Power grid load prediction method, device, equipment, medium and program product

Abstract

The invention relates to the technical field of power grids, in particular to a power grid load prediction method, device and equipment, a medium and a program product. The method comprises the following steps: acquiring first power grid load data in a first historical time period, second power grid load data in a second historical time period, third power grid load data in a third historical time period and predicted meteorological data in a prediction time period of each power consumption scene; for each power consumption scene, according to the first power grid load data and the second power grid load data, determining an average load increment of the power consumption scene in the second historical time period compared with the first historical time period; according to the predicted meteorological data, determining a target power consumption scene existing in each unit duration in the prediction time period; determining predicted power grid load data of the prediction time period according to the third power grid load data and the average load increment of the target power consumption scene; the predicted power grid load data obtained based on the above mode has higher accuracy and is more suitable for the actual situation.

Description

Technical Field

[0001] The present disclosure relates to the technical field of power grids, and in particular, to a power grid load forecasting method, device, equipment, medium, and program product. Background Art

[0002] Power grid load forecasting is of great significance in aspects such as economic dispatching, energy management, market operation, power grid planning, and safe operation of power systems.

[0003] In related technologies, the power grid load corresponding to the same historical period of the to-be-forecast time period is used as the power grid load of the to-be-forecast time period.

[0004] However, the power grid load predicted by the above method often differs greatly from the actual situation, and the accuracy is poor. Summary of the Invention

[0005] To solve the above technical problems, the present disclosure provides a power grid load forecasting method, device, equipment, medium, and program product.

[0006] In a first aspect, the present disclosure provides a power grid load forecasting method, including:

[0007] Obtain first power grid load data corresponding to each power consumption scenario in a first historical time period, second power grid load data corresponding to each power consumption scenario in a second historical time period, third power grid load data in a third historical time period, and predicted meteorological data for a prediction time period, where the first historical time period and the third historical time period are consecutive, the first historical time period is the historical same period of the second historical time period, and the third historical time period is the historical same period of the prediction time period; for each power consumption scenario, determine the average load increment of the power consumption scenario in the second historical time period compared with the first historical time period according to the first power grid load data and the second power grid load data; determine the target power consumption scenarios existing in each unit time period in the prediction time period according to the predicted meteorological data, where the target power consumption scenarios are at least one of the power consumption scenarios; determine the predicted power grid load data for the prediction time period according to the third power grid load data and the average load increment of the target power consumption scenarios.

[0008] In some optional embodiments, obtaining the first power grid load data corresponding to each power consumption scenario in the first historical time period and the second power grid load data corresponding to each power consumption scenario in the second historical time period includes:

[0009] Obtain the first historical meteorological data corresponding to the first historical time period, the second historical meteorological data corresponding to the second historical time period, the power grid load data corresponding to the first historical time period, and the power grid load data corresponding to the second historical time period; Combine the first historical meteorological data, and select the first power grid load data that conforms to the electricity consumption characteristics of each electricity consumption scenario from the power grid load data corresponding to the first historical time period; Combine the second historical meteorological data, and select the second power grid load data that conforms to the electricity consumption characteristics of each electricity consumption scenario from the power grid load data corresponding to the second historical time period.

[0010] In some alternative embodiments, for each electricity consumption scenario, determine the average load increment of the electricity consumption scenario in the second historical time period compared to the first historical time period according to the first power grid load data and the second power grid load data, including:

[0011] For each electricity consumption scenario, select the maximum power grid load data within each unit time period from the first power grid load data, and determine the first load average value according to all the maximum power grid load data in the first historical time period; For each electricity consumption scenario, select the maximum power grid load data within each unit time period from the second power grid load data, and determine the second load average value according to all the maximum power grid load data in the second historical time period; Take the difference between the second load average value and the first load average value as the average load increment.

[0012] In some alternative embodiments, according to the predicted meteorological data, determine the target electricity consumption scenarios existing in each unit time period of the prediction time period, including:

[0013] Judge whether the predicted meteorological data corresponding to each unit time period in the prediction time period conforms to the electricity consumption characteristics of the electricity consumption scenario; When it conforms to the electricity consumption characteristics of the electricity consumption scenario, determine the electricity consumption scenario corresponding to the electricity consumption characteristics as the target electricity consumption scenario.

[0014] In some alternative embodiments, according to the third power grid load data and the average load increment of the target electricity consumption scenario, determine the predicted power grid load data of the prediction time period, including:

[0015] According to the third power grid load data corresponding to each unit time period in the prediction time period and the average load increment of the target electricity consumption scenario existing in the corresponding unit time period, determine the predicted power grid load data within the unit time period; According to the predicted power grid load data corresponding to each unit time period in the prediction time period, obtain the predicted power grid load data of the prediction time period.

[0016] In some alternative embodiments, obtain the predicted meteorological data of the prediction time period, including:

[0017] Obtain the third historical meteorological data corresponding to the third historical time period; Input the third historical meteorological data into the pre-trained meteorological prediction model, and output the predicted meteorological data.

[0018] In a second aspect, the present disclosure provides a power grid load prediction device, including:

[0019] An acquisition module, configured to acquire first power grid load data corresponding to each power consumption scenario in a first historical time period, second power grid load data corresponding to each power consumption scenario in a second historical time period, third power grid load data in a third historical time period, and predicted meteorological data for a prediction time period, where the first historical time period and the third historical time period are consecutive, the first historical time period is the historical same period of the second historical time period, and the third historical time period is the historical same period of the prediction time period; a first determination module, configured to, for each power consumption scenario, determine the average load increment of the power consumption scenario in the second historical time period compared to the first historical time period according to the first power grid load data and the second power grid load data; a second determination module, configured to determine target power consumption scenarios existing in each unit time period in the prediction time period according to the predicted meteorological data, where the target power consumption scenarios are at least one of the power consumption scenarios; and a third determination module, configured to determine predicted power grid load data for the prediction time period according to the third power grid load data and the average load increment of the target power consumption scenarios.

[0020] In a third aspect, the present disclosure provides a computer device, including:

[0021] A memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to execute the power grid load prediction method described in the first aspect and any of its embodiments.

[0022] In a fourth aspect, the present disclosure provides a computer-readable storage medium, on which computer instructions are stored, and the computer instructions are used to cause a computer to execute the power grid load prediction method described in the first aspect and any of its embodiments.

[0023] In a fifth aspect, the present disclosure provides a computer program product, including a computer program, and when the computer program is executed by a processor, the steps of the power grid load prediction method described in the first aspect and any of its embodiments are implemented.

[0024] The technical solutions provided by the embodiments of the present disclosure have the following advantages compared with the prior art:

[0025] The power grid load forecasting method provided in this embodiment obtains the first power grid load data in the first historical time period, the second power grid load data in the second historical time period, the third power grid load data in the third historical time period, and the predicted meteorological data in the prediction time period for each power consumption scenario; for each power consumption scenario, according to the first power grid load data and the second power grid load data, determine the average load increment of the power consumption scenario in the second historical time period compared with the first historical time period; according to the predicted meteorological data, determine the target power consumption scenarios existing in each unit time period in the prediction time period; according to the third power grid load data and the average load increment of the target power consumption scenario, determine the predicted power grid load data in the prediction time period; through the above method, this embodiment obtains the average load increment of each power consumption scenario, so that the predicted power grid load data obtained based on the average load increment and the third power grid load data is more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The accompanying drawings herein are incorporated into and constitute a part of this specification, showing embodiments consistent with the present disclosure and, together with the specification, are used to explain the principles of the present disclosure.

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure or the prior art, the following will briefly introduce the accompanying drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative efforts.

[0028] Figure 1 It is a flowchart of the power grid load forecasting method provided in the embodiment of the present disclosure;

[0029] Figure 2 It is a flowchart of obtaining the first power grid load data and the second power grid load data provided in the embodiment of the present disclosure;

[0030] Figure 3 It is a flowchart of determining the average load increment provided in the embodiment of the present disclosure;

[0031] Figure 4 It is a structural connection diagram of the power grid load forecasting device provided in the embodiment of the present disclosure;

[0032] Figure 5 It is a structural connection diagram of the computer device provided in the embodiment of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0033] In order to better understand the above objects, features and advantages of the present disclosure, the solutions of the present disclosure will be further described below. It should be noted that, without conflict, the embodiments of the present disclosure and the features in the embodiments can be combined with each other.

[0034] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure, but the present disclosure may be practiced in other ways different from those described herein; obviously, the embodiments in the specification are only a part of the embodiments of the present disclosure, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts fall within the scope of protection of the present invention.

[0035] It should be noted that, in this text, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising a..." does not exclude the existence of additional identical elements in the process, method, article or device including the element.

[0036] According to an embodiment of the present invention, an embodiment of a power grid load forecasting method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0037] In this embodiment, a power grid load forecasting method is provided, which can be used in a power grid load forecasting device. Figure 1 is a flowchart of the power grid load forecasting method according to an embodiment of the present invention, as Figure 1 shown, the process includes the following steps:

[0038] S101, obtain first power grid load data corresponding to each power consumption scenario in a first historical time period, second power grid load data corresponding to each power consumption scenario in a second historical time period, third power grid load data in a third historical time period, and predicted meteorological data for a prediction time period.

[0039] Among them, the first historical time period is the historical same period of the second historical time period, and the third historical time period is the historical same period of the prediction time period. The first historical time period is continuous with the third historical time period. Therefore, the second historical time period is continuous with the prediction time period. The prediction time period is a future time period. It should also be added that the first historical time period and the third historical time period have the highest similarity in historical meteorological data with the meteorological data corresponding to the second historical time period and the prediction time period among all historical same periods of the second historical time period and the prediction time period. Each electricity consumption scenario refers to scenarios corresponding to various common electricity consumption natures, such as residential life scenarios, residential air-conditioning usage scenarios, agricultural irrigation scenarios, industrial scenarios, commercial scenarios, etc.

[0040] Specifically, the grid load prediction device obtains the first grid load data of each electricity consumption scenario in the first historical time period, the second grid load data of each electricity consumption scenario in the second historical time period, and the third grid load data corresponding to the third historical time period from the power system or other power data management platforms. The grid load prediction device obtains the predicted meteorological data corresponding to the prediction time period from the output of the meteorological prediction platform or the meteorological prediction model. It should be noted that the grid load prediction method provided in this disclosure is carried out based on a certain area, which can be a certain administrative area or a pre-selected area, or a sub-area after grid division of a large area. In addition, it should also be noted that the third grid load data is all grid load data within the third historical time period.

[0041] In an optional embodiment, the acquisition process of the first grid load data and the second grid load data is optimized, such as Figure 2 shown, including the following steps:

[0042] S201, obtain the first historical meteorological data corresponding to the first historical time period, the second historical meteorological data corresponding to the second historical time period, the grid load data corresponding to the first historical time period, and the grid load data corresponding to the second historical time period.

[0043] Specifically, obtain the historical meteorological data corresponding to the first historical time period (i.e., the first historical meteorological data) from the meteorological management platform, and obtain the historical meteorological data corresponding to the second historical time period (i.e., the second historical meteorological data). At the same time, obtain the grid load data within the first historical time period and the grid load data within the second historical time period from the power system. The meteorological data at least includes the maximum temperature, the minimum temperature, and the precipitation situation, etc. In this embodiment, the minimum statistical unit of the historical meteorological data can be a day. The minimum statistical unit of the grid load data is a moment, such as an hour or a minute, etc.

[0044] Exemplarily, if the first historical time period is from January 2020 to September 2020, and the second historical time period is from January 2024 to September 2024. Obtain the meteorological data of each day in the period from January 2020 to September 2020 (i.e., the first historical meteorological data) and the meteorological data of each day in the period from January 2024 to September 2024 (i.e., the second historical meteorological data) from the meteorological management platform. Obtain the grid load data of each moment of each day in the period from January 2020 to September 2020 and the grid load data of each moment of each day in the period from January 2024 to September 2024 from the power system.

[0045] S202. Combine the first historical meteorological data, and select the first grid load data that conforms to the electricity consumption characteristics of each electricity consumption scenario from the grid load data corresponding to the first historical time period.

[0046] Among them, each electricity consumption scenario has corresponding electricity consumption characteristics, and the electricity consumption characteristics include at least one of meteorological characteristics and time period characteristics. For example, the electricity consumption characteristics corresponding to the residential life scenario are that the daily temperature is in the range of [15°C, 25°C] and the centralized electricity consumption period is from 20:00 to 23:00. For example, the electricity consumption characteristics corresponding to the residential air conditioner usage scenario are that the daily temperature is higher than 38°C and the centralized electricity consumption period is from 20:00 to 22:00. For example, the electricity consumption characteristics of the agricultural irrigation scenario are that there is no precipitation for more than 7 consecutive days, the daily temperature is higher than 35°C, the centralized electricity consumption time is from 6:00 to 10:00 and from 16:00 to 20:00. For example, the electricity consumption characteristics of the industrial scenario are that the centralized electricity consumption time is from 10:00 to 12:00 and from 14:00 to 16:00. For example, the electricity consumption characteristics of the commercial electricity consumption scenario are from 8:00 to 9:00 on Friday to Sunday evenings.

[0047] Specifically, according to the first historical meteorological data, screen out the time period corresponding to the first historical meteorological data that conforms to the meteorological characteristics in the electricity consumption characteristics from the first historical time period. Then, screen out the grid load data that conforms to the time period characteristics in the electricity consumption characteristics from the grid load data corresponding to this time period, and determine the screened grid load data as the first grid load data.

[0048] Exemplarily, taking the agricultural irrigation scenario as an example, according to the first historical meteorological data, screen out the time period with no precipitation for more than 7 consecutive days and a daily temperature higher than 35°C from the first historical time period. Then, screen out the grid load data corresponding to the time periods such as 6:00 - 10:00 and 16:00 - 20:00 from the grid load data corresponding to this time period, and use the screened grid load data as the first grid load data corresponding to the agricultural irrigation scenario in the first historical time period.

[0049] S203. Combine the second historical meteorological data, and select the second grid load data that conforms to the electricity consumption characteristics of each electricity consumption scenario from the grid load data corresponding to the second historical time period.

[0050] Specifically, according to the second historical meteorological data, the time periods corresponding to the second historical meteorological data that meet the meteorological characteristics in the electricity consumption characteristics are screened out from the second historical time period. Then, the grid load data that meet the time period characteristics in the electricity consumption characteristics are screened out from the grid load data corresponding to this time period, and the screened grid load data are determined as the second grid load data.

[0051] Exemplarily, taking the industrial scenario as an example, since the electricity consumption characteristics of the industrial scenario are independent of the meteorological data, it can be understood that all the second historical meteorological data meet the electricity consumption characteristics of the industrial scenario. Therefore, the grid load data within two time periods, such as 10:00 - 12:00 and 14:00 - 16:00, can be selected from the grid load data corresponding to the second historical time period as the second grid load data.

[0052] The acquisition methods of the first grid load data and the second grid load data provided in this embodiment, due to combining the electricity consumption characteristics of the electricity consumption scenario and the historical meteorological data of the first historical time period and the second historical time period, make the obtained first grid load data and second grid load data more accurate.

[0053] In an alternative embodiment, the acquisition method of the predicted meteorological data is optimized to obtain the predicted meteorological data for the prediction time period, including: obtaining the third historical meteorological data corresponding to the third historical time period; inputting the third historical meteorological data into a pre-trained meteorological prediction model to output the predicted meteorological data.

[0054] Specifically, since the third historical time period is a period of time before and continuous with the prediction time period, in this embodiment, the historical meteorological data corresponding to the third historical time period (i.e., the third historical meteorological data) is first obtained. Then, the third historical meteorological data is input into the pre-trained meteorological prediction model. After the analysis and calculation of the meteorological prediction model, the predicted meteorological data corresponding to the prediction time period is obtained. The meteorological prediction model can be a Numerical Weather Prediction (NWP) model or an intelligent machine model based on deep learning. This embodiment does not limit the selection of the meteorological prediction model.

[0055] Step S102, for each electricity consumption scenario, determine the average load increment of the electricity consumption scenario in the second historical time period compared to the first historical time period according to the first grid load data and the second grid load data.

[0056] Specifically, for each of multiple power consumption scenarios, calculate the mean value of the first grid load data corresponding to the power consumption scenario, calculate the mean value of the second grid load data corresponding to the power consumption scenario, and take the difference between the mean value of the second grid load data and the mean value of the first grid load data as the average load increment of the power consumption scenario in the second historical period compared with the first historical period.

[0057] In an alternative embodiment, the method for determining the average load increment is optimized. As Figure 3 shown, the method for determining the average load increment includes the following steps:

[0058] S301. For each power consumption scenario, select the maximum grid load data within each unit time period from the first grid load data, and determine the first load mean value based on all the maximum grid load data in the first historical period.

[0059] Exemplarily, taking the agricultural irrigation scenario as an example, if there are n days in the first historical period that conform to the power consumption characteristics of the agricultural irrigation scenario. Select the maximum grid load data corresponding to each "irrigation day" from the first grid load data corresponding to the n days. Calculate the average value of the n maximum grid load data, and take this average value as the first load mean value. The "day" in this embodiment is the unit time period.

[0060] S302. For each power consumption scenario, select the maximum grid load data within each unit time period from the second grid load data, and determine the second load mean value based on all the maximum grid load data in the second historical period.

[0061] Exemplarily, still taking the agricultural irrigation scenario as an example, similar to the calculation method of the first load mean value, if there are m days in the second historical period that conform to the power consumption characteristics of the agricultural irrigation scenario. Select the maximum grid load data corresponding to each "irrigation day" from the second grid load data corresponding to the m days. Calculate the average value of the m maximum grid load data, and take this average value as the second load mean value.

[0062] S303. Take the difference between the second load mean value and the first load mean value as the average load increment.

[0063] Exemplarily, still taking the agricultural irrigation scenario as an example, calculate the difference between the second load mean value and the first load mean value, and take this difference as the average load increment of the agricultural irrigation scenario in the second historical period compared with the first historical period. This average load increment corresponds to the acquisition unit of the grid load data.

[0064] Step S103. According to the predicted meteorological data, determine the target power consumption scenarios existing within each unit time period in the predicted time period.

[0065] Among them, the target power consumption scenario is at least one of the power consumption scenarios.

[0066] Specifically, it is determined whether the predicted meteorological data corresponding to each unit time period in the prediction time period conforms to the power consumption characteristics of the power consumption scenario; when it conforms to the power consumption characteristics of the power consumption scenario, the power consumption scenario corresponding to the power consumption characteristics is determined as the target power consumption scenario. It should be noted that there can be one or more target power consumption scenarios within a unit time period.

[0067] Exemplarily, for example, the prediction time period is from October 1, 2024 to October 8, 2024, the unit time period is 1 day, the predicted meteorological data corresponding to October 1, 2024 is obtained, and it is determined whether the predicted meteorological data conforms to the power consumption characteristics corresponding to the residential life scenario. When the predicted meteorological data corresponding to October 1, 2024 conforms to the power consumption characteristics corresponding to the residential life scenario, the residential life scenario is determined as the target power consumption scenario existing on October 1, 2024. Similarly, it can continue to be determined whether the predicted meteorological data corresponding to October 1, 2024 conforms to the power consumption characteristics of other power consumption scenarios, and the conforming power consumption scenarios are determined as the target power consumption scenarios existing on October 1, 2024. In chronological order, the target power consumption scenarios existing in each subsequent day are determined in turn until the above judgments are completed for all unit time periods in the prediction time period.

[0068] S104. Determine the predicted power grid load data for the prediction time period according to the third power grid load data and the average load increment of the target power consumption scenario.

[0069] Among them, the predicted power grid load data for the prediction time period refers to the set of the predicted power grid load data corresponding to each unit time period in the prediction time period.

[0070] Specifically, according to the third power grid load data corresponding to each unit time period in the prediction time period and the average load increment of the target power consumption scenario existing within the corresponding unit time period, the predicted power grid load data for the unit time period is determined; according to the predicted power grid load data corresponding to each unit time period in the prediction time period, the predicted power grid load data for the prediction time period is obtained. It should also be noted that when calculating the predicted power grid load data for the unit time period, the average load increment corresponding to the power consumption scenario can be added to the third power grid load data for the corresponding time period (i.e., the time period in the third historical time period corresponding to the concentrated power consumption time period) according to the concentrated power consumption time periods of the power consumption scenarios existing in the unit time period. In this way, more accurate predicted power grid load data can be obtained to guide users in scheduling work at the time period dimension.

[0071] Exemplarily, if the prediction time period is from October 1, 2024 to October 8, 2024, and the third historical time period is from October 1, 2020 to October 8, 2020. Taking October 1, 2024 as an example, through S103, it is determined that there are three electricity consumption scenarios on October 1, 2024, namely the residential life scenario, the industrial scenario, and the commercial scenario. Adding the average load increment corresponding to the residential life scenario, the average load increment corresponding to the industrial scenario, and the average load increment corresponding to the commercial scenario to the third power grid load data of October 1, 2020, the predicted power grid load data corresponding to October 1, 2024 can be obtained. According to the above calculation method, the predicted power grid load data corresponding to each unit time length within the prediction time period can be determined in sequence. Finally, the predicted power grid load data corresponding to each unit time length within the prediction time period is used as the predicted power grid load data for the prediction time period.

[0072] The power grid load prediction method provided in this embodiment obtains the first power grid load data of each electricity consumption scenario in the first historical time period, the second power grid load data in the second historical time period, the third power grid load data in the third historical time period, and the predicted meteorological data of the prediction time period; for each electricity consumption scenario, according to the first power grid load data and the second power grid load data, determine the average load increment of the electricity consumption scenario in the second historical time period compared to the first historical time period; according to the predicted meteorological data, determine the target electricity consumption scenarios existing in each unit time length within the prediction time period; according to the third power grid load data and the average load increment of the target electricity consumption scenario, determine the predicted power grid load data of the prediction time period; through the above method, this embodiment obtains the average load increment of each electricity consumption scenario, so that the predicted power grid load data obtained based on the average load increment and the third power grid load data is more accurate.

[0073] In this embodiment, a power grid load prediction device is also provided. This device is used to implement the above embodiment and the preferred implementation manners, and those that have been described will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that realizes a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0074] This embodiment provides a power grid load prediction device, as Figure 4 shown, including:

[0075] An acquisition module 401, configured to acquire first grid load data corresponding to each power consumption scenario in a first historical time period, second grid load data corresponding to each power consumption scenario in a second historical time period, third grid load data corresponding to each power consumption scenario in a third historical time period, and predicted meteorological data for a prediction time period, wherein the first historical time period is consecutive with the third historical time period, the first historical time period is the historical same period of the second historical time period, and the third historical time period is the historical same period of the prediction time period.

[0076] A first determination module 402, configured to, for each power consumption scenario, determine an average load increment of the power consumption scenario in the second historical time period compared to the first historical time period according to the first grid load data and the second grid load data.

[0077] A second determination module 403, configured to determine target power consumption scenarios existing in each unit time period in the prediction time period according to the predicted meteorological data, where the target power consumption scenarios are at least one of the power consumption scenarios.

[0078] A third determination module 404, configured to determine predicted grid load data for the prediction time period according to the third grid load data and the average load increment of the target power consumption scenarios.

[0079] In some alternative embodiments, the acquisition module 401 includes:

[0080] A first acquisition sub-module, configured to acquire first historical meteorological data corresponding to the first historical time period, second historical meteorological data corresponding to the second historical time period, grid load data corresponding to the first historical time period, and grid load data corresponding to the second historical time period; a first selection sub-module, configured to combine the first historical meteorological data and select first grid load data that conforms to the power consumption characteristics of each power consumption scenario from the grid load data corresponding to the first historical time period; a second selection sub-module, configured to combine the second historical meteorological data and select second grid load data that conforms to the power consumption characteristics of each power consumption scenario from the grid load data corresponding to the second historical time period.

[0081] In some alternative embodiments, the first determination module 402 includes:

[0082] A first determination sub-module, configured to, for each power consumption scenario, select the maximum grid load data within each unit time period from the first grid load data and determine a first load average according to all the maximum grid load data in the first historical time period; a second determination sub-module, configured to, for each power consumption scenario, select the maximum grid load data within each unit time period from the second grid load data and determine a second load average according to all the maximum grid load data in the second historical time period; a first calculation sub-module, configured to use the difference between the second load average and the first load average as the average load increment.

[0083] In some alternative embodiments, the second determination module 403 includes:

[0084] A judgment sub-module, configured to judge whether the predicted meteorological data corresponding to each unit time period in the prediction time period conforms to the electricity consumption characteristics of the electricity consumption scenario; a third determination sub-module, configured to, when the electricity consumption characteristics conform to the electricity consumption scenario, determine the electricity consumption scenario corresponding to the electricity consumption characteristics as the target electricity consumption scenario.

[0085] In some alternative embodiments, the third determination module 404 includes:

[0086] A second calculation sub-module, configured to determine the predicted grid load data within a unit time period according to the third grid load data corresponding to each unit time period in the prediction time period and the average load increment of the target electricity consumption scenario existing within the corresponding unit time period; a fourth determination sub-module, configured to obtain the predicted grid load data of the prediction time period according to the predicted grid load data corresponding to each unit time period in the prediction time period.

[0087] In some alternative embodiments, the acquisition module 401 includes:

[0088] A second acquisition sub-module, configured to acquire the third historical meteorological data corresponding to the third historical time period; a prediction sub-module, configured to input the third historical meteorological data into a pre-trained meteorological prediction model and output the predicted meteorological data.

[0089] The further function descriptions of the above-mentioned various modules and units are the same as those in the corresponding foregoing embodiments, and will not be elaborated herein.

[0090] The grid load prediction device in this embodiment is presented in the form of functional units. Here, the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and a memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0091] An embodiment of the present invention further provides a computer device having the above-mentioned Figure 4 shown grid load prediction device.

[0092] Please refer to Figure 5 , Figure 5 which is a schematic structural diagram of a computer device provided by an alternative embodiment of the present invention. As shown in Figure 5As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting the components, including a high-speed interface and a low-speed interface. Each component communicates with each other using different buses and can be installed on a common motherboard or in other ways as needed. The processor can process instructions executed within the computer device, including instructions stored in the memory or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some alternative embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (such as an array of servers, a set of blade servers, or a multi-processor system). Figure 5 In Figure 5 , a processor 10 is taken as an example.

[0093] The processor 10 can be a central processing unit, a network processor, or a combination thereof. Among them, the processor 10 can further include a hardware chip. The above hardware chip can be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The above programmable logic device can be a complex programmable logic device, a field programmable gate array, a generic array logic, or any combination thereof.

[0094] Among them, the memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiments.

[0095] The memory 20 can include a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created according to the use of the computer device, etc. In addition, the memory 20 can include a high-speed random access memory, and can also include a non-transitory memory, such as at least one disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some alternative embodiments, the memory 20 can optionally include a memory remotely set relative to the processor 10, and these remote memories can be connected to the computer device through a network. Examples of the above network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0096] The memory 20 can include a volatile memory, such as a random access memory; the memory can also include a non-volatile memory, such as a flash memory, a hard disk, or a solid-state drive; the memory 20 can also include a combination of the above types of memories.

[0097] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or communication networks.

[0098] Embodiments of the present invention also provide a computer-readable storage medium. The method according to the embodiments of the present invention can be implemented in hardware, firmware, or be implemented as computer code that can be recorded on a storage medium, or be implemented as computer code originally stored in a remote storage medium or a non-transitory machine-readable storage medium and to be downloaded through a network and stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code, and when the software or computer code is accessed and executed by the computer, the processor, or the hardware, the method shown in the above embodiments is implemented.

[0099] In addition to the above computer devices and computer-readable storage media, embodiments of the present application can also be computer program products, which include computer program instructions that cause a processor to execute the steps of the sound source localization method provided in any embodiment of the present application when the computer program instructions are run by the processor.

[0100] The computer program product can be written in any combination of one or more programming languages to write program code for performing the operations of the embodiments of the present application. The programming languages include object-oriented programming languages, such as Java, C++, etc., and also include conventional procedural programming languages, such as the "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, executed as an independent software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0101] The above are only specific embodiments of the present disclosure, enabling those skilled in the art to understand or implement the present disclosure. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to these embodiments herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for predicting power grid load, characterized in that: include: Obtaining first power grid load data corresponding to each power usage scenario in a first historical time period, second power grid load data corresponding to each power usage scenario in a second historical time period, third power grid load data in a third historical time period, and forecasted meteorological data in a forecasted time period, wherein the first historical time period is continuous with the third historical time period, the first historical time period is the same historical period as the second historical time period, and the third historical time period is the same historical period as the forecasted time period; For each of the power usage scenarios, determining an average load increment of the power usage scenario in the second historical time period compared to the first historical time period according to the first power grid load data and the second power grid load data; Determine, according to the predicted meteorological data, a target power usage scenario existing in each unit time in the predicted time period, wherein the target power usage scenario is at least one of the power usage scenarios; The predicted power grid load data for the predicted time period is determined according to the third power grid load data and the average load increment of the target power usage scenario.

2. The method according to claim 1, characterized in that Acquiring first power grid load data corresponding to each power usage scenario in a first historical time period and second power grid load data corresponding to each power usage scenario in a second historical time period, including: Acquire first historical meteorological data corresponding to the first historical time period, second historical meteorological data corresponding to the second historical time period, power grid load data corresponding to the first historical time period, and power grid load data corresponding to the second historical time period; In combination with the first historical meteorological data, selecting first power grid load data that meets the power consumption characteristics of each power consumption scenario from the power grid load data corresponding to the first historical time period; In combination with the second historical meteorological data, second power grid load data that meets the power consumption characteristics of each of the power consumption scenarios is selected from the power grid load data corresponding to the second historical time period.

3. The method according to claim 1 or 2, characterized in that: For each of the power usage scenarios, determining, according to the first power grid load data and the second power grid load data, an average load increment of the power usage scenario in the second historical time period compared with the first historical time period, includes: For each of the electricity usage scenarios, select the maximum grid load data within each unit time length from the first grid load data, and determine a first load average value according to all the maximum grid load data in the first historical time period; For each of the power usage scenarios, select the maximum power grid load data within each unit time length from the second power grid load data, and determine the second load mean value according to all the maximum power grid load data in the second historical time period; The difference between the second load average value and the first load average value is taken as the average load increment.

4. The method according to claim 1 or 2, characterized in that: The step of determining, based on the predicted meteorological data, a target electricity usage scenario existing in each unit time period in the predicted time period includes: Determine whether the predicted meteorological data corresponding to each unit time in the predicted time period conforms to the power consumption characteristics of the power consumption scenario; When the power usage characteristics of the power usage scenario are met, the power usage scenario corresponding to the power usage characteristics is determined as the target power usage scenario.

5. The method according to claim 1, characterized in that The step of determining the predicted power grid load data for the predicted time period according to the third power grid load data and the average load increment of the target power usage scenario includes: Determine the predicted grid load data within the unit time period according to the third grid load data corresponding to each unit time period in the predicted time period and the average load increment of the target power usage scenario existing within the corresponding unit time period; The predicted power grid load data for the predicted time period is obtained according to the predicted power grid load data corresponding to each unit time length in the predicted time period.

6. The method according to claim 1, characterized in that Get the forecast weather data for the forecast period, including: Acquire third historical meteorological data corresponding to the third historical time period; The third historical meteorological data is input into a pre-trained meteorological forecasting model, and the forecasted meteorological data is output.

7. A power grid load prediction device, characterized in that: include: an acquisition module, used to acquire first power grid load data corresponding to each power usage scenario in a first historical time period, second power grid load data corresponding to each power usage scenario in a second historical time period, third power grid load data in a third historical time period, and predicted meteorological data in a predicted time period, wherein the first historical time period is continuous with the third historical time period, the first historical time period is the same historical period as the second historical time period, and the third historical time period is the same historical period as the predicted time period; A first determination module is used to determine, for each of the power usage scenarios, an average load increment of the power usage scenario in the second historical time period compared with the first historical time period according to the first power grid load data and the second power grid load data; A second determination module is used to determine, according to the predicted meteorological data, a target power usage scenario existing in each unit time length in the predicted time period, wherein the target power usage scenario is at least one of the power usage scenarios; The third determination module is used to determine the predicted power grid load data for the predicted time period according to the third power grid load data and the average load increment of the target power usage scenario.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the power grid load forecasting method according to any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the power grid load forecasting method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the power grid load forecasting method according to any one of claims 1 to 6 are implemented.

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