts to lead to machine-to-machine communication to distribute contracts. In some embodiments, smart contracts can also be used to provide automated configurations where the 5G network (306) dynamically changes in near real-time to meet specific contractual requirements.

[0109] Thus, the smart energy management system (302) performs various functions such as, but not limited to, energy analysis, energy consumption monitoring, ledger management, and demand forecasting.

[0110] Although Figure 3 illustrates example components / aspects of network architecture (300), in other embodiments, network architecture (300) may include fewer components, different components, components in a different arrangement, or additional functional components than those illustrated in Figure 3. Additionally or alternatively, one or more components of network architecture (300) may perform functions described as being performed by one or more other components of network architecture (300).

[0111] 4 illustrates an exemplary network architecture (400) in or with which embodiments of the present disclosure may be implemented. For example, a smart energy management system (110, 206, 302 in FIGS. 1-3) may be implemented as a computer system (400). Alternatively or additionally, a computing device(s) (104, 202 in FIGS. 1-2) may be implemented as a computer system (400).

[0112] As shown in FIG. 4, the computer system (400) may include an external storage device (410), a bus (420), a main memory (430), a read-only memory (440), a mass storage device (450), a communication port(s) (460), and a processor (470). One skilled in the art will recognize that the computer system (400) may include more than one processor and communication port. The communication port(s) (460) may be any of an RS-232 port for use with a modem-based dial-up connection, a 10 / 100 Ethernet port, a 1 Gigabit port or a 10 Gigabit port using copper or fiber, a serial port, a parallel port, or any other existing or next generation port. The communication port(s) (460) may be chosen depending on the network, such as a local area network (LAN), a wide area network (WAN), or any network to which the computer system (400) is connected. The main memory (430) may be a random access memory (RAM) or any other dynamic storage device commonly known in the art. The read-only memory (440) may be any static storage device(s) including, but not limited to, programmable read-only memory (PROM) chips for storing static information, such as boot-up or basic input / output system (BIOS) instructions for the processor (470). The mass storage (450) may be any current or next generation mass storage solution that may be used to store information and / or instructions.

[0113] The bus (420) communicatively couples the processor(s) (470) to other memory, storage, and communication blocks and may be, for example, a Peripheral Component Interconnect (PCI) / PCI Expansion (PCI-X) bus, a Small Computer System Interface (SCSI), a Universal Serial Bus (USB), etc., for connecting expansion cards, drives, and other subsystems, and other buses such as a Front Side Bus (FSB) that connects the processor (470) to the computer system (400).

[0114] Optionally, operator and management interfaces, such as a display, keyboard, and cursor control devices, may also be coupled to bus 420 to support direct operator interaction with computer system 400. Other operator and management interfaces may be provided via a network connection connected via communications port(s) 460. In no way is the foregoing exemplary computer system 400 intended to limit the scope of the present disclosure.

[0115] Thus, the present disclosure provides a unique and effective solution for monitoring energy consumption and providing personalized smart energy management services to users or businesses. The disclosed system provides a combination of smart sensor monitoring, knowledge of availability of different types of 5G network slices based on time criticality of services or network performance requirements, availability of renewable energy, availability of stored energy, and / or dynamic grid pricing based on available power of cloud / edge resources for machine learning and prediction. Additionally, the disclosed system may also utilize a blockchain-based platform to execute smart contracts and provide such services.

[0116] Although considerable emphasis has been placed herein on the preferred embodiment, it is recognized that many embodiments may be made and that many changes may be made in the preferred embodiment without departing from the principles of the invention. Since these and other changes in the preferred embodiment of the invention will be apparent to those skilled in the art from the disclosure herein, it is to be clearly understood that the foregoing description is to be taken merely as illustrative and not limiting of the invention.

[0117] Advantages of the present disclosure The present disclosure provides systems and methods for facilitating smart energy management in a network.

[0118] The present disclosure predicts and provides personalized smart energy management services to users or businesses.

[0119] The present disclosure facilitates network slicing based on knowledge of service time-criticality or network performance requirements, renewable energy availability, stored energy availability, and dynamic grid pricing.

[0120] The present disclosure provides a blockchain-based platform for executing smart contracts to provide smart energy management services.

Claims

1. A system (110) for facilitating energy management services, comprising: one or more processors (470); and a memory operably coupled to the one or more processors (470), the memory including processor-executable instructions that, when executed, cause the one or more processors (470) to: recording information corresponding to operation of one or more computing devices (104) in the network (106); extracting one or more parameters corresponding to one or more energy sources in the network (106) based on the recorded information; providing a personalized energy management service to each of the one or more computing devices (104) over at least one slice of the network (106) based on the extracted one or more parameters and recorded information of the one or more computing devices (104), the personalized energy management service including one or more recommendations to be executed on the one or more computing devices (104); The system (110) performs the above.

2. 2. The system of claim 1, wherein the information corresponding to the operation of the one or more computing devices includes at least one of energy usage by the one or more computing devices over a period of time and connectivity of the one or more computing devices to the one or more energy sources.

3. The system (110) of claim 1, wherein the information corresponding to the operation of the one or more computing devices (104) is collected by one or more sensors (204) associated with the one or more computing devices (104) in the network (106).

4. 2. The system (110) of claim 1, wherein the one or more parameters corresponding to the one or more energy sources comprise at least one of an amount of renewable energy generated by the one or more energy sources, a battery storage capacity, and dynamic energy pricing.

5. 5. The system (110) of claim 4, wherein the one or more recommendations correspond to at least one of: consumption of the renewable energy generated by the one or more energy sources; and feedback of excess energy to the one or more energy sources based on the dynamic energy pricing.

6. 2. The system (110) of claim 1, wherein the memory includes processor-executable instructions that, when instructed, cause the one or more processors (470) to provide the one or more recommendations by being configured to predict expected energy consumption by the one or more computing devices (104) for a predetermined time period in the future based on the recorded information and the extracted one or more parameters.

7. The system (110) of claim 1, wherein the network (106) includes the at least one slice for facilitating communication between the one or more computing devices (104) and the system (110).

8. The system (110) of claim 1 , wherein the information corresponding to the operation of the one or more computing devices (104) is collected from one or more sensors (204) through a massive machine type communication (mMTC) network slice.

9. The system (110) of claim 1, wherein the system (110) executes a smart contract to provide the personalized energy management service to each of the one or more computing devices (104) over the at least one slice of the network.

10. The system (110) of claim 9, wherein the system (110) is implemented as a blockchain-based platform that executes the smart contracts.

11. 11. The system of claim 10, wherein the system is implemented in a hierarchical network, the hierarchical network including a layer 2 network in which the smart contracts are layered on top of a layer 1 network of blockchain nodes, and aggregated data is submitted from the layer 2 network to the layer 1 network in the hierarchical network.

12. 1. A method of providing energy management services, comprising: recording, by the one or more processors (470), information corresponding to operation of one or more computing devices (104) in the network (106); extracting, by the one or more processors (470), one or more parameters corresponding to one or more energy sources in the network (106) based on the recorded information; providing, by the one or more processors (470), a personalized energy management service to each of the one or more computing devices (104) over at least one slice of the network (106) based on the extracted one or more parameters and recorded information of the one or more computing devices (104), the personalized energy management service including one or more recommendations to be executed on the one or more computing devices (104); A method for making something happen.

13. 13. The method of claim 12, wherein the information corresponding to the operation of the one or more computing devices (104) includes at least one of energy usage by the one or more computing devices (104) over a period of time and connectivity of the one or more computing devices (104) to the one or more energy sources.

14. 13. The method of claim 12, wherein the information corresponding to the operation of the one or more computing devices (104) is recorded by one or more sensors (204) associated with the one or more computing devices (104) in the network (106).

15. 13. The method of claim 12, wherein the one or more parameters corresponding to the one or more energy sources include at least one of an amount of renewable energy generated by the one or more energy sources, a battery (charging) storage capacity, and dynamic energy pricing.

16. 16. The method of claim 15, wherein the one or more recommendations correspond to at least one of: consumption of the renewable energy generated by the one or more energy sources; and feedback of excess energy to the one or more energy sources based on the dynamic energy pricing.

17. 13. The method of claim 12, comprising predicting, by the one or more processors (470), expected energy consumption by the one or more computing devices (104) for a predetermined period of time based on the recorded information and the extracted one or more parameters.

18. 13. The method of claim 12, wherein the information corresponding to the operation of the one or more computing devices (104) is collected from one or more sensors (204) through a massive machine type communication (mMTC) network slice.

19. 13. The method of claim 12, comprising executing, by the one or more processors (470), a smart contract for providing the personalized energy management service to each of the one or more computing devices (104) over the at least one slice of the network.

20. 20. The method of claim 19, comprising implementing, by the one or more processors (470), a blockchain-based platform that executes the smart contract.

21. A user device (104), one or more processors; and a memory operably coupled to the one or more processors, the memory including processor-executable instructions that, when executed, cause the one or more processors to: transmitting information corresponding to the operation of the user equipment (104) to a system (110) of a network (106); receiving one or more recommendations from the system (110) via at least one slice of the network (106); executing the one or more recommendations for a predetermined period of time, the one or more recommendations being based on at least the information corresponding to the operation of the user equipment (104) and one or more parameters corresponding to one or more energy sources of the network (106); A user device (104) causes the

22. 22. The user equipment (104) of claim 21, wherein the information corresponding to the operation of the user equipment (104) is recorded by one or more sensors (204) associated with the user equipment (104) in the network (106).

23. 22. The user equipment (104) of claim 21, wherein the one or more parameters corresponding to the one or more energy sources include at least one of an amount of renewable energy generated by the one or more energy sources, a battery (charging) storage capacity, and dynamic energy pricing.

24. 24. The user equipment (104) of claim 23, wherein the one or more recommendations correspond to at least one of: consumption of the renewable energy generated by the one or more energy sources; and feedback of excess energy to the one or more energy sources based on the dynamic energy pricing.

25. 22. The user equipment (104) of claim 21, wherein the memory includes processor-executable instructions that, when executed, cause the one or more processors to receive the one or more recommendations from the system (110) based on a smart contract executed on the system (110).

26. 22. The user equipment (104) of claim 21, wherein the memory includes processor-executable instructions that, when executed, cause the one or more processors to receive the one or more recommendations from the system (110) implemented as a blockchain-based platform.

27. A non-transitory computer readable medium containing processor-executable instructions, the non-transitory computer readable medium comprising: recording information corresponding to operation of one or more computing devices (104) in the network (106); extracting one or more parameters corresponding to one or more energy sources in the network (106) based on the recorded information; providing a personalized energy management service to each of the one or more computing devices (104) over at least one slice of the network (106) based on the extracted one or more parameters and recorded information of the one or more computing devices (104), the personalized energy management service including one or more recommendations to be executed on the one or more computing devices (104); A non-transitory computer-readable medium for causing

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