Smart energy storage management and control method and device, computer equipment and storage medium

Abstract

The invention relates to a smart energy storage management and control method and device, computer equipment and a storage medium, and the method comprises the steps: collecting meteorological data, living information and electric energy consumption data of a management and control region in real time, inputting the meteorological data, the living information and the electric energy consumption data into a preset electric energy consumption prediction model, and obtaining electric energy prediction data; performing difference calculation on the electric energy prediction data and the electric energy consumption data to obtain electric energy difference data, and dividing the control area into a controllable area, a self-sufficient area and a to-be-controlled area according to the electric energy difference data and a preset electric energy difference threshold; and according to a preset electric energy allocation rule, allocating the electric energy in the management and control area, and adjusting the electric energy use strategy of each area in real time to realize optimal configuration of the electric energy. The method has the effect of improving the energy management efficiency.

Description

Technical Field

[0001] The present application relates to the technical field of energy management, and in particular to a smart energy storage management and control method, device, computer equipment and storage medium. Background Art

[0002] The existing energy management methods mainly adopt traditional manual recording and independent equipment monitoring methods. However, since manual recording requires a lot of manpower input, the data processing process is cumbersome and prone to errors, and information is not updated in time, it affects management efficiency and decision-making speed. Independent equipment monitoring methods are independent of each other and lack system integration. It is impossible to achieve real-time and comprehensive data collection, which further restricts the refinement and intelligence of energy management, resulting in the low efficiency of traditional energy management methods for energy management, and it is difficult to meet the needs of efficient, refined and sustainable development of modern energy management.

[0003] The above-mentioned existing technical solutions have the following defects: the traditional energy management method is inefficient in energy management, so there is room for improvement. Summary of the invention

[0004] In order to improve the efficiency of energy management, the present application provides a smart energy storage management and control method, device, computer equipment and storage medium.

[0005] The above-mentioned invention objective of the present application is achieved through the following technical solutions: A smart energy storage management and control method, the smart energy storage management and control method comprising: Collecting meteorological data, residential information and electric energy usage data of the controlled area in real time, and inputting the meteorological data, the residential information and the electric energy usage data into a preset electric energy usage prediction model to obtain electric energy prediction data; The electric energy prediction data and the electric energy usage data are subjected to difference calculation to obtain electric energy difference data, and the control area is divided into a controllable area, a self-sufficient area and an area requiring control according to the electric energy difference data and a preset electric energy difference threshold; According to the preset power allocation rules, the power in the control area is allocated, and the power usage strategy of each area is adjusted in real time to achieve optimal configuration of power.

[0006] By adopting the above technical solution, by collecting the meteorological data, residential information and electricity usage data of the control area in real time, and inputting the meteorological data, residential information and electricity usage data into the preset electricity usage prediction model, the electricity prediction data is obtained, which can realize the accurate prediction of future electricity demand, thereby improving the foresight of electricity management; by performing difference calculation between the electricity prediction data and the electricity usage data, the electricity difference data is obtained, and the control area is divided into controllable areas, self-sufficient areas and controllable areas according to the electricity difference data and the preset electricity difference threshold, which can realize the accurate matching of electricity demand and supply, thereby improving the utilization efficiency of electricity; by allocating the electricity in the control area according to the preset electricity allocation rules, and adjusting the electricity use strategy of each area in real time, it is possible to realize the dynamic optimization configuration of electricity, thereby improving the intelligence and refinement of electricity management.

[0007] In one example, the present application may be further configured as follows: the construction of the electric energy usage prediction model specifically includes: Acquire historical power usage data and historical meteorological data and historical residence information corresponding to the historical power usage data, and classify the historical power usage data according to the historical meteorological data and the historical residence information to obtain a model training data set; The preset multivariate linear regression model is iteratively trained according to the model training data set to obtain the electric energy usage prediction model.

[0008] By adopting the above technical scheme, by acquiring historical electricity usage data, historical meteorological data and historical residential information, and classifying the historical electricity usage data according to the historical meteorological data and historical residential information, a model training data set is obtained, which can ensure that the training data of the prediction model is comprehensive and accurate, thereby improving the accuracy of the electricity usage prediction model; by iteratively training the preset multivariate linear regression model according to the model training data set, an electricity usage prediction model is obtained, which can improve the prediction accuracy of the model by continuously optimizing the model parameters, thereby improving the scientificity and rationality of electricity management.

[0009] In one example, the present application may be further configured as follows: the historical electricity usage data is classified according to the historical meteorological data and the historical residence information to obtain a model training data set, wherein the model training set includes a model positive training set and a model negative training set, specifically including: Perform similarity query on the historical meteorological data and the historical residential information to obtain a similar condition data set; Compare the historical power consumption data corresponding to each condition in the similar condition data set, and calculate the mean of the historical power consumption data to obtain average power consumption data; According to the average electricity consumption data, the historical electricity consumption data outside the preset range of the average electricity consumption data is taken as abnormal data, and the historical meteorological data and the historical residential information corresponding to the abnormal data are encapsulated as the negative training data set, and other data except the abnormal data are encapsulated to obtain the positive training data set.

[0010] By adopting the above technical scheme, by classifying the historical electricity usage data according to the historical meteorological data and the historical residential information to obtain the model training data set, the quality of the model training data can be further improved, thereby improving the accuracy and stability of the prediction model; by performing similar queries on the historical meteorological data and the historical residential information to obtain the similar condition data set, the representativeness and extensiveness of the training data can be ensured, thereby improving the generalization ability of the model; by comparing the historical electricity usage data corresponding to each condition in the similar condition data set, and calculating the mean of the historical electricity usage data to obtain the average electricity usage data, and based on the average electricity usage data, the historical electricity usage data outside the preset range of the average electricity usage data is taken as abnormal data, and the historical meteorological data and historical residential information corresponding to the abnormal data are encapsulated as a negative training data set, and other data other than the abnormal data are encapsulated to obtain a positive training data set, which can effectively distinguish between normal electricity usage patterns and abnormal electricity usage patterns, thereby improving the prediction accuracy and robustness of the model.

[0011] In one example, the present application may be further configured as follows: dividing the control area into a controllable area, a self-sufficient area, and a controllable area according to the electric energy difference data and a preset electric energy difference threshold value, specifically including: Comparing the electric energy difference data of different areas with the preset electric energy difference threshold to obtain corresponding comparison results; When the comparison result is that the electric energy difference data reaches the preset electric energy difference threshold, the corresponding area is used as the adjustable area; When the comparison result shows that the electric energy difference data is between the preset electric energy difference threshold value, the corresponding area is used as the self-sufficient area; When the comparison result is that the electric energy difference data is lower than the preset electric energy difference threshold, the corresponding area is used as the area to be regulated.

[0012] By adopting the above technical solution, by comparing the electric energy difference data of different areas with the preset electric energy difference threshold, the corresponding comparison results are obtained, which can be accurately divided according to the actual electric energy demand, thereby improving the rationality of electric energy distribution; by dividing the control area into different types of areas according to the comparison results, it is possible to identify areas with flexible and controllable electricity use, areas with self-sufficient electricity, and areas that require additional regulation, thereby improving the flexibility and efficiency of electricity use, and improving the accuracy and effectiveness of electricity management.

[0013] In one example, the present application may be further configured as follows: the electric energy in the control area is allocated according to the preset electric energy allocation rule, and the electric energy use strategy of each area is adjusted in real time, specifically including: Allocating excess electric energy in the controllable area to the area requiring control according to the electric energy difference data of the area requiring control; According to the electric energy difference data of different areas, the electric energy allocation strategy of each area is dynamically adjusted to ensure efficient use of electric energy.

[0014] By adopting the above technical solution, by allocating the excess electric energy in the controllable area to the area that needs to be controlled according to the electric energy difference data of the area that needs to be controlled, the excess electric energy can be effectively utilized, thereby improving the overall utilization rate of electric energy; by dynamically adjusting the electric energy allocation strategy of each area according to the electric energy difference data of different areas, the dynamic optimization configuration of electric energy can be achieved, thereby improving the intelligence level and efficiency of electric energy management.

[0015] In one example, the present application may be further configured as follows: the smart energy storage management and control method further includes: When the controllable area does not exist in the control area division, a power shortage message is triggered, and an emergency power consumption reduction instruction is generated according to a preset energy consumption reduction strategy.

[0016] By adopting the above technical solution, when there is no controllable area in the control area division, a power shortage message is triggered, and an emergency power reduction instruction is generated according to the preset energy consumption reduction strategy. It is possible to respond in time when there is power shortage, thereby improving the emergency handling capability and reliability of power management.

[0017] The second object of the invention is achieved by the following technical solutions: A smart energy storage control device, characterized in that the smart energy storage control device comprises: A collection and prediction module is used to collect meteorological data, residential information and electric energy usage data of the control area in real time, and input the meteorological data, the residential information and the electric energy usage data into a preset electric energy usage prediction model to obtain electric energy prediction data; An electric energy difference calculation module is used to perform difference calculation on the electric energy prediction data and the electric energy usage data to obtain electric energy difference data, and divide the control area into a controllable area, a self-sufficient area and an area requiring control according to the electric energy difference data and a preset electric energy difference threshold; The power optimization configuration module is used to allocate the power in the control area according to the preset power allocation rules, and adjust the power usage strategy of each area in real time to achieve optimal configuration of power.

[0018] By adopting the above technical solution, by collecting the meteorological data, residential information and electricity usage data of the control area in real time, and inputting the meteorological data, residential information and electricity usage data into the preset electricity usage prediction model, the electricity prediction data is obtained, which can realize the accurate prediction of future electricity demand, thereby improving the foresight of electricity management; by performing difference calculation between the electricity prediction data and the electricity usage data, the electricity difference data is obtained, and the control area is divided into controllable areas, self-sufficient areas and controllable areas according to the electricity difference data and the preset electricity difference threshold, which can realize the accurate matching of electricity demand and supply, thereby improving the utilization efficiency of electricity; by allocating the electricity in the control area according to the preset electricity allocation rules, and adjusting the electricity use strategy of each area in real time, it is possible to realize the dynamic optimization configuration of electricity, thereby improving the intelligence and refinement of electricity management.

[0019] The third objective of the present application is achieved through the following technical solutions: A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned smart energy storage management and control method when executing the computer program.

[0020] The fourth objective of the present application is achieved through the following technical solutions: A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned smart energy storage management and control method.

[0021] In summary, this application includes the following beneficial technical effects: 1. By collecting meteorological data, residential information and electricity consumption data of the control area in real time, and inputting the meteorological data, residential information and electricity consumption data into the preset electricity consumption prediction model, the electricity prediction data is obtained, which can realize the accurate prediction of future electricity demand, thereby improving the foresight of electricity management; by performing difference calculation between the electricity prediction data and the electricity consumption data, the electricity difference data is obtained, and the control area is divided into controllable areas, self-sufficient areas and controllable areas according to the electricity difference data and the preset electricity difference threshold, which can realize the accurate matching of electricity demand and supply, thereby improving the utilization efficiency of electricity; by allocating the electricity in the control area according to the preset electricity allocation rules, and adjusting the electricity use strategy of each area in real time, it is possible to realize the dynamic optimization configuration of electricity, thereby improving the intelligence and refinement level of electricity management; 2. By obtaining historical electricity consumption data, historical meteorological data and historical residential information, and classifying the historical electricity consumption data according to the historical meteorological data and historical residential information, a model training data set is obtained, which can ensure that the training data of the prediction model is comprehensive and accurate, thereby improving the accuracy of the electricity consumption prediction model; by iteratively training the preset multivariate linear regression model according to the model training data set, an electricity consumption prediction model is obtained, which can improve the prediction accuracy of the model by continuously optimizing the model parameters, thereby improving the scientificity and rationality of electricity management; 3. By classifying the historical electricity usage data according to the historical meteorological data and the historical residential information to obtain the model training data set, the quality of the model training data can be further improved, thereby improving the accuracy and stability of the prediction model; by performing similar queries on the historical meteorological data and the historical residential information to obtain the similar condition data set, the representativeness and extensiveness of the training data can be ensured, thereby improving the generalization ability of the model; by comparing the historical electricity usage data corresponding to each condition in the similar condition data set, and calculating the mean of the historical electricity usage data, the average electricity usage data is obtained, and based on the average electricity usage data, the historical electricity usage data outside the preset range of the average electricity usage data is taken as abnormal data, and the historical meteorological data and historical residential information corresponding to the abnormal data are encapsulated as a negative training data set, and other data other than the abnormal data are encapsulated to obtain a positive training data set, which can effectively distinguish between normal electricity usage patterns and abnormal electricity usage patterns, thereby improving the prediction accuracy and robustness of the model. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a flow chart of a smart energy storage management and control method in one embodiment of the present application; Figure 2 It is a flowchart for implementing the construction of an electric energy consumption prediction model in a smart energy storage control method in one embodiment of the present application; Figure 3It is a flowchart for implementing step S101 in the smart energy storage management and control method in one embodiment of the present application; Figure 4 It is a flowchart for implementing step S20 in the smart energy storage management and control method in one embodiment of the present application; Figure 5 It is a flowchart for implementing step S30 in the smart energy storage management and control method in one embodiment of the present application; Figure 6 This is another implementation flow chart of the smart energy storage management and control method in one embodiment of the present application; Figure 7 This is a principle block diagram of a smart energy storage control device in one embodiment of the present application; Figure 8 It is a schematic diagram of a device in an embodiment of the present application. DETAILED DESCRIPTION

[0023] The present application is further described in detail below in conjunction with the accompanying drawings.

[0024] In one embodiment, if Figure 1 As shown, the present application discloses a smart energy storage management and control method, which specifically includes the following steps: S10: Collect meteorological data, residential information and electricity usage data of the control area in real time, and input the meteorological data, residential information and electricity usage data into a preset electricity usage prediction model to obtain electricity prediction data.

[0025] Specifically, by deploying multiple meteorological monitoring devices in the control area, meteorological data including temperature, humidity, wind speed, etc. are collected in real time. At the same time, through the smart meter and user information management system, the real-time residential information and electricity usage data of each user are obtained, and the collected data is transmitted to the central data processing center. The meteorological data, residential information and electricity usage data are pre-processed, including data cleaning, format conversion and time alignment. Then, the processed data is input into the preset electricity usage prediction model. By analyzing these data, the electricity prediction data for a period of time in the future is output.

[0026] S20: Calculate the difference between the power forecast data and the power consumption data to obtain power difference data, and divide the control area into a controllable area, a self-sufficient area, and a control-required area according to the power difference data and a preset power difference threshold.

[0027] Specifically, the electricity forecast data is compared point by point with the current real-time collected electricity usage data, the electricity difference data for each time period is calculated, and these electricity difference data are compared with the system preset electricity difference threshold. According to the comparison results, the control area is divided into three categories: the controllable area refers to the area where the electricity difference data is greater than the preset threshold, and these areas have excess electricity; the self-sufficient area refers to the area where the electricity difference data is within the preset threshold range, and the electricity supply and demand in these areas are balanced; the area that needs to be controlled refers to the area where the electricity difference data is less than the preset threshold, and these areas are short of electricity and need external allocation.

[0028] S30: According to the preset power allocation rules, the power in the control area is allocated, and the power usage strategy of each area is adjusted in real time to achieve optimal configuration of power.

[0029] Specifically, according to the power allocation rules pre-set by the system, the area that needs to be regulated is first identified, and then the excess power situation in the adjustable area is analyzed. Through the power allocation system, the excess power in the adjustable area is dynamically allocated to the area that needs to be regulated. At the same time, according to the real-time monitored power difference data, the power usage strategy of each area is continuously adjusted to ensure that the power utilization efficiency is maximized while meeting the power demand of the area that needs to be regulated. For example, smart grid technology can be used to allocate excess power to peak power demand periods or high-demand areas, thereby achieving the optimal configuration of power resources.

[0030] In one embodiment, if Figure 2 As shown, in step S10, the construction of the electric energy consumption prediction model specifically includes: S101: Acquire historical power usage data and historical meteorological data corresponding to the historical power usage data and historical residential information, and classify the historical power usage data according to the historical meteorological data and the historical residential information to obtain a model training data set.

[0031] Specifically, through the historical data storage system, the electricity consumption data, meteorological data and residential information of the control area over the years are retrieved. These data contain detailed records of different time periods, seasons and special weather conditions. These historical data are sorted and classified, and the electricity consumption data are divided into different categories according to different meteorological conditions and residential information to obtain training data sets, thereby providing comprehensive and detailed data support for the training of the electricity consumption prediction model.

[0032] S102: Iteratively train a preset multivariate linear regression model according to a model training data set to obtain an electric energy consumption prediction model.

[0033] Specifically, the classified and organized training data set is input into the preset multivariate linear regression model, and the model is trained using an iterative training algorithm. In each iteration, the model continuously adjusts its parameters according to the current input data set to reduce the prediction error. During the training process, the cross-validation technique is used to evaluate the performance of the model to ensure the generalization ability of the model on different data sets. Through multiple iterations, when the error value between the result obtained from the input data and the established result does not exceed 5%, a fully trained electricity consumption prediction model is obtained. The model can accurately predict future electricity consumption based on the input meteorological data and residential information.

[0034] In one embodiment, if Figure 3 As shown, in step S101, the historical electricity consumption data is classified according to the historical meteorological data and the historical residential information to obtain a model training data set, which includes a model positive training set and a model negative training set, specifically including: S1011: Perform similarity query on historical meteorological data and historical residential information to obtain a similar condition data set.

[0035] Specifically, all historical meteorological data and residential information are standardized to ensure the comparability between different data dimensions. Then, similarity calculation algorithms such as Euclidean distance or cosine similarity are used to perform similarity analysis on each historical data record to find the historical records that are most similar to the current conditions. These similar historical records are combined into a new dataset as the similar condition dataset, which contains the historical electricity usage data that is closest to the current meteorological conditions and residential information, thereby providing a reference for further analysis and model training.

[0036] S1012: Compare the historical power consumption data corresponding to each condition in the similar condition data set, and calculate the mean of the historical power consumption data to obtain average power consumption data.

[0037] Specifically, in the similar condition data set, for each meteorological condition and residential information, all corresponding historical electricity usage data are extracted, and these data are statistically analyzed to calculate the average electricity usage under each condition. For example, for historical data under the same temperature and humidity conditions, the average electricity usage value under these conditions can be calculated. In this way, a series of average electricity usage data are obtained. These data reflect the typical usage patterns of electricity under different conditions, and provide a basis for identifying abnormal electricity usage behavior and conducting further electricity optimization.

[0038] S1013: Based on the average electricity consumption data, the historical electricity consumption data outside the preset range of the average electricity consumption data is taken as abnormal data, and the historical meteorological data and historical residential information corresponding to the abnormal data are encapsulated as a negative training data set, and other data except the abnormal data are encapsulated to obtain a positive training data set.

[0039] Specifically, all historical electricity usage data are compared with their corresponding average electricity usage data, and data points that exceed a preset range, such as exceeding 70% of the average or being lower than 70% of the average, are identified. These data points are marked as abnormal data, and these abnormal data and their corresponding meteorological conditions and residential information are extracted to form a negative training data set, which is used in the anomaly detection module in model training. The remaining data that does not exceed the preset range is marked as normal data to form a positive training data set. The two types of data sets are used together for model training to ensure that the model can identify and handle abnormal electricity usage, thereby improving the accuracy and reliability of the prediction.

[0040] In one embodiment, if Figure 4 As shown, in step S20, the control area is divided into a controllable area, a self-sufficient area and a controllable area according to the electric energy difference data and the preset electric energy difference threshold, specifically including: S21: Compare the electric energy difference data of different areas with a preset electric energy difference threshold to obtain a corresponding comparison result.

[0041] Specifically, for each controlled area, its electric energy difference data is calculated, and these data are compared one by one with the electric energy difference threshold preset by the system. During the comparison process, the electric energy difference data of each area is numerically compared with the threshold, and the comparison results are recorded to mark whether each area exceeds or falls below the threshold.

[0042] S22: When the comparison result shows that the electric energy difference data reaches a preset electric energy difference threshold, the corresponding area is used as an adjustable area.

[0043] Specifically, for areas where the power difference data reaches or exceeds a preset power difference threshold, these areas are marked as controllable areas, that is, these areas have excess power that can be allocated and optimized for utilization.

[0044] S23: When the comparison result shows that the electric energy difference data is within the preset electric energy difference threshold, the corresponding area is regarded as a self-sufficient area.

[0045] Specifically, for areas where the power difference data is within a preset power difference threshold range, these areas are marked as self-sufficient areas, that is, the power supply and demand of these areas are balanced and no external regulation is required.

[0046] S24: When the comparison result shows that the electric energy difference data is lower than the preset electric energy difference threshold, the corresponding area is regarded as an area requiring regulation.

[0047] Specifically, for areas where the power difference data is lower than a preset power difference threshold, these areas are marked as areas that need to be regulated, that is, these areas are short of power and need to allocate power from other areas to meet demand.

[0048] In one embodiment, if Figure 5 As shown, in step S30, the electric energy in the control area is allocated according to the preset electric energy allocation rules, and the electric energy use strategy of each area is adjusted in real time, specifically including: S31: Allocate the excess electric energy in the controllable area to the area requiring control according to the electric energy difference data of the area requiring control.

[0049] Specifically, the system identifies the current areas that need to be regulated and the areas that can be regulated based on the real-time monitored electric energy difference data, and uses smart grid technology to accurately distribute the excess electric energy in the regulated areas to the areas that need to be regulated through the electric energy distribution network, such as adjusting the transmission ratio of the transformer, activating the backup electric energy in the energy storage device, or dynamically adjusting the electric energy transmission path between regions, to ensure that the excess electric energy can be quickly and effectively transmitted to the areas that need to be regulated to meet their electric energy needs.

[0050] S32: Dynamically adjust the power allocation strategy of each area according to the power difference data of different areas to ensure efficient use of power.

[0051] Specifically, during the power allocation process, the power difference data of each area is continuously monitored, and the power allocation strategy is dynamically adjusted based on this data. For example, during the peak period of power demand, power can be allocated to the areas that need to be regulated first; during the trough period of power demand, the charging capacity of the energy storage equipment can be increased to prepare for future peak periods. Through intelligent algorithm analysis and real-time regulation, the power allocation ratio of each area can be flexibly adjusted according to actual needs, ensuring the optimal utilization of power resources and improving the overall energy management efficiency.

[0052] In one embodiment, if Figure 6 As shown, in step S10, the smart energy storage management and control method further includes: S40: When there is no controllable area in the control area division, a power shortage message is triggered, and an emergency power consumption reduction instruction is generated according to a preset energy consumption reduction strategy.

[0053] Specifically, when the system detects that there is no excess electricity available for allocation in all controlled areas, it immediately triggers a power shortage alarm and sends a warning message to the management personnel through the system interface and communication equipment. At the same time, the system automatically generates emergency consumption reduction instructions based on the preset energy consumption reduction strategy, instructing each area to immediately take energy-saving measures, such as reducing the operating power of non-critical equipment, suspending the operation of some high-energy-consuming equipment, etc., to reduce electricity consumption, alleviate power shortages, and ensure that basic production and living electricity needs can still be maintained under extreme circumstances.

[0054] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0055] In one embodiment, a smart energy storage management and control device is provided, and the smart energy storage management and control device corresponds to the smart energy storage management and control method in the above embodiment. Figure 7 As shown, the smart energy storage control device includes a collection prediction module, an electric energy difference calculation module, and an electric energy optimization configuration module. The detailed description of each functional module is as follows: The collection and prediction module is used to collect meteorological data, residential information and power consumption data of the control area in real time, and input the meteorological data, residential information and power consumption data into a preset power consumption prediction model to obtain power consumption prediction data; An electric energy difference calculation module is used to perform difference calculation between the electric energy prediction data and the electric energy consumption data to obtain electric energy difference data, and divide the control area into a controllable area, a self-sufficient area and an area requiring control according to the electric energy difference data and a preset electric energy difference threshold; The power optimization configuration module is used to allocate the power in the control area according to the preset power allocation rules, and adjust the power usage strategy of each area in real time to achieve optimal power configuration.

[0056] Optionally, the construction of the electric energy consumption prediction model specifically includes: A model data acquisition module is used to acquire historical power consumption data and historical meteorological data corresponding to the historical power consumption data and historical residential information, and classify the historical power consumption data according to the historical meteorological data and historical residential information to obtain a model training data set; The model training module is used to iteratively train the preset multivariate linear regression model according to the model training data set to obtain the electricity consumption prediction model.

[0057] Optionally, the model data acquisition module specifically includes: The similarity condition query submodule is used to perform similarity query on historical meteorological data and historical residential information to obtain a similar condition data set; The average power consumption calculation submodule is used to compare the historical power consumption data corresponding to each condition in the similar condition data set, and calculate the mean of the historical power consumption data to obtain the average power consumption data; The abnormal data classification submodule is used to classify the historical electricity consumption data outside the preset range of the average electricity consumption data as abnormal data based on the average electricity consumption data, and to encapsulate the historical meteorological data and historical residential information corresponding to the abnormal data as a negative training data set, and to encapsulate other data except the abnormal data to obtain a positive training data set.

[0058] Optionally, the electric energy difference calculation module specifically includes: The regional comparison submodule is used to compare the electric energy difference data of different regions with the preset electric energy difference threshold to obtain the corresponding comparison result; The controllable area division submodule is used to set the corresponding area as a controllable area when the comparison result is that the electric energy difference data reaches a preset electric energy difference threshold; A self-sufficient area division submodule, for treating the corresponding area as a self-sufficient area when the comparison result shows that the electric energy difference data is between a preset electric energy difference threshold value; The submodule for dividing the area to be regulated is used to regard the corresponding area as the area to be regulated when the comparison result shows that the electric energy difference data is lower than the preset electric energy difference threshold.

[0059] Optionally, the power optimization configuration module specifically includes: The excess electric energy distribution submodule is used to distribute the excess electric energy in the controllable area to the area requiring control according to the electric energy difference data of the area requiring control; The dynamic allocation submodule is used to dynamically adjust the power allocation strategy of each area according to the power difference data of different areas to ensure efficient use of power.

[0060] Optionally, the smart energy storage management and control method also includes: The emergency power reduction trigger module is used to trigger the power shortage message when there is no controllable area in the control area division, and generate an emergency power reduction instruction according to the preset energy consumption reduction strategy.

[0061] For the specific limitations of the smart energy storage control device, please refer to the limitations of the smart energy storage control method above, which will not be repeated here. Each module in the above-mentioned smart energy storage control device can be implemented in whole or in part through software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0062] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 8 As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a smart energy storage management and control method is implemented.

[0063] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program: Collect meteorological data, residential information and electricity usage data of the control area in real time, and input the meteorological data, residential information and electricity usage data into a preset electricity usage prediction model to obtain electricity prediction data; The power forecast data and the power consumption data are difference calculated to obtain power difference data, and the control area is divided into a controllable area, a self-sufficient area and a control-required area according to the power difference data and a preset power difference threshold; According to the preset power allocation rules, the power in the control area is allocated, and the power usage strategy of each area is adjusted in real time to achieve optimal configuration of power.

[0064] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: Collect meteorological data, residential information and electricity usage data of the control area in real time, and input the meteorological data, residential information and electricity usage data into a preset electricity usage prediction model to obtain electricity prediction data; The power forecast data and the power consumption data are difference calculated to obtain power difference data, and the control area is divided into a controllable area, a self-sufficient area and a control-required area according to the power difference data and a preset power difference threshold; According to the preset power allocation rules, the power in the control area is allocated, and the power usage strategy of each area is adjusted in real time to achieve optimal configuration of power.

[0065] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0066] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0067] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A smart energy storage management and control method, characterized in that: The smart energy storage management and control method includes: Collecting meteorological data, residential information and electric energy usage data of the controlled area in real time, and inputting the meteorological data, the residential information and the electric energy usage data into a preset electric energy usage prediction model to obtain electric energy prediction data; The electric energy prediction data and the electric energy usage data are subjected to difference calculation to obtain electric energy difference data, and the control area is divided into a controllable area, a self-sufficient area and an area requiring control according to the electric energy difference data and a preset electric energy difference threshold; According to the preset power allocation rules, the power in the control area is allocated, and the power usage strategy of each area is adjusted in real time to achieve optimal configuration of power.

2. The smart energy storage management and control method according to claim 1, characterized in that: The construction of the electric energy consumption prediction model specifically includes: Acquire historical power usage data and historical meteorological data and historical residence information corresponding to the historical power usage data, and classify the historical power usage data according to the historical meteorological data and the historical residence information to obtain a model training data set; The preset multivariate linear regression model is iteratively trained according to the model training data set to obtain the electric energy usage prediction model.

3. The smart energy storage management and control method according to claim 1 is characterized in that: The historical electricity usage data is classified according to the historical meteorological data and the historical residence information to obtain a model training data set, wherein the model training set includes a model positive training set and a model negative training set, specifically including: Perform similarity query on the historical meteorological data and the historical residential information to obtain a similar condition data set; Compare the historical power consumption data corresponding to each condition in the similar condition data set, and calculate the mean of the historical power consumption data to obtain average power consumption data; According to the average electricity consumption data, the historical electricity consumption data outside the preset range of the average electricity consumption data is taken as abnormal data, and the historical meteorological data and the historical residential information corresponding to the abnormal data are encapsulated as the negative training data set, and other data except the abnormal data are encapsulated to obtain the positive training data set.

4. The smart energy storage management and control method according to claim 1, characterized in that: The dividing of the control area into a controllable area, a self-sufficient area and an area requiring control according to the electric energy difference data and a preset electric energy difference threshold specifically includes: Comparing the electric energy difference data of different areas with the preset electric energy difference threshold to obtain corresponding comparison results; When the comparison result is that the electric energy difference data reaches the preset electric energy difference threshold, the corresponding area is used as the adjustable area; When the comparison result shows that the electric energy difference data is between the preset electric energy difference threshold value, the corresponding area is used as the self-sufficient area; When the comparison result is that the electric energy difference data is lower than the preset electric energy difference threshold, the corresponding area is used as the area to be regulated.

5. The smart energy storage management and control method according to claim 4 is characterized in that: The method of allocating the electric energy in the control area according to the preset electric energy allocation rules and adjusting the electric energy use strategy of each area in real time specifically includes: Allocating excess electric energy in the controllable area to the area requiring control according to the electric energy difference data of the area requiring control; According to the electric energy difference data of different areas, the electric energy allocation strategy of each area is dynamically adjusted to ensure efficient use of electric energy.

6. The smart energy storage management and control method according to claim 4 is characterized in that: The smart energy storage management and control method also includes: When the controllable area does not exist in the control area division, a power shortage message is triggered, and an emergency power consumption reduction instruction is generated according to a preset energy consumption reduction strategy.

7. A smart energy storage management and control device, characterized in that: The smart energy storage control device comprises: A collection and prediction module is used to collect meteorological data, residential information and electric energy usage data of the control area in real time, and input the meteorological data, the residential information and the electric energy usage data into a preset electric energy usage prediction model to obtain electric energy prediction data; An electric energy difference calculation module is used to perform difference calculation on the electric energy prediction data and the electric energy usage data to obtain electric energy difference data, and divide the control area into a controllable area, a self-sufficient area and an area requiring control according to the electric energy difference data and a preset electric energy difference threshold; The power optimization configuration module is used to allocate the power in the control area according to the preset power allocation rules, and adjust the power usage strategy of each area in real time to achieve optimal configuration of power.

8. The smart energy storage management and control device according to claim 7, characterized in that: The construction of the electric energy consumption prediction model specifically includes: A model data acquisition module, used to acquire historical power usage data and historical meteorological data and historical residence information corresponding to the historical power usage data, and classify the historical power usage data according to the historical meteorological data and the historical residence information to obtain a model training data set; The model training module is used to iteratively train the preset multivariate linear regression model according to the model training data set to obtain the electric energy usage prediction model.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the smart energy storage management and control method as described in any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the smart energy storage management and control method as described in any one of claims 1 to 6 are implemented.

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