Energy storage device control method and device based on load prediction, terminal equipment and storage medium

Abstract

The invention discloses an energy storage device control method and device based on load prediction, terminal equipment and a storage medium, and the method comprises the steps: predicting a load prediction value of a micro-grid in a future time period through employing a preset load prediction model according to real-time operation data, and carrying out the prediction of the load of the micro-grid according to the power generation amount and the load prediction value; whether surplus electric quantity exists after the current generating capacity of the micro-grid meets the future load requirement or not is judged, if yes, the energy storage device is controlled to be charged, electric energy waste is avoided, and if not, the energy storage device is controlled to supply power to the micro-grid, so that it is guaranteed that the micro-grid can meet the future load requirement, and the power consumption of the micro-grid is reduced. And stable power supply of the micro-grid is effectively ensured.

Description

Technical Field

[0001] The present invention relates to the field of mobile energy storage technology, and in particular to a method, device, terminal equipment and storage medium for controlling an energy storage device based on load prediction. Background Art

[0002] With the country's strong support for new energy power generation technology, new energy units such as photovoltaic power generation and wind power generation are gradually added to microgrids. However, new energy power generation is often affected by time and space, and the output state is not stable, which will cause volatility and uncertainty in the microgrid load. In addition, the power consumption at the user end is also uncertain, especially the promotion of electric vehicles, which is prone to two extreme situations: charging in groups and charging without anyone. Therefore, energy storage in microgrids is very necessary.

[0003] Electrochemical energy storage devices have the characteristics of fast charging / discharging speed and small footprint, and have been widely used for energy storage in microgrids. However, at present, electrochemical energy storage basically needs to wait for the microgrid to feedback the load value before carrying out the next step of scheduling, that is, determining whether to supply power to the microgrid or absorb excess power from the microgrid. Since it is impossible to know the future load in advance, it is easy for the energy storage device to fail to supply energy to the microgrid in time, resulting in the microgrid's own power generation being difficult to meet the current load demand, or failing to absorb the microgrid's excess power for storage in time, resulting in energy waste. Summary of the invention

[0004] The embodiments of the present invention provide a method, device, terminal equipment and storage medium for controlling an energy storage device based on load prediction, which controls the charging and discharging of the energy storage device by predicting the load of a microgrid in a future time period in advance, thereby effectively ensuring the stable power supply of the microgrid and effectively avoiding the waste of electric energy.

[0005] An embodiment of the present invention provides a method for controlling an energy storage device based on load prediction, comprising:

[0006] Obtain real-time operation data and power generation of the microgrid;

[0007] Inputting the real-time operation data into a preset load prediction model so that the load prediction model predicts the load prediction value of the microgrid within a future time period according to the real-time operation data;

[0008] According to the power generation and the load forecast value, determining whether the microgrid will have a power surplus in a future time period;

[0009] If so, controlling the energy storage device to charge and absorb surplus electricity from the microgrid;

[0010] If not, the energy storage device is controlled to supply power to the microgrid.

[0011] Furthermore, the energy storage device control method based on load prediction described in the above embodiment of the invention further includes:

[0012] Acquire historical operation data of the microgrid and historical load data corresponding to each of the historical operation data; wherein the historical operation data includes: current, voltage, and power generation;

[0013] Using the historical operation data and the corresponding historical load data as training samples to construct a training data set;

[0014] Construct the initial prediction model of BP neural network structure;

[0015] The initial prediction model is trained using the training data set, and during the training process, the initial prediction model is evaluated using a preset loss function according to an output result of the initial prediction model and the historical load data, and an evaluation result is generated;

[0016] When the evaluation result converges and is minimized, it is determined that the training of the initial prediction model is completed, and the trained initial prediction model is used as the load prediction model.

[0017] Furthermore, the preset loss function is used to evaluate the initial prediction model according to the output result of the initial prediction model and the historical load data, and an evaluation result is generated, including:

[0018] The following loss function is used to evaluate the initial prediction model based on the output of the initial prediction model and the historical load data:

[0019]

[0020] Among them, LOSS is the evaluation result, n is the total number of training samples in the training data set, and y i is the output result predicted by the initial prediction model based on the historical operation data in the i-th training sample, is the historical load data in the i-th training sample of the initial prediction model.

[0021] Furthermore, the energy storage device control method based on load prediction described in the above embodiment of the invention further includes:

[0022] Obtain the actual load value of the microgrid in the future period of time;

[0023] Calculating the error between the load prediction value and the load actual value;

[0024] When the error is greater than a preset error threshold, the real-time operation data and the actual load value are saved as model update training samples in a preset model training database;

[0025] When the number of the model update training samples is greater than a preset number threshold, a number of model update training samples are acquired from the model training database to train the load forecasting model to generate a new load forecasting model.

[0026] Further, judging whether the microgrid will have a power surplus in a future time period according to the power generation and the load forecast value includes:

[0027] comparing the power generation with the load forecast value;

[0028] When the power generation is greater than the load forecast value, determining that the microgrid will have a power surplus in a future time period;

[0029] When the power generation is less than the load forecast value, it is determined that the microgrid will have insufficient power supply within a future time period.

[0030] Another embodiment of the present invention provides a load forecasting-based energy storage device control device, comprising:

[0031] Data acquisition module, used to obtain real-time operation data and power generation of the microgrid;

[0032] A load prediction module, used for inputting the real-time operation data into a preset load prediction model, so that the load prediction model predicts the load prediction value of the microgrid within a future time period according to the real-time operation data;

[0033] An electricity evaluation module, used to determine whether the microgrid will have an electricity surplus in a future time period according to the power generation and the load forecast value;

[0034] A first control module, configured to control the energy storage device to charge and absorb surplus electricity from the microgrid;

[0035] The second control module is used to control the energy storage device to supply power to the microgrid if no.

[0036] Furthermore, the energy storage device control device based on load prediction described in the above-mentioned embodiment of the invention further includes:

[0037] Model training module;

[0038] The model training module is used to obtain historical operation data of the microgrid and historical load data corresponding to each of the historical operation data; wherein the historical operation data includes: current, voltage, power generation, and weather data;

[0039] Using the historical operation data and the corresponding historical load data as training samples to construct a training data set;

[0040] Construct the initial prediction model of BP neural network structure;

[0041] The initial prediction model is trained using the training data set, and during the training process, the initial prediction model is evaluated using a preset loss function according to an output result of the initial prediction model and the historical load data, and an evaluation result is generated;

[0042] When the evaluation result converges and is minimized, it is determined that the training of the initial prediction model is completed, and the trained initial prediction model is used as the load prediction model.

[0043] Furthermore, the energy storage device control device based on load prediction described in the above-mentioned embodiment of the invention further includes:

[0044] Model updating module;

[0045] The model updating module is used to obtain the actual load value of the microgrid in a future time period;

[0046] Calculating the error between the load prediction value and the load actual value;

[0047] When the error is greater than a preset error threshold, the real-time operation data and the actual load value are saved as model update training samples in a preset model training database;

[0048] When the number of the model update training samples is greater than a preset number threshold, a number of model update training samples are acquired from the model training database to train the load forecasting model to generate a new load forecasting model.

[0049] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, a method for controlling an energy storage device based on load forecasting as described in any one of the embodiments is implemented.

[0050] Another embodiment of the present invention provides a storage medium, characterized in that the storage medium includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute a method for controlling an energy storage device based on load forecasting as described in any one of the above embodiments.

[0051] The following beneficial effects are achieved by implementing the present invention:

[0052] The present invention discloses a control method, device, terminal equipment and storage medium of an energy storage device based on load prediction. The method adopts a preset load prediction model to predict the load prediction value of the microgrid in a future time period according to real-time operation data, and then judges whether the power generation of the current microgrid has surplus electricity after meeting the future load demand according to the power generation and the load prediction value. If so, the energy storage device is controlled to charge to avoid wasting electric energy. If not, the energy storage device is controlled to supply power to the microgrid to ensure that the microgrid can meet the future load demand, thereby effectively ensuring the stable power supply of the microgrid. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a flow chart of a method for controlling an energy storage device based on load prediction provided by one embodiment of the present invention.

[0054] Figure 2 It is a structural schematic diagram of a load prediction-based energy storage device control device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions in this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0056] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" in the specification and claims of this application and the above-mentioned figure descriptions and any variations thereof are intended to cover non-exclusive inclusions.

[0057] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "multiple" is more than two, unless otherwise clearly and specifically defined.

[0058] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0059] In the description of the embodiments of the present application, the term "and / or" is only a description of the association relationship of the associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0060] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0061] In the description of the embodiments of the present application, unless otherwise clearly specified and limited, technical terms such as "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the internal connection of two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0062] See also Figure 1 , is a flow chart of a method for controlling an energy storage device based on load prediction provided by an embodiment of the present invention, comprising:

[0063] S1, obtain the real-time operation data and power generation of the microgrid;

[0064] In a preferred embodiment of the present invention, the real-time operation data includes: current, voltage, and power generation. It can be understood that when there are new energy units such as photovoltaic power generation and wind power generation in the microgrid, the real-time operation data also includes: real-time weather data. The power generation is the sum of the power generation of each power generation unit in the microgrid.

[0065] S2. Inputting the real-time operation data into a preset load prediction model, so that the load prediction model predicts the load prediction value of the microgrid within a future time period according to the real-time operation data;

[0066] In a preferred embodiment of the present invention, in order to improve the accuracy of prediction, before the real-time operation data is input into the load prediction model, pre-processing operations such as removing outliers and normalization are performed on the real-time operation data.

[0067] Preferably, the training process of the load forecasting model includes:

[0068] S01. Acquire historical operation data of the microgrid and historical load data corresponding to each of the historical operation data; wherein the historical operation data includes: current, voltage, and power generation;

[0069] S02, using the historical operation data and the corresponding historical load data as training samples to construct a training data set;

[0070] S03, constructing an initial prediction model of BP neural network structure;

[0071] S04, using the training data set to train the initial prediction model, and during the training process, using a preset loss function to evaluate the initial prediction model according to the output result of the initial prediction model and the historical load data, and generating an evaluation result;

[0072] Preferably, the use of a preset loss function to evaluate the initial prediction model according to the output result of the initial prediction model and the historical load data, and to generate an evaluation result, including:

[0073] The following loss function is used to evaluate the initial prediction model based on the output of the initial prediction model and the historical load data:

[0074]

[0075] Among them, LOSS is the evaluation result, n is the total number of training samples in the training data set, and y i is the output result predicted by the initial prediction model based on the historical operation data in the i-th training sample, is the historical load data in the i-th training sample of the initial prediction model.

[0076] S05. When the evaluation result converges and is minimized, it is determined that the training of the initial prediction model is completed, and the trained initial prediction model is used as the load prediction model.

[0077] In a preferred embodiment of the present invention, the data in the training data set is randomly divided into training data and test data, and the ratio of training data to test data is 60% and 40% respectively.

[0078] Furthermore, the initial prediction model includes an input layer, a hidden layer and an output layer, and the node connections between layers are reflected by weights. During the training process, the training data is input into the initial prediction model, and it is continuously iterated and updated to obtain the initial prediction model with the minimized loss function.

[0079] It should be noted that the initial prediction model uses the Sigmod function as the activation function, as shown below:

[0080]

[0081] Where x is the input value.

[0082] S3. judging whether the microgrid will have a power surplus in a future time period according to the power generation and the load forecast value;

[0083] Preferably, judging whether the microgrid will have a power surplus in a future time period according to the power generation and the load forecast value includes:

[0084] S31, comparing the power generation with the load forecast value;

[0085] S32, when the power generation is greater than the load forecast value, determining that the microgrid will have a power surplus in a future time period;

[0086] S33: When the power generation is less than the load forecast value, it is determined that the microgrid will have insufficient power supply in a future time period.

[0087] S4. If yes, control the energy storage device to charge and absorb surplus electricity from the microgrid;

[0088] S5. If not, control the energy storage device to supply power to the microgrid.

[0089] In a preferred embodiment of the present invention, the energy storage device can be various electrochemical energy storage devices such as lead-acid batteries, lithium batteries, sodium-sulfur batteries, and flow batteries. When the microgrid has surplus electricity, the electrochemical energy storage device is charged to accommodate the excess electricity of the microgrid; when the microgrid has insufficient electricity supply, the electrochemical energy storage device is discharged to provide electricity to the microgrid.

[0090] Preferably, the energy storage device control method based on load prediction described in the above-mentioned embodiment of the invention further includes:

[0091] S6. Obtain the actual load value of the microgrid in the future period of time;

[0092] S7, calculating the error between the load prediction value and the load actual value;

[0093] S8. When the error is greater than a preset error threshold, the real-time operation data and the actual load value are saved as model update training samples in a preset model training database;

[0094] S9. When the number of the model update training samples is greater than a preset number threshold, a number of model update training samples are obtained from the model training database to train the load forecasting model to generate a new load forecasting model.

[0095] In a preferred embodiment of the present invention, considering the continuous development and changes of the power system, the load characteristics will also change. Therefore, this embodiment updates the neural network model to adapt to the new load characteristics.

[0096] The present embodiment provides a method for controlling an energy storage device based on load prediction. The method predicts the load prediction value of the microgrid in a future time period according to real-time operation data by using a preset load prediction model, and then determines whether the power generation of the current microgrid has surplus electricity after meeting future load requirements based on the power generation and the load prediction value. If so, the energy storage device is controlled to charge to avoid wasting electricity. If not, the energy storage device is controlled to supply power to the microgrid to ensure that the microgrid can meet future load requirements, thereby effectively ensuring the stable power supply of the microgrid.

[0097] See also Figure 2 , is a schematic diagram of a structure of a load prediction-based energy storage device control device provided by an embodiment of the present invention, comprising:

[0098] Data acquisition module, used to obtain real-time operation data and power generation of the microgrid;

[0099] A load prediction module, used for inputting the real-time operation data into a preset load prediction model, so that the load prediction model predicts the load prediction value of the microgrid within a future time period according to the real-time operation data;

[0100] An electricity evaluation module, used to determine whether the microgrid will have an electricity surplus in a future time period according to the power generation and the load forecast value;

[0101] A first control module, configured to control the energy storage device to charge and absorb surplus electricity from the microgrid;

[0102] The second control module is used to control the energy storage device to supply power to the microgrid if no.

[0103] Preferably, the energy storage device control device based on load prediction further includes:

[0104] Model training module;

[0105] The model training module is used to obtain historical operation data of the microgrid and historical load data corresponding to each of the historical operation data; wherein the historical operation data includes: current, voltage, power generation, and weather data;

[0106] Using the historical operation data and the corresponding historical load data as training samples to construct a training data set;

[0107] Construct the initial prediction model of BP neural network structure;

[0108] The initial prediction model is trained using the training data set, and during the training process, the initial prediction model is evaluated using a preset loss function according to an output result of the initial prediction model and the historical load data, and an evaluation result is generated;

[0109] When the evaluation result converges and is minimized, it is determined that the training of the initial prediction model is completed, and the trained initial prediction model is used as the load prediction model.

[0110] Preferably, the energy storage device control device based on load prediction further includes: a model updating module;

[0111] The model updating module is used to obtain the actual load value of the microgrid in a future time period;

[0112] Calculating the error between the load prediction value and the load actual value;

[0113] When the error is greater than a preset error threshold, the real-time operation data and the actual load value are saved as model update training samples in a preset model training database;

[0114] When the number of the model update training samples is greater than a preset number threshold, a number of model update training samples are acquired from the model training database to train the load forecasting model to generate a new load forecasting model.

[0115] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0116] Those skilled in the art can clearly understand that for the sake of convenience and brevity, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0117] Another preferred embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, a method for controlling an energy storage device based on load forecasting as described in any one of the above embodiments is implemented.

[0118] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0119] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, and various interfaces and lines are used to connect various parts of the entire terminal device.

[0120] The memory can be used to store the computer program, and the processor realizes various functions of the terminal device by running or executing the computer program stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Med ia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0121] Another preferred embodiment of the present invention provides a storage medium, the storage medium is a computer-readable storage medium, the computer program is stored in the computer-readable storage medium, and the computer program, when executed by the processor, can implement the steps of the above-mentioned various method embodiments. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0122] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for controlling an energy storage device based on load prediction, characterized in that: include: Obtain real-time operation data and power generation of the microgrid; Inputting the real-time operation data into a preset load prediction model so that the load prediction model predicts the load prediction value of the microgrid within a future time period according to the real-time operation data; According to the power generation and the load forecast value, determining whether the microgrid will have a power surplus in a future time period; If so, controlling the energy storage device to charge and absorb surplus electricity from the microgrid; If not, the energy storage device is controlled to supply power to the microgrid.

2. A method for controlling an energy storage device based on load prediction according to claim 1, characterized in that: Also includes: Acquire historical operation data of the microgrid and historical load data corresponding to each of the historical operation data; wherein the historical operation data includes: current, voltage, and power generation; Using the historical operation data and the corresponding historical load data as training samples to construct a training data set; Construct the initial prediction model of BP neural network structure; The initial prediction model is trained using the training data set, and during the training process, the initial prediction model is evaluated using a preset loss function according to an output result of the initial prediction model and the historical load data, and an evaluation result is generated; When the evaluation result converges and is minimized, it is determined that the training of the initial prediction model is completed, and the trained initial prediction model is used as the load prediction model.

3. A method for controlling an energy storage device based on load prediction as claimed in claim 2, characterized in that: The preset loss function is used to evaluate the initial prediction model according to the output result of the initial prediction model and the historical load data, and an evaluation result is generated, including: The following loss function is used to evaluate the initial prediction model based on the output of the initial prediction model and the historical load data: Among them, LOSS is the evaluation result, n is the total number of training samples in the training data set, and y i is the output result predicted by the initial prediction model based on the historical operation data in the i-th training sample, is the historical load data in the i-th training sample of the initial prediction model.

4. A method for controlling an energy storage device based on load prediction as claimed in claim 1, characterized in that: Also includes: Obtain the actual load value of the microgrid in the future period of time; Calculating the error between the load prediction value and the load actual value; When the error is greater than a preset error threshold, the real-time operation data and the actual load value are saved as model update training samples in a preset model training database; When the number of the model update training samples is greater than a preset number threshold, a number of model update training samples are acquired from the model training database to train the load forecasting model to generate a new load forecasting model.

5. The method for controlling an energy storage device based on load prediction according to claim 1, characterized in that: The determining, based on the power generation and the load forecast value, whether the microgrid will have a power surplus in a future time period includes: comparing the power generation with the load forecast value; When the power generation is greater than the load forecast value, determining that the microgrid will have a power surplus in a future time period; When the power generation is less than the load forecast value, it is determined that the microgrid will have insufficient power supply within a future time period.

6. A control device for an energy storage device based on load prediction, characterized in that: include: Data acquisition module, used to obtain real-time operation data and power generation of the microgrid; A load prediction module, used for inputting the real-time operation data into a preset load prediction model, so that the load prediction model predicts the load prediction value of the microgrid within a future time period according to the real-time operation data; An electricity evaluation module, used to determine whether the microgrid will have an electricity surplus in a future time period according to the power generation and the load forecast value; A first control module, configured to control the energy storage device to charge and absorb surplus electricity from the microgrid; The second control module is used to control the energy storage device to supply power to the microgrid if no.

7. The energy storage device control device based on load prediction according to claim 1, characterized in that: Also includes: Model training module; The model training module is used to obtain historical operation data of the microgrid and historical load data corresponding to each of the historical operation data; wherein the historical operation data includes: current, voltage, power generation, and weather data; Using the historical operation data and the corresponding historical load data as training samples to construct a training data set; Construct the initial prediction model of BP neural network structure; The initial prediction model is trained using the training data set, and during the training process, the initial prediction model is evaluated using a preset loss function according to an output result of the initial prediction model and the historical load data, and an evaluation result is generated; When the evaluation result converges and is minimized, it is determined that the training of the initial prediction model is completed, and the trained initial prediction model is used as the load prediction model.

8. The energy storage device control device based on load prediction according to claim 6, characterized in that: Also includes: Model updating module; The model updating module is used to obtain the actual load value of the microgrid in a future time period; Calculating the error between the load prediction value and the load actual value; When the error is greater than a preset error threshold, the real-time operation data and the actual load value are saved as model update training samples in a preset model training database; When the number of the model update training samples is greater than a preset number threshold, a number of model update training samples are acquired from the model training database to train the load forecasting model to generate a new load forecasting model.

9. A terminal device, characterized in that: It comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, a method for controlling an energy storage device based on load forecasting as described in any one of claims 1 to 5 is implemented.

10. A storage medium, characterized in that: The storage medium includes a stored computer program, wherein when the computer program is executed, the device where the storage medium is located is controlled to execute a method for controlling an energy storage device based on load prediction as described in any one of claims 1 to 5.

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