Park power load prediction method and device, electronic equipment and storage medium

Abstract

The invention is suitable for the technical field of electric power, and provides a park power load prediction method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining the power load of a park and historical data of a plurality of meteorological factors; performing correlation analysis on the power load and each meteorological factor by using historical data; selecting a target meteorological factor from a plurality of meteorological factors according to a principle of strongest correlation; using the target meteorological factor as an independent variable and the power load as a dependent variable, and using historical data to construct a power load prediction model; and performing prediction by using the power load prediction model to obtain a power load prediction value.

Description

Technical Field

[0001] The present application relates to the field of electric power, and in particular to a method and device for predicting power load in a park, an electronic device, and a computer-readable storage medium. Background Art

[0002] Smart parks build an industrial service ecosystem by integrating information and intelligence, improving services for enterprises and industries and promoting industrial agglomeration. Since it is an ecosystem, supporting measures must be complete, such as water, electricity, air conditioning, fire protection, charging piles, etc. Among these supporting facilities, the power system is at the core.

[0003] The total load allocated to the park from the national grid is fixed, but the power consumption of various facilities within the park needs to be allocated by itself, so that the park can manage and utilize energy resources more efficiently, reduce energy costs, and improve energy efficiency. The power load forecasting of the park is of great significance for power system scheduling and power generation planning, and high-accuracy power load forecasting is still a problem to be solved. Summary of the invention

[0004] The embodiments of the present application provide a park power load prediction method and device, an electronic device and a computer-readable storage medium, which can solve the problem in the related art that the accuracy of park power load prediction needs to be improved.

[0005] In the first aspect, an embodiment of the present application provides a method for predicting power load in a park, the method comprising: obtaining historical data of the power load in the park and various meteorological factors; using the historical data to perform correlation analysis between the power load and various meteorological factors; selecting a target meteorological factor from multiple meteorological factors according to the principle of the strongest correlation; using the target meteorological factor as an independent variable and the power load as a dependent variable, and using historical data to construct a power load prediction model; and using the power load prediction model to predict and obtain a power load prediction value.

[0006] In the second aspect, an embodiment of the present application provides a device for predicting power load in a park, which includes: an acquisition module for acquiring historical data of the power load in the park and various meteorological factors; a calculation module for using historical data to perform correlation analysis on the power load and various meteorological factors; a selection module for selecting a target meteorological factor from multiple meteorological factors according to the principle of the strongest correlation; a construction module for using historical data to construct a power load prediction model with the target meteorological factor as an independent variable and the power load as a dependent variable; and a prediction module for using the power load prediction model to predict and obtain a power load prediction value.

[0007] In a third aspect, an embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable by the processor. When the processor executes the computer program, the park power load forecasting method described in the first aspect above is implemented.

[0008] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the park power load forecasting method described in the first aspect above.

[0009] In a fifth aspect, an embodiment of the present application provides a computer program product. When the computer program product is run on an electronic device, the electronic device executes the campus power load forecasting method described in the first aspect above.

[0010] Compared with the prior art, the embodiments of the present application have the following beneficial effects: by obtaining historical data of the power load of the park and various meteorological factors; using the historical data to perform correlation analysis on the power load and various meteorological factors; selecting the target meteorological factor from the various meteorological factors according to the principle of the strongest correlation; using the target meteorological factor as the independent variable and the power load as the dependent variable, and using historical data to construct a power load prediction model; using the power load prediction model to predict and obtain the power load prediction value, meteorological factors are introduced in the process of constructing the power load prediction model, and the meteorological factors are screened based on the correlation, thereby removing the influence of meteorological factors with weak correlation, so that the accuracy of the constructed prediction model is significantly improved, and the accuracy of the power load prediction is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0012] Figure 1 is a structural schematic diagram of an electronic device provided by an embodiment of the present application; Figure 2 It is a flow chart of a method for predicting power load in a park provided in an embodiment of the present application; Figure 3 It is a schematic diagram of the MIC coefficients of the power load and various meteorological factors in each month in a specific example of the present application; Figure 4 It is a flow chart of a method for predicting power load in a park provided in a specific embodiment of the present application; Figure 5It is a structural schematic diagram of a park power load prediction device provided in one embodiment of the present application. DETAILED DESCRIPTION In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0013] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0014] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0015] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0016] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0017] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0018] The park power load forecasting method provided in the embodiment of the present application can be applied to electronic devices, including but not limited to servers, server clusters, mobile phones, tablet computers, laptop computers, desktop computers, personal digital assistants, wearable devices and other electronic devices with computing functions. The embodiment of the present application does not impose any restrictions on the specific type of electronic devices.

[0019] Figure 1 FIG. 1 is a block diagram of a partial structure of an electronic device provided in an embodiment of the present application. Figure 1 , the electronic device includes: a processor 10, a memory 20, a bus 30, an input device 40, an output device 50 and a communication device 60. The processor 10 and the memory 20 are connected to each other through the bus 30, and the input device 40, the output device 50 and the communication device 60 are also connected to the bus 30. Those skilled in the art can understand that Figure 1 The structure of the electronic device shown in the figure does not constitute a limitation of the electronic device, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0020] Combine the following Figure 1 A detailed introduction to the various components of electronic equipment: The processor 10 is the control center of the electronic device, and can run the program stored in the memory 20 to perform various functions and process data. The processor 10 can be a central processing unit (CPU), and the processor 10 can also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. In some embodiments, the processor 10 may include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

[0021] The memory 20 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as program codes of computer programs. The memory 20 can also be used to temporarily store data required for executing the program and generated. The memory 20 may include a high-speed random access memory, and may also include a non-volatile memory, such as a flash memory, a hard disk, a multimedia card, a card-type memory, etc. The memory 20 may include a storage unit disposed inside the electronic device, such as a hard disk of the electronic device, and / or a removable external storage unit, such as a mobile hard disk, a USB flash drive, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, etc.

[0022] The input device 40 may include at least one of a keyboard, a mouse, a touch panel, a joystick, etc., and is used to collect user input operations to generate corresponding operation instructions.

[0023] The output device 50 is used to output information to be provided to the user. The output device 50 generally includes a display, and optionally, a liquid crystal display (LCD), an organic light-emitting diode (OLED), etc. can be used. In addition, the output device can further include a speaker.

[0024] The communication device 60 may include a modem, a network card, etc., for establishing a network connection with other electronic devices and communicating with each other.

[0025] The park power load forecasting method provided in the embodiment of the present application can be implemented as a computer software program. For example, the embodiment of the present application provides a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device 60, and / or installed from a removable external storage unit. When the computer program is executed by the processor 10, the various functions defined in the park power load forecasting method provided in the embodiment of the present application are implemented.

[0026] Figure 2 A schematic flow chart of a campus power load prediction method provided in an embodiment of the present application is shown. As an example but not a limitation, the method can be applied to the above-mentioned electronic device.

[0027] S1: Obtain historical data of the park’s power load and various meteorological factors.

[0028] The power load may include the load of the infrastructure in the park, such as lighting, power supply for basic equipment (such as computers, servers, elevators, etc.), power supply for air conditioning, etc.

[0029] Meteorological factors include at least two of the following: temperature, humidity, wind speed, rainfall, sunshine hours, perceived temperature, and humidity index. Among them, temperature, humidity, wind speed, rainfall, and sunshine hours can be directly obtained from the meteorological data of the park location provided by the meteorological website, while perceived temperature and humidity index need to be further calculated. The specific calculation formula is as follows.

[0030] The apparent temperature is defined as follows:

[0031] Where T e is the air temperature (℉), WCI is the wind cold index, and HI is the heat index.

[0032] In the above formula, the WCI calculation formula is as follows:

[0033] Where W s is the wind speed (mph).

[0034] The HI formula is as follows:

[0035] Where R H is the relative humidity (%).

[0036] The calculation formula of humidity index is as follows:

[0037] Where T e is the air temperature (℃), T d is the dew point temperature (℃).

[0038] In historical data, power load and various meteorological factors are generally recorded in units of a smaller time period (such as one day), and the historical data of power load / various meteorological factors form a time series.

[0039] A time series is an ordered sequence of variables at different time points. For convenience, we generally assume that adjacent elements in a sequence have the same time interval. A time series can generally be divided into three parts: trend part, cycle part, and residual part. The trend part refers to the trend of continuous development and change over a long period of time. The cycle part can also be called the seasonal part, which refers to the part with periodic regular changes. The residual part refers to the unexplained part of the rest, which is generally caused by accidental factors.

[0040] The time series of historical data of power load is affected by many factors. The trend part is affected by social and economic factors, and generally shows an increasing trend with the development of social and economic development; the seasonal part is affected by meteorology and shows obvious periodicity, that is, the seasonal part has the strongest correlation with meteorological factors. Therefore, when building a model for predicting power load based on meteorological factors, the historical data of power load can be processed first, and the seasonal part can be separated to build a prediction model.

[0041] Specifically, the historical data of power load is filtered to retain the part related to meteorological factors. The historical data of power load is decomposed into time series, and the seasonal part thereof is retained as the part related to meteorological factors.

[0042] Since the cyclical change of power load is not affected by the change trend, the additive model is generally used for decomposition. The additive model means that the time series is represented by the sum of its parts. The specific method of time series decomposition is not limited here, for example, it can be a moving average, Holt-Winters algorithm, seasonal and trend decomposition using Loess (STL) using local weighted smoothing (LOESS), etc.

[0043] Taking the STL algorithm as an example, its calculation process consists of two parts: an inner loop and an outer loop. The inner loop is used to update the trend part and the cycle part. After each inner loop, the outer loop calculates the robust weight, thereby reducing the impact of outliers on the trend part and the cycle part in the next inner loop.

[0044] S2: Use historical data to conduct correlation analysis between power load and various meteorological factors.

[0045] For each meteorological factor, a correlation analysis is performed based on the historical data of the meteorological factor and the historical data of the power load, and specifically, the correlation coefficient between the meteorological factor and the power load can be calculated. The correlation coefficient is used to quantitatively analyze the correlation between two groups of variables, and its specific type is not limited here, for example, it can be a Pearson correlation coefficient, a maximum information coefficient, etc.

[0046] Taking the maximum information coefficient (MIC) as an example, this method mainly uses mutual information (MI) and grid division for calculation. Mutual information is an indicator to measure the degree of correlation between variables. For a given variable A=[a i， i = 1, 2, ..., n] and B = [b i， i=1, 2, ..., n], n is the number of samples, and the mutual information between the two is:

[0047] Where p(a, b) is the joint probability density of A and B; p(a) and p(b) are the marginal probability densities of A and B respectively.

[0048] S3: Select the target meteorological factor from multiple meteorological factors according to the principle of strongest correlation.

[0049] Specifically, the meteorological factors can be arranged in order from strong to weak correlation represented by the correlation coefficient. For example, when the larger the correlation coefficient, the stronger the correlation, the meteorological factors can be arranged in order from large to small correlation coefficients. The first n meteorological factors are selected from the sorted meteorological factors as target meteorological factors, where n is a positive integer and is the maximum number of specified target meteorological factors. Alternatively, meteorological factors with correlation coefficients greater than a preset threshold can be used as target meteorological factors. Alternatively, the above two methods can be combined to select up to n meteorological factors as target meteorological factors from the meteorological factors with correlation coefficients greater than the preset threshold in order from large to small correlation coefficients.

[0050] S4: Taking the target meteorological factor as the independent variable and the power load as the dependent variable, the power load forecasting model is constructed using historical data.

[0051] The output of the power load forecasting model includes the target meteorological factor, and the output includes the power load. The specific type of the power load forecasting model and the corresponding construction method is not limited here. For example, a linear model can be obtained by linear regression, or a more complex machine learning model can be used, such as a decision tree, Bayesian learning, neural network, etc.

[0052] S5: Use the power load forecasting model to predict and obtain the power load forecast value.

[0053] Specifically, the target meteorological factor of the time to be predicted (generally obtained according to the weather forecast) can be input into the power load prediction model, and the model outputs the power load prediction value.

[0054] In the above-mentioned time series decomposition, since the change cycle of meteorological data itself is generally annual, accordingly, the time series decomposition of historical data of power load with a time length greater than or equal to 1 year is more effective, the obtained periodic part is more accurate, and the correlation analysis with meteorological factors is better. However, in different time periods of 1 year or more, the correlation between power load and the same meteorological factor may change, resulting in different meteorological factors to be selected in different time periods according to the same principle. Using complete historical data for calculation, the target meteorological factor obtained is the one with the strongest overall correlation with power load in the complete time length, which does not mean that it is the same as the meteorological factor that should be selected in a specific time period.

[0055] To solve the above problems, the historical data of power load and various meteorological factors can be divided into multiple sub-historical data according to time periods. The length of the time period is less than the change cycle of the meteorological factor (i.e., year). For example, the unit of the time period can be month or quarter. If the duration of the historical data is greater than 1 year, the parts belonging to the same time period can be divided into the same sub-historical data. That is to say, assuming that the unit of the time period is month, for any historical data with a duration greater than or equal to 1 year, the sub-historical data obtained by division are 12.

[0056] Execute S2-S4 for each sub-historical data to obtain the power load forecasting model for each time period. When making a forecast, it is necessary to determine the time period corresponding to the time to be predicted, input the target meteorological factor of the time to be predicted into the power load forecasting model of the corresponding time period, and obtain the power load forecast value. In this way, while ensuring the accuracy of the separation of the historical data of the power load, independent power load forecasting models are constructed for different time periods, and the corresponding model is selected for forecasting during forecasting, thereby further improving the accuracy of the power load forecasting.

[0057] For example, historical data of power load and seven meteorological factors, including temperature, humidity, wind speed, rainfall, sunshine hours, perceived temperature, and humidity index, are obtained for the whole year of 2023. The historical data of power load is decomposed into time series using STL, and the periodic part is retained; the historical data of power load and meteorological factors are then divided into 12 parts by month, and the MIC coefficient of power load and each meteorological factor is calculated for each historical data. The meteorological factor with a MIC coefficient greater than the set threshold (taken as 0.3) is selected as the target meteorological factor. The MIC coefficients of power load and each meteorological factor are as follows: Figure 3As shown, the highlighted MIC coefficient is greater than the set threshold, indicating that the corresponding meteorological factor of the corresponding month is the target meteorological factor. It can be seen that the target meteorological factors of different months will change. Then, the target meteorological factor is used as the independent variable and the power load is used as the dependent variable. Based on the historical data of each month, a linear regression algorithm is used to build a power load forecasting model for each month. When forecasting, the power load forecasting model of the month of the day to be predicted is selected and the target meteorological factor used for the model prediction is determined. The target meteorological factor is obtained according to the weather forecast data of the day to be predicted and input into the model to obtain the power load forecast value of the day to be predicted.

[0058] In addition, the power load can be predicted based on the trend part obtained by decomposing the time series to obtain the power load trend correction. The prediction here is simpler than the prediction based on the periodic part mentioned above, and can generally be expressed by a single simple function, the independent variable of the function is time, and the dependent variable is the power load trend correction. After using the model built based on the periodic part to predict and obtain the power load forecast value, the power load trend correction can be used to correct the power load forecast value. Specifically, the sum of the power load trend correction and the power load forecast value can be calculated as the corrected power load forecast value.

[0059] According to the power load forecast value, the power of the park can be regulated. For example, combined with the total load allocated from the power grid, the power allocated to auxiliary facilities such as charging piles and green space sprinklers can be determined, thereby reducing energy waste without affecting the power supply of infrastructure.

[0060] Through the implementation of this embodiment, the historical data of the power load and various meteorological factors of the park are obtained; the historical data are used to perform correlation analysis on the power load and various meteorological factors; the target meteorological factor is selected from the various meteorological factors according to the principle of the strongest correlation; the target meteorological factor is used as the independent variable and the power load is used as the dependent variable, and the power load prediction model is constructed using historical data; the power load prediction model is used to predict the power load prediction value, and meteorological factors are introduced in the process of constructing the power load prediction model, and the meteorological factors are screened based on the correlation, thereby removing the influence of meteorological factors with weak correlation, so that the accuracy of the constructed prediction model is significantly improved, and the accuracy of the power load prediction is improved.

[0061] The specific process of the park power load forecasting method is explained below with examples in conjunction with the accompanying drawings.

[0062] like Figure 4 As shown, in a specific embodiment of the present application, the park power load prediction method includes the following parts. This embodiment is a further extension of the above embodiment, and the same parts are not repeated here.

[0063] S10: Determine the time range of historical data.

[0064] The length of the time range is generally greater than or equal to 1 year.

[0065] S11: Obtain the historical data of the power load of the park within the time range.

[0066] The power load in this embodiment is the power load of the infrastructure.

[0067] S12: Decompose the historical data of power load into time series and retain the periodic part.

[0068] S13: Obtain the temperature, humidity, wind speed, rainfall, and sunshine hours of the park location within the time range from the meteorological website.

[0069] S14: The perceived temperature is calculated based on the air temperature, wind speed and humidity, and the hygrothermal index is calculated based on the air temperature and dew point temperature, to obtain historical data of seven meteorological factors.

[0070] There is no restriction on the execution order between S11-S12 and S13-14 S15: Taking months as the unit, the historical data of power load and seven meteorological factors are divided into 12 sub-historical data.

[0071] S16: Use sub-historical data to perform correlation analysis between power load and various meteorological factors.

[0072] S17: Select the target meteorological factor from multiple meteorological factors according to the principle of strongest correlation.

[0073] The target meteorological factors calculated from the sub-historical data of different months may be different.

[0074] S18: Taking the target meteorological factor as the independent variable and the power load as the dependent variable, the power load forecasting model for the corresponding month is constructed using historical data.

[0075] S16-S18 are executed for each sub-historical data (the part in the dotted line frame in the figure) to obtain the power load forecast model for each month. Months, sub-historical data and power load forecast models correspond one to one.

[0076] S19: Determine the corresponding power load forecasting model and the target meteorological factors required according to the month in which the date to be predicted falls.

[0077] S20: Obtain target meteorological factors according to weather forecast data of the date to be predicted and input them into the power load prediction model to obtain the power load prediction value of the day to be predicted.

[0078] S21: Regulate the power supply in the park on the forecast day according to the forecast value of power load.

[0079] Figure 5 A schematic diagram of the structure of a park power load prediction device provided in an embodiment of the present application is shown. The park power load prediction device includes an acquisition module 11, a calculation module 12, a selection module 13, a construction module 14 and a prediction module 15.

[0080] The acquisition module 11 is used to obtain the historical data of the power load of the park and various meteorological factors.

[0081] The calculation module 12 is used to perform correlation analysis between the power load and various meteorological factors using historical data.

[0082] The selection module 13 is used to select a target meteorological factor from multiple meteorological factors according to the principle of the strongest correlation.

[0083] The construction module 14 is used to construct a power load prediction model using historical data, taking the target meteorological factor as an independent variable and the power load as a dependent variable.

[0084] The prediction module 15 is used to use the power load prediction model to predict and obtain a power load prediction value.

[0085] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / modules / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0086] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0087] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0088] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0089] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, 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. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the camera / electronic device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), electric carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0090] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0091] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0092] In the embodiments provided in the present application, it should be understood that the disclosed devices / network equipment and methods can be implemented in other ways. For example, the device / network equipment embodiments described above are merely schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0093] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0094] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for predicting power load in a park, characterized in that: The method comprises: Obtain historical data on the park's power load and various meteorological factors; Performing a correlation analysis between the power load and each of the meteorological factors using the historical data; Selecting a target meteorological factor from the plurality of meteorological factors according to the principle of strongest correlation; Taking the target meteorological factor as an independent variable and the power load as a dependent variable, constructing a power load forecasting model using the historical data; The power load prediction model is used to perform prediction to obtain a power load prediction value.

2. The method according to claim 1, characterized in that Before using the historical data to perform correlation analysis on the power load and each of the meteorological factors, the following step is further included: The historical data of the electric power load is filtered to retain a portion related to the meteorological factor.

3. The method according to claim 2, characterized in that The filtering of the historical data of the power load to retain the portion related to the meteorological factor includes: The historical data of the power load is decomposed into time series, and the seasonal part thereof is retained as the part related to the meteorological factor.

4. The method according to claim 3, characterized in that Also includes: Predicting the power load according to the trend part obtained by decomposing the time series to obtain a power load trend correction amount; The power load forecast value is corrected using the power load trend correction amount.

5. The method according to claim 2, characterized in that After filtering the historical data of the power load to retain the part related to the meteorological factor, the method further includes: Dividing the historical data of the power load and the plurality of meteorological factors into a plurality of sub-historical data according to time periods; The process of performing the correlation analysis between the power load and each of the meteorological factors using the historical data for each of the sub-historical data; selecting a target meteorological factor from the multiple meteorological factors according to the principle of the strongest correlation; taking the target meteorological factor as the independent variable and the power load as the dependent variable, and using the historical data to construct a power load forecasting model, thereby obtaining a power load forecasting model for each of the time periods.

6. The method according to claim 5, characterized in that The using the power load prediction model to predict and obtain a power load prediction value comprises: The target meteorological factor of the time to be predicted is input into the power load prediction model of the corresponding time period to obtain the power load prediction value.

7. The method according to any one of claims 1 to 5, characterized in that The meteorological factors include at least two of air temperature, humidity, wind speed, rainfall, sunshine hours, perceived temperature and humidity index.

8. A park power load forecasting device, characterized in that: The device comprises: The acquisition module is used to obtain the historical data of the park's power load and various meteorological factors; A calculation module, used for performing a correlation analysis between the power load and each of the meteorological factors using the historical data; A selection module, for selecting a target meteorological factor from the plurality of meteorological factors according to the principle of strongest correlation; A construction module, for taking the target meteorological factor as an independent variable, the power load as a dependent variable, and using the historical data to construct a power load forecasting model; The prediction module is used to use the power load prediction model to predict and obtain a power load prediction value.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable by the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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