Smart energy operation cost control method and system based on big data

Abstract

The embodiment of the invention provides a smart energy operation cost control method and system based on big data. The method comprises the following steps: acquiring energy consumption data of each energy device in different time periods through a data acquisition module; cleaning and preprocessing the energy consumption data, and constructing an energy consumption model; identifying an abnormal energy consumption mode based on the historical energy consumption data; and optimizing an energy distribution strategy according to an identification result to reduce resource waste. Through the processing scheme disclosed by the invention, the abnormal energy consumption mode can be identified, so that the problem of resource waste is solved.

Description

Technical Field

[0001] The present invention relates to the field of new energy technologies, and in particular to a smart energy operation cost control method and system based on big data. Background Art

[0002] Energy operation cost control is a method to optimize the energy consumption management of energy equipment and thus reduce energy operation costs. However, there is a problem with this method in practical application, that is, how to accurately identify abnormal energy consumption patterns. If these abnormal patterns cannot be effectively identified, it is impossible to optimize the energy allocation strategy in a targeted manner, and thus it is impossible to effectively solve the problem of resource waste. Summary of the invention

[0003] In view of this, the embodiments of the present disclosure provide a smart energy operation cost control method and system based on big data, which at least partially solve the problems existing in the prior art.

[0004] In a first aspect, a smart energy operation cost control method based on big data of the present application comprises:

[0005] Obtain energy consumption data of each energy device at different time periods through the data acquisition module;

[0006] Cleaning and preprocessing the energy consumption data, and building an energy consumption model;

[0007] Identify abnormal energy consumption patterns based on historical energy consumption data;

[0008] The energy allocation strategy is optimized based on the identification results to reduce resource waste.

[0009] In a specific embodiment, the identifying abnormal energy consumption pattern based on historical energy consumption data comprises the following steps:

[0010] Calculate the average energy consumption of each energy device at different time periods ,in Indicates the energy equipment number, Indicates the time period number;

[0011] Calculate the standard deviation of energy consumption of each energy device at different time periods ;

[0012] For each energy device and time period , determine its energy consumption value Does it satisfy the formula ,in is the preset threshold coefficient;

[0013] If the formula is satisfied , the energy consumption mode of the device during this period is marked as abnormal.

[0014] In a specific embodiment, optimizing the energy allocation strategy according to the identification result to reduce resource waste includes the following steps:

[0015] For energy devices marked as abnormal energy consumption patterns, calculate the proportion of their energy consumption during the abnormal period. ;

[0016] According to energy consumption , determine the energy allocation priority of the device ,The higher the priority is, the less amount of energy allocated;

[0017] For devices with a priority lower than a predefined threshold, calculate their energy consumption potential during non-abnormal periods ,in is the preset energy saving factor;

[0018] According to energy consumption potential and energy allocation priorities , adjust the energy allocation strategy to give priority to meeting the energy needs of high-priority devices.

[0019] In a specific embodiment, the energy consumption potential and energy allocation priorities ,Adjusting the energy allocation strategy includes the following steps:

[0020] Calculate the total budget for energy distribution ;

[0021] According to priority and energy consumption potential , allocating energy budgets to each device ;

[0022] For the allocated energy budget Less than the minimum operating requirement of the equipment Energy subsidies are provided for equipment with an amount of ;

[0023] Generate an energy allocation optimization plan based on the adjusted energy allocation strategy and energy subsidy situation.

[0024] In a specific embodiment, generating an energy allocation optimization plan according to the adjusted energy allocation strategy and energy subsidy situation includes the following steps:

[0025] For each energy device, calculate its expected energy consumption under the optimization solution ;

[0026] According to expected energy consumption and equipment operating efficiency , calculate its expected energy output under the optimization scheme ;

[0027] For expected energy output Lower than the minimum output requirement of the equipment Equipment maintenance or upgrade is required, and the maintenance or upgrade cost is ;

[0028] Generate detailed energy distribution optimization reports based on the expected energy consumption, expected energy output, and equipment maintenance or upgrade status of the optimization plan.

[0029] In a specific embodiment, optimizing the energy allocation strategy according to the identification result to reduce resource waste includes the following steps:

[0030] For energy devices marked as abnormal energy consumption patterns, calculate their energy consumption fluctuation rate during the abnormal period ;

[0031] According to the energy consumption fluctuation rate , determine the energy allocation adjustment range of the equipment , the larger the adjustment, the less energy is allocated;

[0032] For devices with adjustment ranges greater than a predetermined threshold, calculate their energy consumption optimization potential during non-abnormal periods ,in is the preset optimization coefficient;

[0033] Optimization potential according to energy consumption and energy allocation adjustments , adjust the energy allocation strategy to give priority to meeting the energy needs of equipment with small adjustment range.

[0034] In one embodiment, the energy consumption optimization potential and energy allocation adjustments ,Adjusting the energy allocation strategy includes the following steps:

[0035] Calculate the total optimal budget for energy distribution ;

[0036] According to the adjustment range and energy optimization potential , allocating optimized energy budget to each device ;

[0037] Optimized energy budget for allocation Less than the minimum optimization requirement of the device equipment, and provide energy optimization subsidies, the subsidy amount is ;

[0038] Generate an energy allocation optimization plan based on the adjusted energy allocation strategy and optimized energy subsidies.

[0039] In a specific embodiment, generating an energy allocation optimization plan according to the adjusted energy allocation strategy and the optimized energy subsidy situation includes the following steps:

[0040] For each energy device, calculate its expected energy consumption under the optimization solution ;

[0041] According to expected energy consumption and equipment operating efficiency , calculate its expected energy output under the optimization scheme ;

[0042] For expected energy output Lower than the minimum optimized output requirement of the equipment Equipment maintenance or upgrade is required, and the maintenance or upgrade cost is ;

[0043] Generate detailed energy distribution optimization reports based on the expected energy consumption, expected energy output, and equipment maintenance or upgrade status of the optimization plan.

[0044] In the second aspect, the present application provides a smart energy operation cost control system based on big data, including:

[0045] Data collection module, through which energy consumption data of various energy devices at different time periods are obtained;

[0046] A model building module, which cleans and preprocesses the energy consumption data and builds an energy consumption model;

[0047] Abnormal energy consumption pattern recognition module, which recognizes abnormal energy consumption patterns based on historical energy consumption data;

[0048] The energy allocation strategy optimization module optimizes the energy allocation strategy according to the identification results to reduce resource waste.

[0049] A smart energy operation cost control method based on big data in the embodiment of the present disclosure includes obtaining energy consumption data of various energy devices at different time periods through a data acquisition module; cleaning and preprocessing the energy consumption data and building an energy consumption model; identifying abnormal energy consumption patterns based on historical energy consumption data; and optimizing energy allocation strategies according to the identification results to reduce resource waste. Through the processing scheme of the present disclosure, abnormal energy consumption patterns can be identified, thereby solving the problem of resource waste. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0051] Figure 1 A flowchart of a smart energy operation cost control method based on big data provided in an embodiment of the present disclosure;

[0052] Figure 2 A flowchart of identifying abnormal energy consumption patterns based on historical energy consumption data provided by an embodiment of the present disclosure;

[0053] Figure 3 A flow chart of optimizing energy allocation strategy according to identification results to reduce resource waste provided in an embodiment of the present disclosure;

[0054] Figure 4 A schematic diagram of a process for adjusting an energy allocation strategy according to energy consumption potential and energy allocation priority provided in an embodiment of the present disclosure;

[0055] Figure 5 A flow chart for generating an energy allocation optimization solution according to the adjusted energy allocation strategy and energy subsidy situation provided in an embodiment of the present disclosure;

[0056] Figure 6 A flowchart of optimizing energy allocation strategy according to identification results to reduce resource waste provided by another embodiment of the present disclosure;

[0057] Figure 7 The energy consumption optimization potential and energy allocation adjustment range provided in the embodiments of the present disclosure, and the flow chart of adjusting the energy allocation strategy;

[0058] Figure 8 A flow chart for generating an energy allocation optimization solution according to an adjusted energy allocation strategy and optimized energy subsidy conditions provided in an embodiment of the present disclosure;

[0059] Fig. 9 A block diagram of a smart energy operation cost control system based on big data provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0060] The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0061] The following describes the embodiments of the present disclosure through specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all of the embodiments. The present disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present disclosure.

[0062] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein may be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present disclosure, it should be understood by those skilled in the art that an aspect described herein may be implemented independently of any other aspect, and two or more of these aspects may be combined in various ways. For example, any number of aspects described herein may be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein may be used to implement this device and / or practice this method.

[0063] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present disclosure. The drawings only show components related to the present disclosure rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0064] Additionally, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects described may be practiced without these specific details.

[0065] First, refer to Figure 1 , describes a smart energy operation cost control method based on big data in the present application, the method comprising:

[0066] S101: Obtain energy consumption data of each energy device at different time periods through a data acquisition module.

[0067] The data collection module usually includes sensors, smart meters and other equipment, which can monitor and record the energy consumption of energy equipment in real time. For example, in one embodiment, the energy management system of a factory collects the power consumption data of the production line every 15 minutes through the smart meters installed on each production line. These data include the energy consumption of the production line in different production tasks and different time periods, providing basic data support for subsequent data analysis and energy consumption management.

[0068] S102: Clean and preprocess the energy consumption data, and build an energy consumption model. Data cleaning refers to removing noise, outliers, duplicate data, etc. in the data to ensure the quality and accuracy of the data. Preprocessing includes operations such as data formatting, normalization, and feature extraction to facilitate subsequent analysis and modeling. For example, in a specific example, the energy consumption data collected by the energy management system may contain some abnormally high energy consumption records due to sensor failure, or duplicate data due to data transmission errors. The system will filter out these abnormal data by setting reasonable thresholds, and eliminate or correct them. At the same time, the data is normalized to convert energy consumption data of different dimensions into a unified numerical range for easy comparison and analysis.

[0069] S103: Construct an energy consumption model based on the cleaned and preprocessed data. The energy consumption model can be a regression model based on statistics or a prediction model based on machine learning. For example, a multivariate linear regression model can be used to take energy consumption data as the dependent variable and production tasks, equipment operating status, environmental factors, etc. as independent variables to establish a relationship model between energy consumption and these factors. Through model fitting and parameter estimation, the energy consumption of energy equipment under different production conditions and equipment states can be predicted.

[0070] S103: Identify abnormal energy consumption patterns based on historical energy consumption data. Abnormal energy consumption patterns refer to situations where the energy consumption of energy equipment is significantly higher than normal levels under certain conditions, or there are obvious differences from historical energy consumption data. Identifying abnormal energy consumption patterns is a key step in solving the problem of resource waste. Specifically, a variety of methods can be used to identify abnormal patterns. For example, an energy consumption threshold can be set. When the energy consumption of an energy device exceeds the threshold in a certain time period, it is considered that it has an abnormal energy consumption pattern. This threshold can be determined based on the average value of historical energy consumption data plus several times the standard deviation. In addition, cluster analysis, anomaly detection algorithms and other methods can be used to analyze historical energy consumption data to find abnormal patterns. For example, in an energy management system of a factory, historical energy consumption data is divided into two categories: normal energy consumption patterns and abnormal energy consumption patterns through cluster analysis. The data points in the normal energy consumption pattern show a certain regular distribution on the energy consumption-time plane, while the data points in the abnormal energy consumption pattern obviously deviate from this distribution. In this way, it is possible to accurately identify which time periods and which devices have abnormal energy consumption.

[0071] S104: Optimize the energy allocation strategy according to the identification results to reduce resource waste. Optimizing the energy allocation strategy means adjusting the operating parameters, production plan, equipment maintenance, etc. of the energy equipment according to the identified abnormal energy consumption mode to reduce energy consumption and improve energy utilization efficiency. For example, in a specific embodiment, when it is found that a production line has abnormally high energy consumption during nighttime production, the energy management system will analyze the reason, which may be due to fewer production tasks at night and low equipment operation efficiency, resulting in increased unit energy consumption. In view of this situation, the production plan can be adjusted to arrange some production tasks with lower energy consumption at night, or the equipment can be maintained and optimized to improve its nighttime operation efficiency. At the same time, by optimizing energy allocation, more energy can be allocated to equipment with lower energy consumption and higher production efficiency, reducing the energy supply to equipment with abnormal energy consumption, thereby reducing the overall energy operation cost. Through the above steps, a smart energy operation cost control method based on big data in this application can effectively identify abnormal energy consumption patterns, optimize energy allocation strategies according to the identification results, solve the problem of resource waste, improve energy utilization efficiency, and reduce energy operation costs.

[0072] Next, refer to Figure 2 , specifically describing the specific steps of the present application for identifying abnormal energy consumption patterns based on historical energy consumption data, such as Figure 2 As shown, it specifically includes:

[0073] S201: Calculate the average energy consumption of each energy device at different time periods ,in Indicates the energy equipment number, Indicates the time period number. Average energy consumption value It is obtained by averaging the energy consumption data of the equipment in a specific period of time, and it reflects the normal energy consumption level of the equipment in that period of time. For example, in one embodiment, the energy management system of a factory collects the power consumption data of each production line at different time periods of a day, and then calculates the average energy consumption value of each production line in each time period.

[0074] S202: Calculate the standard deviation of energy consumption of each energy device at different time periods The standard deviation is a statistic that measures the degree of fluctuation of energy consumption data. It can help identify abnormal fluctuations in energy consumption data. For example, if the standard deviation of energy consumption data of a production line in a certain period of time is large, it means that the energy consumption fluctuation in this period of time is large, and there may be abnormal conditions. Specifically, if the standard deviation is greater than or less than a predetermined threshold, it can be considered that the standard deviation is large.

[0075] S203: For each energy device and time period , determine its energy consumption value Does it satisfy the formula ,in is the preset threshold coefficient. Parameters The range is usually between 1 and 3, and the optimal value is 2. This is because according to statistical principles, when When the value is 2, the probability that the energy consumption value exceeds the average value plus 2 times the standard deviation is about 2.28%, which can effectively identify abnormal energy consumption. The meaning of this formula is that if the energy consumption value of a device in a certain period exceeds its average energy consumption value plus For example, in a specific example, if the energy consumption value of a production line in a certain period of time exceeds its average energy consumption value plus 2 times the standard deviation, the energy consumption pattern of the production line in this period of time will be marked as abnormal.

[0076] S204: If the formula is satisfied , the energy consumption pattern of the device during this period is marked as abnormal. This step is to mark the identified abnormal energy consumption pattern for subsequent targeted analysis and optimization. For example, in a factory’s energy management system, the above steps are used to identify that the energy consumption pattern of a production line is abnormal during nighttime production. The system will record this information and take it into account in subsequent energy allocation optimization. Through these steps, the present application can effectively identify abnormal energy consumption patterns of energy equipment and provide reliable data support for optimizing energy allocation strategies, thereby helping to reduce resource waste and improve energy efficiency.

[0077] Next, refer to Figure 3, and further describe the specific steps of optimizing the energy allocation strategy based on the identification results to reduce resource waste.

[0078] S301: For energy devices marked as abnormal energy consumption modes, calculate their energy consumption ratio during the abnormal period Energy consumption ratio Indicates the proportion of the energy consumption of the equipment in a certain abnormal period to its total energy consumption, which is used to evaluate the impact of the energy consumption in this period on the overall energy consumption. For example, in one embodiment, if the energy consumption of a production line in a certain abnormal period is 1000 kWh, and its total energy consumption in all periods is 5000 kWh, the energy consumption in this abnormal period accounts for 20%.

[0079] S302: Based on energy consumption ratio , determine the energy allocation priority of the device , the higher the priority, the less energy is allocated. The higher the priority, the greater the proportion of energy consumption of the device during abnormal periods, so the amount of energy allocated should be relatively small to encourage the device to improve energy efficiency during normal periods. For example, you can set When it is greater than a certain threshold, the priority of the device is reduced.

[0080] S303: For devices with a priority lower than a predetermined threshold, calculate their energy consumption potential during non-abnormal periods ,in It is the preset energy saving factor. The range of is usually between 0 and 0.3, and the optimal value is 0.1, which means that the energy consumption of the equipment can be reduced by 10% during non-abnormal periods. The meaning of this formula is that during non-abnormal periods, by optimizing the operating parameters or production plan of the equipment, the energy-saving potential of the equipment can be tapped. For example, in a specific example, if the energy consumption of a production line during non-abnormal periods is 800 kWh and the energy-saving coefficient is 0.1, its energy consumption potential is 720 kWh. Here, the priority is lower than the third threshold and can be considered to be low.

[0081] S304: Based on energy consumption potential and energy allocation priorities , adjust the energy allocation strategy to give priority to meeting the energy needs of high-priority equipment. This means that when allocating energy, priority will be given to equipment that has a small proportion of energy consumption during abnormal periods and a large potential for energy saving during non-abnormal periods, so as to achieve reasonable allocation and optimal utilization of energy. For example, in a factory's energy management system, for equipment with high priority, its energy supply will be increased during peak production periods, while for equipment with low priority, energy will be allocated during non-peak production periods, thereby achieving the purpose of reducing resource waste and improving energy utilization efficiency. Through these steps, the present application can effectively optimize the energy allocation strategy, solve the problem of resource waste, and reduce energy operating costs.

[0082] Next, refer to Figure 4 , further describing the energy consumption potential of this application and energy allocation priorities , specific steps to adjust the energy allocation strategy.

[0083] S401: Calculate the total budget for energy distribution Total Budget Refers to the total amount of energy that can be allocated to various energy devices within a certain period of time, and its range depends on the energy supply capacity and cost budget. For example, in a factory's energy management system, the total budget may be determined based on the power company's supply capacity and the enterprise's energy cost budget.

[0084] S402: Based on priority and energy consumption potential , allocating energy budgets to each device In this formula, the priority and energy consumption potential It is a key factor in allocating energy budget. The higher the priority, the greater the energy consumption potential, and the more energy budget is allocated to the device. This formula is designed to make energy more reasonably allocated to devices that can use energy more efficiently. For example, if a device has a high energy consumption potential during non-abnormal hours and a high priority, it will get more energy budget.

[0085] S403: For allocated energy budget Less than the minimum operating requirement of the equipment Energy subsidies are provided for equipment with an amount of . Minimum equipment operating requirements It refers to the minimum amount of energy required for the normal operation of the equipment. The subsidy mechanism ensures that the equipment can meet its basic operating needs and avoid production being affected by insufficient energy. For example, in a specific example, if the minimum operating requirement of a certain equipment is 100 kWh, and only 80 kWh is allocated according to the budget, it needs to be subsidized for 20 kWh.

[0086] S404: Generate an energy allocation optimization plan based on the adjusted energy allocation strategy and energy subsidy situation. The plan lists in detail the energy allocation amount and subsidy situation of each device, providing clear guidance for energy management. For example, in a factory's energy management system, the optimization plan will formulate a detailed energy allocation plan based on the energy budget and subsidy situation of each device to ensure the rational use of energy and effective control of costs. Through these steps, this application can effectively optimize the energy allocation strategy, improve energy utilization efficiency, and reduce energy operating costs.

[0087] Next, refer to Figure 5 , further describing the generation of energy allocation optimization scheme according to the adjusted energy allocation strategy and energy subsidy situation.

[0088] S501: For each energy device, calculate its expected energy consumption under the optimization scheme Expected energy consumption It refers to the predicted energy consumption of the equipment in each period under the new energy allocation strategy. is the energy budget allocated to the device, It is the energy consumption potential of the equipment during non-abnormal periods. This formula can be used to reasonably allocate the energy consumption of the equipment in different time periods.

[0089] S502: Based on expected energy consumption and equipment operating efficiency , calculate its expected energy output under the optimization scheme Operational efficiency It is the ability of a device to convert input energy into useful energy, usually expressed as a percentage. The meaning of this formula is the energy output that a device can produce under a given expected energy consumption. For example, if the operating efficiency of a device is 80% and the expected energy consumption is 200 kWh, then its expected energy output is 200×0.8=160 kWh. Here, the expected energy output is The evaluation can be based on the value generated, for example, 100 kWh of energy consumption can generate 160 kWh of electricity.

[0090] S503: For expected energy output Lower than the minimum output requirement of the equipment Equipment maintenance or upgrade is required, and the maintenance or upgrade cost is . Minimum equipment output requirements It refers to the minimum energy output required for the normal operation of the equipment. If the expected output is not up to standard, maintenance or upgrade is required to improve the performance of the equipment. For example, in a specific example, if the minimum output requirement of the equipment is 180 kWh and the expected energy output is 160 kWh, maintenance or upgrade is required, and the cost is 180-160=20 kWh of equivalent cost.

[0091] S504: Generate a detailed energy allocation optimization report based on the expected energy consumption, expected energy output, and equipment maintenance or upgrade of the optimization plan. The report will list in detail the expected energy consumption and output of each device, as well as the maintenance or upgrade measures required, to provide comprehensive data support for energy management and decision-making. For example, in a factory's energy management system, the optimization report will detail the energy consumption and output of each device, and make corresponding maintenance or upgrade recommendations to ensure efficient use of energy and stable operation of equipment. Through these steps, the present application can effectively generate an energy allocation optimization plan, improve energy utilization efficiency, and reduce energy operating costs.

[0092] Next, refer to Figure 6 , further describing the specific steps of the present application for optimizing the energy allocation strategy according to the identification results to reduce resource waste.

[0093] It should be noted that in this application, the following references Figure 6-Figure 8 The described scheme can exist alone or in combination with the above reference Figure 2-Figure 5 Described scheme combination.

[0094] S601: For energy devices marked as abnormal energy consumption modes, calculate their energy consumption fluctuation rate during abnormal periods Energy consumption fluctuation rate Indicates the relative deviation between the energy consumption of the device during the abnormal period and the average energy consumption, and is used to evaluate the abnormal degree of energy consumption. For example, in one embodiment, if the energy consumption of a device during an abnormal period is 150 kWh, and its average energy consumption is 100 kWh, the energy consumption fluctuation rate during this period is 50%.

[0095] S602: Based on energy consumption fluctuation rate , determine the energy allocation adjustment range of the equipment , the larger the adjustment, the less energy is allocated. A larger adjustment means greater fluctuations in the energy consumption of the device, so the amount of energy allocated should be relatively small to encourage the device to improve energy efficiency during normal hours. For example, you can set When it is greater than a certain threshold, the energy allocation adjustment of the device increases.

[0096] S603: For devices whose adjustment range is greater than a predetermined threshold, calculate their energy consumption optimization potential during non-abnormal periods ,in is the preset optimization coefficient. The range of is usually between 0 and 0.3, and the optimal value can be set to 0.1, which means that the energy consumption of the equipment can be reduced by 10% during non-abnormal periods. The meaning of this formula is that during non-abnormal periods, by optimizing the operating parameters or production plans of the equipment, the energy-saving potential of the equipment can be tapped. For example, in a specific example, if the energy consumption of a device during non-abnormal periods is 200 kWh and the optimization coefficient is 0.1, its energy consumption optimization potential is 200×(1−0.1)=180 kWh. Here, the adjustment amplitude is greater than the second threshold, and it can be considered that the adjustment amplitude is large.

[0097] S604: Optimization potential based on energy consumption and energy allocation adjustments , adjust the energy allocation strategy to give priority to meeting the energy needs of equipment with a small adjustment range. This means that when allocating energy, priority will be given to equipment with small energy consumption fluctuations and large energy consumption optimization potential, so as to achieve reasonable allocation and optimal utilization of energy. For example, in a factory's energy management system, for equipment with a small adjustment range, its energy supply will be increased during peak production periods, while for equipment with a large adjustment range, energy will be allocated during non-peak production periods, thereby achieving the purpose of reducing resource waste and improving energy utilization efficiency. Through these steps, the present application can effectively optimize the energy allocation strategy, solve the problem of resource waste, and reduce energy operating costs.

[0098] Next, refer to Figure 7 , further describing the potential of this application based on energy consumption optimization and energy allocation adjustments , specific steps to adjust the energy allocation strategy.

[0099] S701: Calculate the total optimal budget for energy distribution Total Optimization Budget Refers to the total amount of energy that can be optimally allocated to each energy device within a certain period of time, and its range depends on the energy supply capacity and cost budget. For example, in a factory's energy management system, the total optimization budget may be determined based on the power supply capacity of the power company and the energy cost budget of the enterprise.

[0100] S702: According to the adjustment range and energy optimization potential , allocating optimized energy budget to each device In this formula, the adjustment range and energy optimization potential It is a key factor in allocating optimized energy budget. The smaller the adjustment, the greater the potential for energy optimization, and the more optimized energy budget is allocated to the equipment. This formula is designed to make energy more reasonably allocated to those equipment that can use energy more efficiently.

[0101] S703: Optimizing energy budget for allocation Less than the minimum optimization requirement of the device equipment, and provide energy optimization subsidies, the subsidy amount is . Minimum equipment optimization requirements It refers to the minimum amount of energy required for the normal operation of the equipment under optimized conditions. The subsidy mechanism ensures that the equipment can meet its basic operating needs and avoid production being affected by insufficient energy. For example, in a specific example, if the minimum optimization requirement of a certain equipment is 150 kWh, and only 120 kWh is obtained according to the budget allocation, it needs to be subsidized with an optimized energy of 30 kWh.

[0102] S704: Generate an energy allocation optimization plan based on the adjusted energy allocation strategy and optimized energy subsidies. The plan lists in detail the optimized energy allocation amount and subsidy situation for each device, providing clear guidance for energy management. For example, in a factory's energy management system, the optimization plan will formulate a detailed energy allocation plan based on the optimized energy budget and subsidy situation of each device to ensure the rational use of energy and effective control of costs. Through these steps, this application can effectively optimize the energy allocation strategy, improve energy utilization efficiency, and reduce energy operating costs.

[0103] Next, refer to Figure 8 , further describing the steps of generating an energy allocation optimization plan based on the adjusted energy allocation strategy and optimized energy subsidy situation in this application.

[0104] S801: For each energy device, calculate its expected energy consumption under the optimization scheme Expected energy consumption It refers to the predicted energy consumption of the equipment in each period under the new optimized energy allocation strategy. is the optimized energy budget allocated to the device, It is the energy consumption optimization potential of the equipment during non-abnormal periods. Through this formula, the energy consumption of the equipment in different time periods can be reasonably allocated to ensure the effective use of energy.

[0105] S802: Based on expected energy consumption and equipment operating efficiency , calculate its expected energy output under the optimization scheme Operational efficiency It is the ability of a device to convert input energy into useful energy under optimized conditions, usually expressed as a percentage. The meaning of this formula is the energy output that a device can generate under a given expected energy consumption, where similar to the above description, the energy output can be evaluated by the value generated, for example, if consuming 100 kWh of electricity can generate the value corresponding to 150 kWh of electricity, then the operating efficiency of the device can be 150%. For example, in one embodiment, if the operating efficiency of the device is 85% and the expected energy consumption is 200 kWh of electricity, then its expected energy output is 200×0.85=170 kWh of electricity.

[0106] S803: For expected energy output Lower than the minimum optimized output requirement of the equipment Equipment maintenance or upgrade is required, and the maintenance or upgrade cost is . Minimum equipment optimization output requirements It refers to the minimum energy output required for the normal operation of the equipment under optimized conditions. The minimum energy output is calculated based on the overall energy output of each device included in the entire system. For example, the energy output of some devices is less than 1, while the energy output of other devices is greater than 1, and the overall satisfaction is greater than 120%. For one of the devices, if the expected output does not meet the target, it is necessary to improve the performance of the device through maintenance or upgrade. For example, in a specific example, if the minimum optimized output requirement of the device is 180 kWh, and the expected energy output is 170 kWh, maintenance or upgrade is required, and the cost is 180-170=10 kWh of equivalent cost.

[0107] S804: Generate a detailed energy allocation optimization report based on the expected energy consumption, expected energy output, and equipment maintenance or upgrade of the optimization plan. The report will list in detail the expected energy consumption and output of each device, as well as the maintenance or upgrade measures required, to provide comprehensive data support for energy management and decision-making. For example, in a factory's energy management system, the optimization report will detail the energy consumption and output of each device, and make corresponding maintenance or upgrade recommendations to ensure efficient use of energy and stable operation of equipment. Through these steps, this application can effectively generate energy allocation optimization plans, improve energy utilization efficiency, and reduce energy operating costs.

[0108] The above has described the smart energy operation cost control method based on big data of the present application with reference to the accompanying drawings. Fig. 9 , describing the smart energy operation cost control system 900 based on big data of the present application, which specifically includes:

[0109] Data collection module 901, which obtains energy consumption data of each energy device at different time periods through the data collection module;

[0110] A model building module 902 cleans and preprocesses the energy consumption data and builds an energy consumption model;

[0111] An abnormal energy consumption pattern recognition module 903 is used to recognize abnormal energy consumption patterns based on historical energy consumption data;

[0112] The energy allocation strategy optimization module 904 optimizes the energy allocation strategy according to the identification result to reduce resource waste.

[0113] The functions of each module included in the smart energy operation cost control system 900 based on big data of this application have been referred to above. Figure 1-Figure 8 The description has been given and will not be repeated here.

[0114] The above is only a specific implementation of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present disclosure should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A smart energy operation cost control method based on big data, characterized in that: include: Obtain energy consumption data of each energy device at different time periods through the data acquisition module; Cleaning and preprocessing the energy consumption data, and building an energy consumption model; Identify abnormal energy consumption patterns based on historical energy consumption data; The energy allocation strategy is optimized based on the identification results to reduce resource waste.

2. The smart energy operation cost control method based on big data according to claim 1 is characterized in that: The method of identifying abnormal energy consumption patterns based on historical energy consumption data comprises the following steps: Calculate the average energy consumption of each energy device at different time periods ,in Indicates the energy equipment number, Indicates the time period number; Calculate the standard deviation of energy consumption of each energy device at different time periods ; For each energy device and time period , determine its energy consumption value Does it satisfy the formula ,in is the preset threshold coefficient; If the formula is satisfied , the energy consumption mode of the device during this period is marked as abnormal.

3. The smart energy operation cost control method based on big data according to claim 2 is characterized in that: Optimizing the energy allocation strategy according to the identification results to reduce resource waste includes the following steps: For energy devices marked as abnormal energy consumption patterns, calculate their energy consumption ratio during the abnormal period. ; According to energy consumption , determine the energy allocation priority of the device ,The higher the priority is, the less amount of energy allocated; For devices with a priority lower than a predefined threshold, calculate their energy consumption potential during non-abnormal periods ,in is the preset energy saving factor; According to energy consumption potential and energy allocation priorities , adjust the energy allocation strategy to give priority to meeting the energy needs of high-priority devices.

4. The smart energy operation cost control method based on big data according to claim 3 is characterized in that: According to the energy consumption potential and energy allocation priorities ,Adjusting the energy allocation strategy includes the following steps: Calculate the total budget for energy distribution ; According to priority and energy consumption potential , allocating energy budgets to each device ; For the allocated energy budget Less than the minimum operating requirement of the equipment Equipment with energy subsidies of ; Generate an energy allocation optimization plan based on the adjusted energy allocation strategy and energy subsidy situation.

5. The smart energy operation cost control method based on big data according to claim 4 is characterized in that: The generating of the energy allocation optimization plan according to the adjusted energy allocation strategy and energy subsidy situation comprises the following steps: For each energy device, calculate its expected energy consumption under the optimization solution ; According to expected energy consumption and equipment operating efficiency , calculate its expected energy output under the optimization scheme ; For expected energy output Lower than the minimum output requirement of the equipment Equipment maintenance or upgrade is required, and the maintenance or upgrade cost is ; Generate detailed energy distribution optimization reports based on the expected energy consumption, expected energy output, and equipment maintenance or upgrade status of the optimization plan.

6. The smart energy operation cost control method based on big data according to claim 2 is characterized in that: Optimizing the energy allocation strategy according to the identification results to reduce resource waste includes the following steps: For energy devices marked as abnormal energy consumption patterns, calculate their energy consumption fluctuation rate during the abnormal period ; According to the energy consumption fluctuation rate , determine the energy allocation adjustment range of the equipment , the larger the adjustment, the less energy is allocated; For devices with adjustment ranges greater than a predetermined threshold, calculate their energy consumption optimization potential during non-abnormal periods ,in is the preset optimization coefficient; Optimization potential according to energy consumption and energy allocation adjustments , adjust the energy allocation strategy to give priority to meeting the energy needs of equipment with small adjustment range.

7. The method for controlling smart energy operation costs based on big data according to claim 6 is characterized in that: Optimization potential according to energy consumption and energy allocation adjustments ,Adjusting the energy allocation strategy includes the following steps: Calculate the total optimal budget for energy distribution ; According to the adjustment range and energy optimization potential , allocating optimized energy budget to each device ; Optimized energy budget for allocation Less than the minimum optimization requirement of the device equipment, and provide energy optimization subsidies, the subsidy amount is ; Generate an energy allocation optimization plan based on the adjusted energy allocation strategy and optimized energy subsidies.

8. The method for controlling smart energy operation costs based on big data according to claim 7 is characterized in that: Generating an energy allocation optimization plan according to the adjusted energy allocation strategy and the optimized energy subsidy situation includes the following steps: For each energy device, calculate its expected energy consumption under the optimization solution ; According to expected energy consumption and equipment operating efficiency , calculate its expected energy output under the optimization scheme ; For expected energy output Lower than the minimum optimized output requirement of the equipment Equipment maintenance or upgrade is required, and the maintenance or upgrade cost is ; Generate detailed energy distribution optimization reports based on the expected energy consumption, expected energy output, and equipment maintenance or upgrade status of the optimization plan.

9. A smart energy operation cost control system based on big data, characterized in that: include: Data collection module, through which energy consumption data of various energy devices at different time periods are obtained; A model building module, which cleans and preprocesses the energy consumption data and builds an energy consumption model; Abnormal energy consumption pattern recognition module, which recognizes abnormal energy consumption patterns based on historical energy consumption data; The energy allocation strategy optimization module optimizes the energy allocation strategy according to the identification results to reduce resource waste.

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