Power grid load increment prediction method and device, equipment and storage medium

Abstract

The invention relates to a power grid load increment prediction method and device, equipment and a storage medium. The day-by-day increment of the power grid load is predicted according to the human body comfort of the area where the power grid is located, the human body comfort change rule and the maximum load value change rule of the power grid, the prediction difficulty of the power grid load increment can be greatly reduced, and the prediction accuracy of the power grid load increment is improved.

Description

Technical Field

[0001] The present disclosure relates to the field of power technologies, and in particular, to a method, device, equipment, and storage medium for predicting the increment of grid load. Background Art

[0002] Currently, grid load is mainly related to climatic conditions and social and economic activities. For example, high-temperature weather prompts individuals and commercial buildings to generally use air conditioners and refrigeration equipment to maintain indoor comfort, resulting in an increase in grid load; for another example, changes in climate characteristics cause frequent occurrences of consecutive sunny and hot weather, leading to a straight rise in the perceived temperature and exacerbating the increase in grid load.

[0003] Currently, due to the lack of sufficient historical data for analysis and the continuous change in the annual load level of the power grid, it is difficult to predict the increment of grid load, and the prediction error is relatively large. Summary of the Invention

[0004] To solve the above technical problems, the present disclosure provides a method, device, equipment, and storage medium for predicting the increment of grid load.

[0005] The first aspect of the present disclosure provides a method for predicting the increment of grid load, including:

[0006] Obtaining the human comfort level on the m-th day in the area where the power grid is located and the predicted value of the human comfort level on the (m + 1)-th day, and obtaining the change rule of the human comfort level within a preset number of days before the m-th day in the area where the power grid is located and the change rule of the maximum load value of the power grid within the preset number of days, where m is a positive integer;

[0007] Calculating the difference between the predicted value of the human comfort level on the (m + 1)-th day and the human comfort level on the m-th day to obtain the change value of the human comfort level on the (m + 1)-th day compared to the m-th day;

[0008] Multiplying the predicted value of the human comfort level on the (m + 1)-th day by the change value of the human comfort level on the (m + 1)-th day compared to the m-th day to obtain the increment index of the human comfort level on the (m + 1)-th day;

[0009] Calculating the target coefficient corresponding to the area where the power grid is located based on the change rule of the human comfort level and the change rule of the maximum load value;

[0010] Predicting the increment of the grid load on the (m + 1)-th day compared to the m-th day based on the increment index of the human comfort level on the (m + 1)-th day, the change value of the human comfort level on the (m + 1)-th day compared to the m-th day, and the target coefficient corresponding to the area where the power grid is located.

[0011] The second aspect of the present disclosure provides a device for predicting the increment of grid load, including:

[0012] An acquisition module, configured to acquire the human comfort level on the m-th day and the predicted value of the human comfort level on the (m + 1)-th day in the area where the power grid is located, and acquire the variation law of the human comfort level within a preset number of days before the m-th day and the variation law of the maximum load value of the power grid within the preset number of days, where m is a positive integer;

[0013] A first calculation module, configured to calculate the difference between the predicted value of the human comfort level on the (m + 1)-th day and the human comfort level on the m-th day, to obtain the variation value of the human comfort level on the (m + 1)-th day compared to the m-th day;

[0014] A multiplication module, configured to multiply the predicted value of the human comfort level on the (m + 1)-th day by the variation value of the human comfort level on the (m + 1)-th day compared to the m-th day, to obtain the human comfort level increment index on the (m + 1)-th day;

[0015] A second calculation module, configured to calculate the target coefficient corresponding to the area where the power grid is located based on the variation law of the human comfort level and the variation law of the maximum load value;

[0016] A prediction module, configured to predict the power grid load increment on the (m + 1)-th day compared to the m-th day based on the human comfort level increment index on the (m + 1)-th day, the variation value of the human comfort level on the (m + 1)-th day compared to the m-th day, and the target coefficient corresponding to the area where the power grid is located.

[0017] A third aspect of the present disclosure provides a computer device, including a memory and a processor. Among them, a computer program is stored in the memory. When the computer program is executed by the processor, the power grid load increment prediction method in the first aspect above can be implemented.

[0018] A fourth aspect of the present disclosure provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by the processor, the power grid load increment prediction method in the first aspect above can be implemented.

[0019] The technical solution provided by the present disclosure has the following advantages compared with the prior art:

[0020] The present disclosure obtains the human comfort level on the m-th day and the predicted value of the human comfort level on the (m + 1)-th day in the area where the power grid is located, and obtains the variation law of the human comfort level within a preset number of days before the m-th day and the variation law of the maximum load value of the power grid within the preset number of days, where m is a positive integer; calculates the difference between the predicted value of the human comfort level on the (m + 1)-th day and the human comfort level on the m-th day to obtain the change value of the human comfort level on the (m + 1)-th day compared to the m-th day; multiplies the predicted value of the human comfort level on the (m + 1)-th day by the change value of the human comfort level on the (m + 1)-th day compared to the m-th day to obtain the human comfort level increment index on the (m + 1)-th day; calculates the target coefficient corresponding to the area where the power grid is located based on the variation law of the human comfort level and the variation law of the maximum load value; predicts the power grid load increment on the (m + 1)-th day compared to the m-th day based on the human comfort level increment index on the (m + 1)-th day, the change value of the human comfort level on the (m + 1)-th day compared to the m-th day, and the target coefficient corresponding to the area where the power grid is located. The present disclosure can predict the daily increment of the power grid load according to the human comfort level, the variation law of the human comfort level, and the variation law of the maximum load value of the area where the power grid is located, which can greatly reduce the prediction difficulty of the power grid load increment and improve the accuracy of the power grid load increment prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] 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.

[0022] To more clearly illustrate the technical solutions in the embodiments of the present disclosure or in the prior art, the following will briefly introduce the 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 be obtained based on these drawings without creative efforts.

[0023] Figure 1 is a flowchart of a method for predicting the power grid load increment provided by an embodiment of the present disclosure;

[0024] Figure 2 is a flowchart of a method for calculating the human comfort level provided by an embodiment of the present disclosure;

[0025] Figure 3 is a flowchart of a method for predicting the power grid load increment provided by an embodiment of the present disclosure;

[0026] Figure 4 is a schematic structural diagram of a device for predicting the power grid load increment provided by an embodiment of the present disclosure;

[0027] Figure 5 is a schematic structural diagram of a computer device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] In order to more clearly understand the above-mentioned 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 may be combined with each other.

[0029] In the following description, many specific details are set forth in order to fully understand the present disclosure, but the present disclosure may also be implemented 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 of the embodiments.

[0030] It should be understood that the various steps recorded in the method implementation manners of the present disclosure may be executed in different orders and / or executed in parallel. In addition, the method implementation manners may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this regard.

[0031] It should be noted that, in this document, 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 such 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 "including one..." does not exclude the existence of additional identical elements in the process, method, article or device including the said element.

[0032] It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative rather than restrictive. Those skilled in the art should understand that, unless otherwise clearly specified in the context, it should be understood as "one or more".

[0033] In order to better understand the inventive concept of the embodiments of the present disclosure, the technical solutions of the embodiments of the present disclosure will be described below in conjunction with exemplary embodiments.

[0034] Figure 1 is a flowchart of a method for predicting the increment of power grid load provided by an embodiment of the present disclosure. This method can be executed by a computer device, such as Figure 1 As shown, the method for predicting the increment of power grid load provided in this embodiment may include the following steps:

[0035] Step 110: Obtain the human comfort level on the m-th day and the predicted value of the human comfort level on the (m + 1)-th day in the area where the power grid is located, and obtain the variation law of the human comfort level within a preset number of days before the m-th day and the variation law of the maximum load value of the power grid within the preset number of days. Here, m is a positive integer.

[0036] In the embodiments of the present disclosure, the human comfort level can be understood as the degree of comfort of the human body in the weather in the area where the power grid is located. The higher the human comfort level, the more comfortable the human body feels; the lower the human comfort level, the more uncomfortable the human body feels.

[0037] The m-th day can be today or any day other than today, which is not limited here. m is a positive integer.

[0038] For example, when the m-th day is today, the (m + 1)-th day is tomorrow.

[0039] The computer device can obtain the human comfort level on the m-th day in the area where the power grid is located and the predicted value of the human comfort level on the (m + 1)-th day in the area where the power grid is located.

[0040] The preset number of days can be set as needed, such as 3 days, 7 days, etc., which is not limited here.

[0041] The computer device can obtain the variation law of the human comfort level within the preset number of days before the m-th day in the area where the power grid is located, and the variation law of the maximum load value of the power grid within the preset number of days.

[0042] Step 120: Calculate the difference between the predicted value of the human comfort level on the (m + 1)-th day and the human comfort level on the m-th day to obtain the change value of the human comfort level on the (m + 1)-th day compared to the m-th day.

[0043] For example, the change value D of the human comfort level on the (m + 1)-th day compared to the m-th day m+1 can be calculated by Equation (1):

[0044] D m+1 = S m+1 - S m ; (1);

[0045] where S m represents the human comfort level on the m-th day in the area where the power grid is located;

[0046] S m+1 represents the predicted value of the human comfort level on the (m + 1)-th day in the area where the power grid is located.

[0047] Step 130: Multiply the predicted value of the human comfort level on the (m + 1)-th day by the change value of the human comfort level on the (m + 1)-th day compared to the m-th day to obtain the increment index of the human comfort level on the (m + 1)-th day.

[0048] For example, the human comfort increment index Z on the (m + 1)-th day m+1 can be calculated by Equation (2):

[0049] Z m+1 = S m+1 · D m+1 ; (2);

[0050] Step 140: Calculate the target coefficient corresponding to the area where the power grid is located based on the variation law of human comfort and the variation law of the maximum load value.

[0051] Step 150: Predict the power grid load increment on the (m + 1)-th day compared with the m-th day based on the human comfort increment index on the (m + 1)-th day, the change value of human comfort on the (m + 1)-th day compared with the m-th day, and the target coefficient corresponding to the area where the power grid is located.

[0052] In the embodiments of the present disclosure, the power grid load increment can be understood as the change amount of the power grid load.

[0053] The computer device can predict the power grid load increment on the (m + 1)-th day compared with the m-th day based on the human comfort increment index on the (m + 1)-th day, the change value of human comfort on the (m + 1)-th day compared with the m-th day, and the target coefficient corresponding to the area where the power grid is located.

[0054] For example, the power grid load increment Y on the (m + 1)-th day compared with the m-th day m+1 can be calculated by Equation (3):

[0055] Y m+1 = (α + βZ m+1 + γZ 2 m+1 + θZ 3 m+1 )D m+1 ; (3);

[0056] where α, β, γ, and θ are the target coefficients corresponding to the area where the power grid is located, respectively.

[0057] Therefore, according to the human comfort in the area where the power grid is located, the variation law of human comfort, and the variation law of the maximum load value of the power grid, the daily increment of the power grid load can be predicted, which can greatly reduce the prediction difficulty of the power grid load increment and improve the accuracy of the power grid load increment prediction.

[0058] In some embodiments of the present disclosure, for obtaining the human comfort on the m-th day and the predicted value of human comfort on the (m + 1)-th day in the area where the power grid is located, the computer device can execute Figure 2 the flowchart of a human comfort calculation method provided, as Figure 2 shown. The human comfort calculation method provided in this embodiment may include the following steps:

[0059] Step 210: Obtain the maximum temperature value, average humidity value, and average wind speed value of the area where the power grid is located on the m-th day, and obtain the predicted maximum temperature value, predicted average humidity value, and predicted average wind speed value of the area where the power grid is located on the (m + 1)-th day.

[0060] The maximum temperature value can be understood as the maximum temperature value in a day.

[0061] The average humidity value can be understood as the average of multiple humidity values in a day.

[0062] The average wind speed value can be understood as the average of multiple wind speed values in a day.

[0063] The computer device can obtain the temperature values at multiple time points in the m-th day in the area where the power grid is located through the temperature measurement device in the area where the power grid is located, and then determine the maximum temperature value among the temperature values at multiple time points in the m-th day as the maximum temperature value of the m-th day.

[0064] The computer device can obtain the humidity values at multiple time points in the m-th day in the area where the power grid is located through the humidity measurement device in the area where the power grid is located, and then determine the average of the humidity values at multiple time points in the m-th day as the average humidity value of the m-th day.

[0065] The computer device can obtain the wind speed values at multiple time points in the m-th day in the area where the power grid is located through the wind speed measurement device in the area where the power grid is located, and then determine the average of the wind speed values at multiple time points in the m-th day as the average wind speed value of the m-th day.

[0066] The computer device can predict the maximum temperature in the area where the power grid is located on the (m + 1)-th day based on the maximum temperature value on the m-th day and the maximum temperature values of each day within the target number of days before the m-th day, and obtain the predicted maximum temperature value of the area where the power grid is located on the (m + 1)-th day.

[0067] The computer device can predict the average humidity value in the area where the power grid is located on the (m + 1)-th day based on the average humidity value on the m-th day and the average humidity values of each day within the target number of days before the m-th day, and obtain the predicted average humidity value of the area where the power grid is located on the (m + 1)-th day.

[0068] The computer device can predict the average wind speed value in the area where the power grid is located on the (m + 1)-th day based on the average wind speed value on the m-th day and the average wind speed values of each day within the target number of days before the m-th day, and obtain the predicted average wind speed value of the area where the power grid is located on the (m + 1)-th day.

[0069] Step 220: Calculate the human comfort level on the m-th day based on the maximum temperature value, average humidity value, and average wind speed value on the m-th day.

[0070] For example, a computer device can calculate the human comfort level S of the area where the power grid is located on the m-th day through Equation (4). m :

[0071] S m =(1.818T m +18.18)(0.88 + 0.002F m )+(T m -32) / (45 - T m ) - 3.2V m +18.2; (4);

[0073] wherein, T m represents the maximum temperature value of the area where the power grid is located on the m-th day; F m represents the average humidity value of the area where the power grid is located on the m-th day; V m represents the average wind speed value of the area where the power grid is located on the m-th day.

[0074] Step 230: Based on the predicted maximum temperature value, predicted average humidity value, and predicted average wind speed value on the (m + 1)-th day, calculate the predicted human comfort level value on the (m + 1)-th day.

[0075] For example, a computer device can calculate the predicted human comfort level value S of the area where the power grid is located on the (m + 1)-th day through Equation (5). m+1 :

[0076] S m+1 =(1.818T m+1 +18.18)(0.88 + 0.002F m+1 )+(T m+1 -32) / (45 - T m+1 ) - 3.2V m+1 +18.2; (5);

[0078] wherein, T m+1 represents the predicted maximum temperature value of the area where the power grid is located on the (m + 1)-th day; F m+1 represents the predicted average humidity value of the area where the power grid is located on the (m + 1)-th day; V m+1 represents the predicted average wind speed value of the area where the power grid is located on the (m + 1)-th day.

[0079] Thus, the human comfort level can be determined according to the maximum temperature value, average humidity value, and average wind speed value of the area where the power grid is located.

[0080] Figure 3 is a flowchart of a power grid load increment prediction method provided by an embodiment of the present disclosure. This method can be executed by a computer device, such as Figure 3As shown in the figure, the power grid load increment prediction method provided in this embodiment may include the following steps:

[0081] Step 301: Obtain the human comfort level on the m-th day and the predicted value of the human comfort level on the (m + 1)-th day in the area where the power grid is located, and obtain the change law of the human comfort level within a preset number of days before the m-th day and the change law of the maximum load value of the power grid within the preset number of days. m is a positive integer.

[0082] Step 302: Calculate the difference between the predicted value of the human comfort level on the (m + 1)-th day and the human comfort level on the m-th day to obtain the change value of the human comfort level on the (m + 1)-th day compared to the m-th day.

[0083] Step 303: Multiply the predicted value of the human comfort level on the (m + 1)-th day by the change value of the human comfort level on the (m + 1)-th day compared to the m-th day to obtain the human comfort level increment index on the (m + 1)-th day.

[0084] Step 304: Within the preset number of days before the m-th day, obtain the human comfort level of the area where the power grid is located every day within the preset number of days.

[0085] Step 305: For the n-th day within the preset number of days, calculate the difference between the human comfort level on the n-th day and the human comfort level on the (n - 1)-th day to obtain the change value of the human comfort level corresponding to the n-th day. n is a positive integer.

[0086] Step 306: Multiply the human comfort level on the n-th day by the change value of the human comfort level corresponding to the n-th day to obtain the human comfort level increment index on the n-th day.

[0087] Step 307: Determine the human comfort level increment index of each day within the preset number of days as the change law of the human comfort level in the area where the power grid is located within the preset number of days before the m-th day.

[0088] Step 308: Within the preset number of days before the m-th day, obtain the maximum load value of the power grid every day within the preset number of days.

[0089] Step 309: For the n-th day within the preset number of days, calculate the difference between the maximum load value of the power grid on the n-th day and the maximum load value of the power grid on the (n - 1)-th day to obtain the change value of the maximum load of the power grid on the n-th day compared to the (n - 1)-th day.

[0090] For example, the change value G of the maximum load of the power grid on the n-th day compared to the (n - 1)-th day n can be calculated by Equation (6):

[0091] G n = L n - L n-1 ; (6);

[0092] where Ln represents the maximum load value of the power grid on the nth day;

[0093] L n-1 represents the maximum load value of the power grid on the (n - 1)th day.

[0094] Step 310: Determine the variation law of the maximum load value of the power grid within a preset number of days as the variation law of the maximum load value of the power grid within the preset number of days.

[0095] Step 311: For the nth day within the preset number of days, calculate the ratio between the variation value of the maximum load of the power grid on the nth day compared to the (n - 1)th day and the variation value of human comfort on the nth day compared to the (n - 1)th day to obtain the target ratio corresponding to the nth day.

[0096] For example, the target ratio r corresponding to the nth day n can be calculated by Equation (7):

[0097] r n = G n / D n-1 ; (7);

[0098] where D n-1 represents the variation value of human comfort on the nth day compared to the (n - 1)th day.

[0099] Step 312: Perform polynomial fitting on the target ratio corresponding to each day within the preset number of days and the human comfort increment index for each day to obtain the fitting formula between the target ratio and the human comfort increment index.

[0100] Step 313: Solve the fitting formula to obtain the target coefficient corresponding to the area where the power grid is located.

[0101] For example, the fitting formula between the target ratio and the human comfort increment index can be Equation (8):

[0102] r i = (α + βZ i + γZ 2 i + θZ 3 i )D i , (i = 1, 2, 3,..., n); (8);

[0103] where ri represents the target ratio corresponding to each day within the preset number of days;

[0104] Zi represents the human comfort increment index corresponding to each day within the preset number of days;

[0105] α, β, γ, and θ are the target coefficients corresponding to the area where the power grid is located respectively;

[0106] By solving Equation (8), the values of the target coefficients α, β, γ, and θ corresponding to the area where the power grid is located can be obtained.

[0107] Step 314: Based on the human comfort increment index on the (m + 1)-th day, the change value of human comfort from the m-th day to the (m + 1)-th day, and the target coefficients corresponding to the area where the power grid is located, predict the power grid load increment from the m-th day to the (m + 1)-th day.

[0108] Thus, according to the human comfort in the area where the power grid is located, the change law of human comfort, and the change law of the maximum load value of the power grid, the daily increment of the power grid load can be predicted, which can greatly reduce the prediction difficulty of the power grid load increment and improve the accuracy of the power grid load increment prediction.

[0109] Figure 4 It is a schematic structural diagram of a power grid load increment prediction device provided by an embodiment of the present disclosure. This device can be understood as the above computer device or some functional modules in the above computer device. As Figure 4 shown, the power grid load increment prediction device 400 includes:

[0110] An acquisition module 410, configured to acquire the human comfort on the m-th day and the predicted value of human comfort on the (m + 1)-th day in the area where the power grid is located, and acquire the change law of human comfort within a preset number of days before the m-th day in the area where the power grid is located and the change law of the maximum load value of the power grid within the preset number of days, where m is a positive integer;

[0111] A first calculation module 420, configured to calculate the difference between the predicted value of human comfort on the (m + 1)-th day and the human comfort on the m-th day, to obtain the change value of human comfort from the m-th day to the (m + 1)-th day;

[0112] A multiplication module 430, configured to multiply the predicted value of human comfort on the (m + 1)-th day by the change value of human comfort from the m-th day to the (m + 1)-th day, to obtain the human comfort increment index on the (m + 1)-th day;

[0113] A second calculation module 440, configured to calculate the target coefficients corresponding to the area where the power grid is located based on the change law of human comfort and the change law of the maximum load value;

[0114] A prediction module 450, configured to predict the power grid load increment from the m-th day to the (m + 1)-th day based on the human comfort increment index on the (m + 1)-th day, the change value of human comfort from the m-th day to the (m + 1)-th day, and the target coefficients corresponding to the area where the power grid is located.

[0115] Optionally, the above acquisition module includes:

[0116] The first acquisition sub-module is used to acquire the maximum temperature value, average humidity value, and average wind speed value of the area where the power grid is located on the m-th day, and to acquire the predicted maximum temperature value, predicted average humidity value, and predicted average wind speed value of the area where the power grid is located on the (m + 1)-th day;

[0117] The first calculation sub-module is used to calculate the human comfort level on the m-th day based on the maximum temperature value, average humidity value, and average wind speed value on the m-th day;

[0118] The second calculation sub-module is used to calculate the predicted human comfort level prediction value on the (m + 1)-th day based on the predicted maximum temperature value, predicted average humidity value, and predicted average wind speed value on the (m + 1)-th day.

[0119] Optionally, the above acquisition module includes:

[0120] The second acquisition sub-module is used to acquire the human comfort level of each day within the preset number of days in the area where the power grid is located;

[0121] The third calculation sub-module is used to calculate the difference between the human comfort level on the n-th day and the human comfort level on the (n - 1)-th day within the preset number of days, to obtain the human comfort level change value corresponding to the n-th day, where n is a positive integer;

[0122] The multiplication sub-module is used to multiply the human comfort level on the n-th day by the human comfort level change value corresponding to the n-th day to obtain the human comfort level increment index on the n-th day;

[0123] The first determination sub-module is used to determine the human comfort level increment index of each day within the preset number of days as the human comfort level change rule within the preset number of days before the m-th day in the area where the power grid is located;

[0124] The third acquisition sub-module is used to acquire the maximum load value of the power grid every day within the preset number of days before the m-th day;

[0125] The fourth calculation sub-module is used to calculate the difference between the maximum load value of the power grid on the n-th day and the maximum load value of the power grid on the (n - 1)-th day within the preset number of days, to obtain the maximum load change value of the power grid on the n-th day compared to the (n - 1)-th day;

[0126] The second determination sub-module is used to determine the maximum load change value of the power grid every day within the preset number of days as the maximum load value change rule of the power grid within the preset number of days.

[0127] Optionally, the above second calculation module includes:

[0128] The fifth calculation sub-module is configured to calculate, for the nth day within the preset number of days, the ratio between the maximum load change value of the power grid on the nth day compared to the (n - 1)th day and the change value of human comfort on the nth day compared to the (n - 1)th day, so as to obtain the target ratio corresponding to the nth day;

[0129] The fitting sub-module is configured to perform polynomial fitting on the target ratio corresponding to each day within the preset number of days and the human comfort increment index of each day, so as to obtain a fitting formula between the target ratio and the human comfort increment index;

[0130] The solving sub-module is configured to solve the fitting formula to obtain the target coefficient corresponding to the area where the power grid is located.

[0131] The power grid load increment prediction device provided by the embodiments of the present disclosure can implement the method of any of the above embodiments, and its execution manner and beneficial effects are similar, which will not be elaborated here.

[0132] The embodiments of the present disclosure further provide a computer device, which includes a processor and a memory. Among them, a computer program is stored in the memory, and when the computer program is executed by the processor, the method of any of the above embodiments can be implemented, and its execution manner and beneficial effects are similar, which will not be elaborated here.

[0133] The computer device in the embodiments of the present disclosure can be understood as any device with processing and computing capabilities, and this device may include, but is not limited to, mobile terminals such as smart phones, laptop computers, personal digital assistants (PDAs), tablet computers (PADs), wearable devices, etc., and fixed electronic devices such as digital TVs, desktop computers, etc.

[0134] Figure 5 is a schematic structural diagram of a computer device provided by the embodiments of the present disclosure. As Figure 5 shown, the computer device 500 may include a processor 510 and a memory 520. Among them, a computer program 521 is stored in the memory 520, and when the computer program 521 is executed by the processor 510, the method provided by any of the above embodiments can be implemented, and its execution manner and beneficial effects are similar, which will not be elaborated here.

[0135] Of course, for simplicity, Figure 5 only some of the components related to the present invention in the computer device 500 are shown in

[0136] An embodiment of the present disclosure provides a computer-readable storage medium. A computer program is stored in the storage medium. When the computer program is executed by a processor, the methods of any of the above embodiments can be implemented, and their execution manners and beneficial effects are similar and will not be elaborated here.

[0137] The above computer-readable storage medium may adopt any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may include, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the readable storage medium include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0138] The above computer program 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 disclosure. 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's computer device, partially on the user device, executed as an independent software package, partially on the user's computer device and partially on a remote computer device, or entirely on a remote computer device or server.

[0139] The above description is only a preferred embodiment of the present disclosure and an explanation of the applied technical principle. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by the specific combination of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosure concept. For example, a technical solution formed by mutually replacing the above features with (but not limited to) technical features having similar functions disclosed in the present disclosure.

[0140] In addition, although the operations are depicted in a particular order, this should not be construed as requiring that the operations be performed in the particular order shown or in a sequential order. In certain environments, multitasking and parallel processing may be advantageous. Similarly, although several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the present disclosure. Certain features that are described in the context of separate embodiments may also be implemented in combination in a single embodiment. Conversely, the various features that are described in the context of a single embodiment may also be implemented separately or in any suitable sub-combination in multiple embodiments.

[0141] The foregoing 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 readily apparent to those skilled in the art, and the general principles defined herein may 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 the embodiments described herein, but rather will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for predicting power grid load increment, characterized in that: include: Obtain the human comfort level of the area where the power grid is located on the mth day and the predicted value of human comfort level on the m+1th day, and obtain the variation law of human comfort level of the area where the power grid is located within a preset number of days before the mth day and the variation law of the maximum load value of the power grid within the preset number of days, where m is a positive integer; Calculate the difference between the predicted value of human comfort on the m+1th day and the human comfort on the mth day to obtain a change value of human comfort on the m+1th day compared with the mth day; The predicted value of human comfort on the m+1th day is multiplied by the change value of human comfort on the m+1th day compared with the mth day to obtain the human comfort increment index on the m+1th day; Calculating a target coefficient corresponding to the area where the power grid is located based on the variation law of the human body comfort and the variation law of the maximum load value; Based on the human comfort increment index of the m+1th day, the human comfort change value of the m+1th day compared with the mth day, and the target coefficient corresponding to the area where the power grid is located, the power grid load increment of the m+1th day compared with the mth day is predicted.

2. The method according to claim 1, characterized in that The obtaining of the human comfort level of the area where the power grid is located on the mth day and the predicted value of human comfort level on the m+1th day includes: Obtain the maximum temperature value, average humidity value and average wind speed value of the area where the power grid is located on the mth day, and obtain the maximum temperature forecast value, average humidity forecast value and average wind speed forecast value of the area where the power grid is located on the m+1th day; Calculate the human comfort level on the mth day based on the maximum temperature value, average humidity value and average wind speed value on the mth day; Based on the maximum temperature prediction value, the average humidity prediction value and the average wind speed prediction value of the m+1th day, the predicted human comfort prediction value of the m+1th day is calculated.

3. The method according to claim 1, characterized in that The obtaining of a change rule of human comfort level in the area where the power grid is located within a preset number of days before the mth day and a change rule of a maximum load value of the power grid within the preset number of days includes: Obtaining the human comfort level in the area where the power grid is located every day within the preset number of days; For the nth day within the preset number of days, calculate the difference between the human body comfort on the nth day and the human body comfort on the n-1th day to obtain the human body comfort change value corresponding to the nth day, where n is a positive integer; Multiplying the human body comfort level on the nth day by the human body comfort level change value corresponding to the nth day to obtain a human body comfort level increment index on the nth day; Determine the daily human comfort increment index within the preset number of days as the human comfort change rule of the area where the power grid is located within the preset number of days before the mth day; Within the preset number of days before the mth day, obtaining the maximum load value of the power grid every day within the preset number of days; For the nth day within the preset number of days, calculate the difference between the maximum load value of the power grid on the nth day and the maximum load value of the power grid on the n-1th day, and obtain the maximum load change value of the power grid on the nth day compared with the n-1th day; The maximum load change value of the power grid each day within the preset number of days is determined as the maximum load value change rule of the power grid within the preset number of days.

4. The method according to claim 3, characterized in that The calculating, based on the human body comfort variation law and the maximum load value variation law, a target coefficient corresponding to the area where the power grid is located, comprises: For the nth day within the preset number of days, calculate the ratio between the maximum load change value of the power grid on the nth day compared to the n-1th day and the human comfort change value on the nth day compared to the n-1th day, and obtain the target ratio corresponding to the nth day; Performing polynomial fitting on the target ratio corresponding to each day within the preset number of days and the daily human comfort increment index to obtain a fitting formula between the target ratio and the human comfort increment index; The fitting formula is solved to obtain the target coefficient corresponding to the area where the power grid is located.

5. A power grid load increment prediction device, characterized in that: include: An acquisition module is used to obtain the human comfort level of the area where the power grid is located on the mth day and the predicted value of human comfort level on the m+1th day, and to obtain the variation law of the human comfort level of the area where the power grid is located within a preset number of days before the mth day and the variation law of the maximum load value of the power grid within the preset number of days, where m is a positive integer; The first calculation module is used to calculate the difference between the predicted value of human comfort on the m+1th day and the human comfort on the mth day, and obtain the change value of human comfort on the m+1th day compared with the mth day; A multiplication module, used for multiplying the predicted value of human comfort on the m+1th day by the change value of human comfort on the m+1th day compared with the mth day, to obtain an incremental index of human comfort on the m+1th day; A second calculation module is used to calculate the target coefficient corresponding to the area where the power grid is located based on the variation law of the human body comfort and the variation law of the maximum load value; A prediction module is used to predict the grid load increment on the m+1th day compared with the mth day based on the human comfort increment index on the m+1th day, the human comfort change value on the m+1th day compared with the mth day, and the target coefficient corresponding to the area where the grid is located.

6. The device according to claim 5, characterized in that The acquisition module comprises: The first acquisition submodule is used to obtain the maximum temperature value, average humidity value and average wind speed value of the area where the power grid is located on the mth day, and to obtain the maximum temperature forecast value, average humidity forecast value and average wind speed forecast value of the area where the power grid is located on the m+1th day; A first calculation submodule is used to calculate the human comfort level on the mth day based on the maximum temperature value, average humidity value and average wind speed value on the mth day; The second calculation submodule is used to calculate the predicted human comfort prediction value of the m+1th day based on the maximum temperature prediction value, the average humidity prediction value and the average wind speed prediction value of the m+1th day.

7. The device according to claim 5, characterized in that The acquisition module comprises: The second acquisition submodule is used to acquire the human comfort level in the area where the power grid is located every day within the preset number of days; A third calculation submodule is used to calculate the difference between the human body comfort on the nth day and the human body comfort on the n-1th day for the nth day within the preset number of days, and obtain a human body comfort change value corresponding to the nth day, where n is a positive integer; A multiplication submodule, used for multiplying the human body comfort degree on the nth day by the human body comfort degree change value corresponding to the nth day, to obtain the human body comfort degree increment index on the nth day; A first determination submodule is used to determine the human comfort increment index of each day within the preset number of days as the human comfort change rule of the area where the power grid is located within the preset number of days before the mth day; The third acquisition submodule is used to obtain the maximum load value of the power grid every day within the preset number of days before the mth day; A fourth calculation submodule is used to calculate, for the nth day within the preset number of days, the difference between the maximum load value of the power grid on the nth day and the maximum load value of the power grid on the n-1th day, to obtain a maximum load change value of the power grid on the nth day compared with the n-1th day; The second determining submodule is used to determine the maximum load change value of the power grid every day within the preset number of days as the maximum load value change rule of the power grid within the preset number of days.

8. The device according to claim 7, characterized in that The second calculation module includes: a fifth calculation submodule, for calculating, for the nth day within the preset number of days, a ratio between a maximum load change value of the power grid on the nth day compared to the n-1th day and a human comfort change value on the nth day compared to the n-1th day, to obtain a target ratio corresponding to the nth day; A fitting submodule, for performing polynomial fitting on the target ratio corresponding to each day within the preset number of days and the daily human comfort increment index, to obtain a fitting formula between the target ratio and the human comfort increment index; The solution submodule is used to solve the fitting formula to obtain the target coefficient corresponding to the area where the power grid is located.

9. A computer device, characterized in that: include: A memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the power grid load increment prediction method according to any one of claims 1 to 4 is implemented.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method for predicting power grid load increment according to any one of claims 1 to 4 is implemented.

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