Power grid load prediction method and device, electronic equipment and medium

Abstract

The invention provides a power grid load prediction method and device, electronic equipment and a medium. By analyzing the daily change, the cumulative change and the change trend of the air temperature, the influence of the air temperature on the power grid load can be carefully captured, so that the prediction accuracy is improved. Under extreme weather conditions, accurate load prediction can help an electric power company respond to surge of electric power demands more quickly, and the emergency processing capacity is improved.

Description

Technical Field

[0001] This disclosure belongs to the field of power grid technology, and in particular relates to a power grid load forecasting method, device, electronic equipment and medium. Background Technology

[0002] The escalating global climate change has led to frequent extreme heat events, causing residents and commercial buildings to rely heavily on air conditioning for cooling. At the same time, agricultural irrigation and industrial loads have also increased accordingly, resulting in a significant increase in overall electricity demand. This has caused the power grid load to repeatedly exceed peak levels, which is highly detrimental to power grid planning and safe and stable operation.

[0003] In related technologies, the prediction of power grid load is usually based on training and analysis of historical electricity consumption data. However, there is little historical electricity consumption data under extreme high temperature conditions, making it difficult to predict. Summary of the Invention

[0004] Therefore, it is necessary to provide a power grid load forecasting method, device, electronic equipment, and medium to address the above problems.

[0005] In a first aspect, embodiments of this disclosure provide a power grid load forecasting method, the method comprising: Obtain the daily maximum temperature and daily maximum power grid load within a first preset time period; the first preset time period includes multiple historical time windows; Obtain the predicted maximum temperature within a second preset time period; the second preset time period includes a future time window; Based on the daily maximum temperature within the first preset time period and the predicted maximum temperature within the second preset time period, calculate the daily change in maximum temperature compared to the previous day, the cumulative change in maximum temperature, and the change in maximum temperature. Determine a first target influence coefficient, a second target influence coefficient, and a third target influence coefficient; the first target influence coefficient is used to represent the degree of influence of temperature change on grid load, the second target influence coefficient is used to represent the degree of influence of the cumulative value of temperature change on grid load, and the third target influence coefficient is used to represent the degree of influence of the change value of temperature change on grid load; Based on the maximum temperature change value, the first target influence coefficient, the cumulative value of the maximum temperature change, the second target influence coefficient, the change value of the maximum temperature change, the third target influence coefficient, and the daily maximum grid load within the first preset time period, the predicted value of the maximum grid load within the second preset time period is determined.

[0006] As an optional implementation of this disclosure, the step of calculating the daily change in maximum temperature compared to the previous day, the cumulative change in maximum temperature, and the change in maximum temperature based on the daily maximum temperature within the first preset time period and the predicted maximum temperature within the second preset time period includes: Based on the daily maximum temperature within the first preset time period and the predicted maximum temperature within the second preset time period, calculate the daily change in maximum temperature compared to the previous day; The daily changes in maximum temperature compared to the previous day are summed to calculate the cumulative value of the daily changes in maximum temperature compared to the previous day. Based on the daily change in the highest temperature compared to the previous day, calculate the daily change in the highest temperature compared to the previous day.

[0007] As an optional implementation of this disclosure, determining the first target influence coefficient, the second target influence coefficient, and the third target influence coefficient includes: Initialize the first influence coefficient; Based on the daily maximum temperature variation and the daily maximum grid load within the first preset time period, calculate the first predicted value of the daily maximum grid load within the first preset time period under the influence of the first influence coefficient. Based on the first predicted value of the daily maximum grid load and the daily maximum grid load, a first deviation value is calculated; the first deviation value is the proportion of the average absolute deviation between the first predicted value and the actual value of the daily maximum grid load to the total actual grid load. If the first deviation value is less than or equal to the first preset deviation value, then the first influence coefficient corresponding to the first deviation value is determined to be the first target influence coefficient; If the first deviation value is greater than the first preset deviation value, then the first influence coefficient is updated to the sum of the first influence coefficient and the first preset value, and the process of calculating the first predicted value of the daily maximum grid load under the first influence coefficient based on the change value of the daily maximum temperature and the daily maximum grid load within the first preset time period is repeated; and the first deviation value is calculated based on the first predicted value of the daily maximum grid load and the daily maximum grid load.

[0008] As an optional implementation of this disclosure, determining the first target influence coefficient, the second target influence coefficient, and the third target influence coefficient includes: Initialize the second influence coefficient; Based on the daily change in maximum temperature compared to the previous day, the cumulative change in daily maximum temperature compared to the previous day, and the daily maximum grid load within the first preset time period, calculate the second predicted value of the daily maximum grid load within the first preset time period under the influence of the first target influence coefficient and the second influence coefficient. A second deviation value is obtained based on the second predicted value of the daily maximum grid load and the daily maximum grid load; the second deviation value is the proportion of the average absolute deviation between the second predicted value and the actual value of the daily maximum grid load to the total actual grid load. If the second deviation value is less than or equal to the second preset deviation value, then the second influence coefficient corresponding to the second deviation value is determined to be the second target influence coefficient; If the second deviation value is greater than the second preset deviation value, then the second influence coefficient is updated to the sum of the second influence coefficient and the second preset value. The process of calculating the second predicted value of the daily maximum grid load in the first preset time period under the action of the first target influence coefficient and the second influence coefficient is repeated based on the daily change value of the highest temperature compared to the previous day, the cumulative value of the daily change value of the highest temperature compared to the previous day, and the daily maximum grid load in the first preset time period. The second deviation value is obtained based on the second predicted value of the daily maximum grid load and the daily maximum grid load.

[0009] As an optional implementation of this disclosure, determining the first target influence coefficient, the second target influence coefficient, and the third target influence coefficient includes: Initialize the third influence coefficient; Based on the daily change in maximum temperature compared to the previous day, the cumulative change in daily maximum temperature compared to the previous day, the change in daily maximum temperature compared to the previous day, and the daily maximum grid load within the first preset time period, the third predicted value of the daily maximum grid load within the first preset time period is calculated under the influence of the first target influence coefficient, the second target influence coefficient, and the third influence coefficient. A third deviation value is obtained based on the third predicted value of the daily maximum grid load and the daily maximum grid load; the third deviation value is the proportion of the average absolute deviation between the third predicted value and the actual value of the daily maximum grid load to the total actual grid load. If the third deviation value is less than or equal to the third preset deviation value, then the third influence coefficient corresponding to the third deviation value is determined to be the third target influence coefficient; If the third deviation value is greater than the third preset deviation value, then the third influence coefficient is updated to the sum of the third influence coefficient and the third preset value. The process of calculating the third predicted value of the daily maximum grid load in the first preset time period under the influence of the first target influence coefficient, the second target influence coefficient, and the third influence coefficient is repeated based on the daily maximum temperature change value compared to the previous day, the cumulative value of the daily maximum temperature change compared to the previous day, the change value of the daily maximum temperature change compared to the previous day, and the daily maximum grid load in the first preset time period. The third deviation value is obtained based on the third predicted value of the daily maximum grid load and the daily maximum grid load.

[0010] As an optional implementation of this disclosure, the step of calculating the daily maximum temperature change value compared to the previous day based on the daily maximum temperature within the first preset time period and the predicted maximum temperature within the second preset time period includes: Calculate the daily change in maximum temperature compared to the previous day using the following formula:

[0011] in, Indicates the first Compared to the first The change in the highest temperature of the previous day. This represents the predicted highest temperature within the second preset time period, i.e., the [missing information]. The predicted highest temperature of the day, This represents the daily maximum temperature within the first preset time period, i.e., the... The highest temperature of the day before yesterday. It is an integer greater than or equal to 0.

[0012] As an optional implementation of this disclosure, the step of summing the daily changes in maximum temperature compared to the previous day to calculate the cumulative value of the daily changes in maximum temperature compared to the previous day includes: The cumulative change in daily maximum temperature compared to the previous day is calculated using the following formula:

[0013] in, Indicates the first Compared to the first The cumulative value of the highest temperature change of the previous day. Indicates the first Compared to the first The change in the highest temperature of the previous day. For integers greater than or equal to 0, It is an integer greater than or equal to 0.

[0014] As an optional implementation of this disclosure, calculating the daily change in the highest temperature compared to the previous day based on the daily change in the highest temperature compared to the previous day includes: Calculate the daily change in maximum temperature compared to the previous day using the following formula:

[0015] in, Indicates the first Compared to the first The change in the highest temperature of the previous day. Indicates the first Compared to the first The change in the highest temperature of the previous day. Indicates the first The day before the day compared to the day The change in the highest temperature of the day before and after.

[0016] Secondly, embodiments of this disclosure provide a power grid load forecasting device, the device comprising: The data collection module is used to obtain the daily maximum temperature and the daily maximum power grid load within a first preset time period; the first preset time period includes multiple historical time windows; The acquisition module is used to acquire the predicted maximum temperature within a second preset time period; the second preset time period includes a future time window; The calculation module is used to calculate the daily change in maximum temperature compared to the previous day, the cumulative change in maximum temperature, and the change in maximum temperature based on the daily maximum temperature during the first preset time period and the predicted maximum temperature during the second preset time period. The determination module is used to determine a first target influence coefficient, a second target influence coefficient, and a third target influence coefficient; the first target influence coefficient is used to represent the degree of influence of temperature change on grid load; the second target influence coefficient is used to represent the degree of influence of the cumulative value of temperature change on grid load; and the third target influence coefficient is used to represent the degree of influence of the change value of temperature change on grid load. The prediction module is used to determine the predicted value of the maximum grid load in the second preset time period based on the change value of the maximum temperature, the first target influence coefficient, the cumulative value of the change of the maximum temperature, the second target influence coefficient, the change value of the change of the maximum temperature, the third target influence coefficient, and the daily maximum grid load in the first preset time period.

[0017] Thirdly, embodiments of this disclosure provide an electronic device, including: a memory and a processor, wherein the memory is used to store a computer program; and the processor is used to execute the power grid load forecasting method described in the first aspect or any embodiment of the first aspect when the computer program is invoked.

[0018] Fourthly, embodiments of this disclosure provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the power grid load forecasting method described in the first aspect or any embodiment of the first aspect.

[0019] The power grid load forecasting method provided in this disclosure obtains the daily maximum temperature and daily maximum power grid load within a first preset time period; and obtains the predicted maximum temperature within a second preset time period. The first preset time period includes multiple historical time windows, and the second preset time period includes one future time window. Based on the daily maximum temperature within the first preset time period and the predicted maximum temperature within the second preset time period, the method calculates the daily change in maximum temperature compared to the previous day, the cumulative change in maximum temperature, and the change in maximum temperature. It then determines a first target influence coefficient, a second target influence coefficient, and a third target influence coefficient. The first target influence coefficient represents the degree of influence of temperature change on power grid load, the second target influence coefficient represents the degree of influence of the cumulative change in temperature on power grid load, and the third target influence coefficient represents the degree of influence of the change in temperature on power grid load. Finally, based on the maximum temperature change, the first target influence coefficient, the cumulative change in maximum temperature, the second target influence coefficient, the change in maximum temperature, the third target influence coefficient, and the daily maximum power grid load within the first preset time period, the method determines the predicted maximum power grid load within the second preset time period. By analyzing the daily variation, cumulative variation, and trend of temperature, the method can more precisely capture the impact of temperature on power grid load, thereby improving the accuracy of forecasting. In extreme weather conditions, accurate load forecasting can help power companies respond more quickly to surges in electricity demand and improve their emergency response capabilities. Attached Figure Description

[0020] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0021] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1A flowchart of a power grid load forecasting method provided in an embodiment of this disclosure; Figure 2 This is a schematic diagram of a power grid load forecasting device provided in an embodiment of the present disclosure; Figure 3 This is an internal structural diagram of an electronic device provided in one embodiment of the present disclosure. Detailed Implementation

[0023] To better understand the above-mentioned objectives, features, and advantages of this disclosure, the solutions disclosed herein will be further described below. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0024] Numerous specific details are set forth in the following description in order to provide a full understanding of this disclosure, but this disclosure may also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only some, and not all, of the embodiments of this disclosure.

[0025] In this article, the term "and / or" is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0026] In this disclosure, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" or "for example" in this disclosure should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a specific manner. Furthermore, in the description of the embodiments in this disclosure, unless otherwise stated, "a plurality of" means two or more.

[0027] This disclosure provides a method for power grid load forecasting. Specifically, refer to... Figure 1 As shown, the power grid load forecasting method provided in this embodiment includes the following steps S11-S15: S11. Obtain the daily maximum temperature and daily maximum power grid load within the first preset time period.

[0028] The first preset time period includes multiple historical time windows. For example, the first preset time period can start from today and trace back 7 days, including today, for a total of 8 days.

[0029] Load value refers to the total electrical load carried by the power grid within a certain period of time, reflecting the power demand of the power grid. Daily maximum grid load refers to the highest electrical load carried by the power grid within a 24-hour period. Daily maximum temperature refers to the highest temperature recorded within a single day.

[0030] For example, the historical data query function of a power grid monitoring system can be used to obtain the daily maximum temperature and daily maximum grid load for a power grid over the past week, including today. For instance, each day corresponds to a maximum temperature value, which are respectively... ,in Represents today's maximum temperature. Represents the highest temperature of the previous day. Representing the maximum temperature of the second day prior, ... This represents the maximum temperature of the previous seven days. Each day corresponds to a maximum load value, which are as follows: ,in Represents today's maximum grid load. Represents the maximum grid load of the previous day. Represents the maximum grid load of the previous two days, ... This represents the maximum grid load on the seventh day prior to the peak.

[0031] S12. Obtain the predicted maximum temperature within the second preset time period.

[0032] The second preset time period includes a future time window. For example, a future time window could be tomorrow.

[0033] Specifically, the predicted maximum temperature for tomorrow can be obtained through a meteorological forecasting numerical model. For example, the predicted maximum temperature for this power grid tomorrow can be denoted as... .

[0034] S13. Based on the daily maximum temperature within the first preset time period and the predicted maximum temperature within the second preset time period, calculate the daily change in maximum temperature compared to the previous day, the cumulative change in maximum temperature, and the change in maximum temperature.

[0035] In some embodiments, step S13 (calculating the daily change in maximum temperature compared to the previous day, the cumulative change in maximum temperature, and the change in maximum temperature based on the daily maximum temperature within the first preset time period and the predicted maximum temperature within the second preset time period) can be implemented in the following manner: (1) Based on the daily maximum temperature in the first preset time period and the predicted maximum temperature in the second preset time period, calculate the daily change in maximum temperature compared to the previous day.

[0036] Optionally, step (1) above can be implemented in the following way: Calculate the daily change in maximum temperature compared to the previous day using the following formula:

[0037] in, Indicates the first Compared to the first The change in the highest temperature of the previous day. This represents the predicted highest temperature within the second preset time period, i.e., the [missing information]. The predicted highest temperature of the day, This represents the daily maximum temperature within the first preset time period, i.e., the... The highest temperature of the day before yesterday. It is an integer greater than or equal to 0.

[0038] For example, The range of values ​​can be =0, 1, 2, ..., 7. For example... This indicates the predicted high temperature for tomorrow. Compared to today's highest temperature The change value, Indicates today's highest temperature Compared to the previous day's highest temperature The change value, ..., Indicates the highest temperature of the sixth day prior. Compared to the highest temperature of the previous seven days The change value.

[0039] (2) Sum the daily changes in the highest temperature compared to the previous day to calculate the cumulative value of the daily changes in the highest temperature compared to the previous day.

[0040] Optionally, step (2) above can be implemented in the following way: The cumulative change in daily maximum temperature compared to the previous day is calculated using the following formula:

[0041] in, Indicates the first Compared to the first The cumulative value of the highest temperature change of the previous day. Indicates the first Compared to the first The change in the highest temperature of the previous day. For integers greater than or equal to 0, It is an integer greater than or equal to 0.

[0042] For example, The range of values ​​can be =0, 1, 2, ..., 7. For example... This indicates the predicted high temperature for tomorrow. Compared to today's highest temperature The cumulative value of the change in value, , , , ..., , .

[0043] (3) Based on the daily change in the highest temperature compared to the previous day, calculate the daily change in the highest temperature compared to the previous day.

[0044] Optionally, step (3) above can be implemented in the following way: Calculate the daily change in maximum temperature compared to the previous day using the following formula:

[0045] in, Indicates the first Compared to the first The change in the highest temperature of the previous day. Indicates the first Compared to the first The change in the highest temperature of the previous day. Indicates the first The day before the day compared to the day The change in the highest temperature of the day before and after.

[0046] For example, The range of values ​​can be =0, 1, 2, ..., 7. For example... This indicates the predicted high temperature for tomorrow. Compared to today's highest temperature The change value, This indicates the change in tomorrow's maximum temperature compared to today's. , , , ..., .

[0047] S14. Determine the influence coefficients of the first objective, the second objective, and the third objective.

[0048] Wherein, the first target influence coefficient is used to represent the degree of influence of temperature change on grid load, the second target influence coefficient is used to represent the degree of influence of cumulative temperature change on grid load, and the third target influence coefficient is used to represent the degree of influence of temperature change on grid load.

[0049] Optionally, the determination of the first target influence coefficient in step S14 (determining the first target influence coefficient, the second target influence coefficient, and the third target influence coefficient) can be achieved in the following way: Initialize the first influence coefficient.

[0050] The first influence coefficient is used to represent the degree of impact of temperature changes on the power grid load. The first influence coefficient can be expressed as follows: express.

[0051] Specifically, the first influence coefficient is initialized, and the first influence coefficient is set to... It is 0.

[0052] Based on the daily maximum temperature variation and the daily maximum grid load within the first preset time period, calculate the first predicted value of the daily maximum grid load within the first preset time period under the influence of the first influence coefficient.

[0053] Optionally, the first predicted value of the daily maximum power grid load within the first preset time period under the influence of the first influence coefficient can be calculated using the following formula:

[0054] in, This indicates that under the influence of the first influence coefficient, the first time period within the first preset time period... The first predicted value of the maximum grid load for the day; This represents the first influence coefficient. Indicates the first Compared to the first The change in the highest temperature of the previous day. Indicates the first The maximum grid load of the day. In this embodiment of the disclosure, The range of values ​​can be =1, 2, ..., 7.

[0055] A first deviation value is calculated based on the first predicted value of the daily maximum grid load and the daily maximum grid load.

[0056] The first deviation is the proportion of the average absolute deviation between the first predicted value and the actual value of the daily maximum grid load to the total actual grid load.

[0057] Optionally, the first deviation value can be calculated using the following formula:

[0058] in, This represents the first deviation value. This indicates that under the influence of the first influence coefficient, the first time period within the first preset time period... The first predicted value of the maximum grid load for the day; Indicates the first The maximum grid load of the day.

[0059] If the first deviation value is less than or equal to the first preset deviation value, then the first influence coefficient corresponding to the first deviation value is determined to be the first target influence coefficient.

[0060] The first preset deviation value can be selected according to the actual application scenario. For example, it can be selected as 5%, 2%, 3% or other reasonable values. No specific restrictions are imposed here.

[0061] For example, if the first deviation value is less than or equal to 5%, then the first influence coefficient corresponding to the first deviation value is determined as the first target influence coefficient. The first target influence coefficient can be used as follows: express.

[0062] If the first deviation value is greater than the first preset deviation value, then the first influence coefficient is updated to the sum of the first influence coefficient and the first preset value, and the process of calculating the first predicted value of the daily maximum grid load under the first influence coefficient based on the change value of the daily maximum temperature and the daily maximum grid load within the first preset time period is repeated; and the first deviation value is calculated based on the first predicted value of the daily maximum grid load and the daily maximum grid load.

[0063] The first preset value can be selected according to the actual application scenario. For example, it can be selected as 0.1, 0.01, 0.2 or other reasonable values. There are no specific restrictions here.

[0064] For example, taking a first preset deviation value of 5% and a first preset value of 0.1 as an example, if the first deviation value is greater than 5%, then... Add 0.1 to the original value and return to the execution steps. Based on the change value of the daily maximum temperature and the daily maximum grid load within the first preset time period, calculate the first predicted value of the daily maximum grid load within the first preset time period under the action of the first influence coefficient. Based on the first predicted value of the daily maximum grid load and the daily maximum grid load, calculate the first deviation value until the first deviation value is less than or equal to the first preset deviation value. Then, determine the first influence coefficient corresponding to the first deviation value at this time as the first target influence coefficient.

[0065] Optionally, the determination of the second target influence coefficient in step S14 (determining the first target influence coefficient, the second target influence coefficient, and the third target influence coefficient) can be achieved in the following way: Initialize the second influence coefficient.

[0066] The second influence coefficient is used to represent the degree of impact of cumulative temperature changes on the power grid load. The second influence coefficient can be expressed as follows: express.

[0067] Specifically, the second influence coefficient is initialized, and the second influence coefficient is set to... It is 0.

[0068] Based on the daily change in maximum temperature compared to the previous day, the cumulative change in daily maximum temperature compared to the previous day, and the daily maximum grid load within the first preset time period, a second predicted value of the daily maximum grid load within the first preset time period is calculated under the influence of the first target influence coefficient and the second influence coefficient.

[0069] Optionally, the second predicted value of the daily maximum grid load within the first preset time period, under the influence of the first target influence coefficient and the second influence coefficient, is calculated using the following formula:

[0070] in, This indicates that, under the influence of the first target influence coefficient and the second influence coefficient, the [number]th [time period] within the first preset time period... The second forecast value for the maximum grid load of the day; This represents the influence coefficient of the first objective. This represents the second influence coefficient. Indicates the first Compared to the first The change in the highest temperature of the previous day. Indicates the first Compared to the first The cumulative value of the highest temperature change of the previous day. Indicates the first The maximum grid load of the day. In this embodiment of the disclosure, The range of values ​​can be =1, 2, ..., 7.

[0071] A second deviation value is obtained based on the second predicted value of the daily maximum grid load and the daily maximum grid load.

[0072] The second deviation value is the proportion of the average absolute deviation between the second predicted value and the actual value of the daily maximum grid load to the total actual grid load.

[0073] Optionally, the second deviation value can be calculated using the following formula:

[0074] in, This represents the second deviation value. This indicates that, under the influence of the first target influence coefficient and the second influence coefficient, the [number]th [time period] within the first preset time period... The second forecast value for the maximum grid load of the day; Indicates the first The maximum grid load of the day.

[0075] If the second deviation value is less than or equal to the second preset deviation value, then the second influence coefficient corresponding to the second deviation value is determined to be the second target influence coefficient.

[0076] The second preset deviation value can be selected according to the actual application scenario. For example, it can be selected as 5%, 2%, 3% or other reasonable values. No specific restrictions are imposed here.

[0077] For example, if the second deviation value is less than or equal to 5%, then the second influence coefficient corresponding to the second deviation value is determined as the second target influence coefficient.

[0078] If the second deviation value is greater than the second preset deviation value, then the second influence coefficient is updated to the sum of the second influence coefficient and the second preset value. The process of calculating the second predicted value of the daily maximum grid load in the first preset time period under the action of the first target influence coefficient and the second influence coefficient is repeated based on the daily change value of the highest temperature compared to the previous day, the cumulative value of the daily change value of the highest temperature compared to the previous day, and the daily maximum grid load in the first preset time period. The second deviation value is obtained based on the second predicted value of the daily maximum grid load and the daily maximum grid load.

[0079] The second preset value can be selected according to the actual application scenario. For example, it can be selected as 0.1, 0.01, 0.2 or other reasonable values. There are no specific restrictions here.

[0080] For example, taking a second preset deviation value of 5% and a second preset value of 0.1 as an example, if the second deviation value is greater than 5%, then... Add 0.1 to the original value and return to the execution steps. Based on the daily change in maximum temperature compared to the previous day, the cumulative daily change in maximum temperature compared to the previous day, and the daily maximum grid load within the first preset time period, calculate the second predicted value of the daily maximum grid load within the first preset time period under the influence of the first target influence coefficient and the second influence coefficient. Based on the second predicted value of the daily maximum grid load and the daily maximum grid load, calculate the second deviation value until the second deviation value is less than or equal to the second preset deviation value. Then, determine the second influence coefficient corresponding to the second deviation value at this point as the first target influence coefficient. The second target influence coefficient can be used... express.

[0081] Optionally, the determination of the second target influence coefficient in step S14 (determining the first target influence coefficient, the second target influence coefficient, and the third target influence coefficient) can be achieved in the following way: Initialize the third influence coefficient.

[0082] The third influence coefficient is used to represent the degree of impact of temperature changes on the power grid load. The third influence coefficient can be used... express.

[0083] Specifically, the third influence coefficient is initialized, and the third influence coefficient is set to... It is 0.

[0084] Based on the daily change in maximum temperature compared to the previous day, the cumulative change in daily maximum temperature compared to the previous day, the change in daily maximum temperature compared to the previous day, and the daily maximum grid load within the first preset time period, a third predicted value of the daily maximum grid load within the first preset time period is calculated under the influence of the first target influence coefficient, the second target influence coefficient, and the third influence coefficient.

[0085] Optionally, the third predicted value of the daily maximum grid load within the first preset time period is calculated using the following formula, under the influence of the first target influence coefficient, the second target influence coefficient, and the third influence coefficient:

[0086] in, This indicates that, under the influence of the first target influence coefficient, the second target influence coefficient, and the third influence coefficient, the [number]th [time period] within the first preset time period... The third predicted value of the maximum grid load for the day; This represents the influence coefficient of the first objective. This represents the influence coefficient of the second objective. This represents the third influence coefficient. Indicates the first Compared to the first The change in the highest temperature of the previous day. Indicates the first Compared to the first The cumulative value of the highest temperature change of the previous day. Indicates the first Compared to the first The change in the highest temperature of the previous day. Indicates the first The maximum grid load of the day. In this embodiment of the disclosure, The range of values ​​can be =1, 2, ..., 7.

[0087] A third deviation value is obtained based on the third predicted value of the daily maximum grid load and the daily maximum grid load.

[0088] The third deviation value is the proportion of the average absolute deviation between the third predicted value and the actual value of the daily maximum grid load to the total actual grid load.

[0089] Optionally, the third deviation value can be calculated using the following formula:

[0090] in, This represents the third deviation value. This indicates that, under the influence of the first target influence coefficient, the second target influence coefficient, and the third influence coefficient, the [number]th [time period] within the first preset time period... The third predicted value of the maximum grid load for the day; Indicates the first The maximum grid load of the day.

[0091] If the third deviation value is less than or equal to the third preset deviation value, then the third influence coefficient corresponding to the third deviation value is determined to be the third target influence coefficient.

[0092] The third preset deviation value can be selected according to the actual application scenario. For example, it can be selected as 5%, 2%, 3% or other reasonable values. There are no specific restrictions here.

[0093] For example, if the third deviation value is less than or equal to 5%, then the third influence coefficient corresponding to the third deviation value is determined as the third target influence coefficient. The third target influence coefficient can be used... express.

[0094] If the third deviation value is greater than the third preset deviation value, then the third influence coefficient is updated to the sum of the third influence coefficient and the third preset value. The process of calculating the third predicted value of the daily maximum grid load in the first preset time period under the influence of the first target influence coefficient, the second target influence coefficient, and the third influence coefficient is repeated based on the daily maximum temperature change value compared to the previous day, the cumulative value of the daily maximum temperature change compared to the previous day, the change value of the daily maximum temperature change compared to the previous day, and the daily maximum grid load in the first preset time period. The third deviation value is obtained based on the third predicted value of the daily maximum grid load and the daily maximum grid load.

[0095] The third preset value can be selected according to the actual application scenario. For example, it can be selected as 0.1, 0.01, 0.2 or other reasonable values. There are no specific restrictions here.

[0096] For example, taking a third preset deviation value of 5% and a third preset value of 0.1 as an example, if the third deviation value is greater than 5%, then... Add 0.1 to the original value and return to the execution steps. Based on the daily change in the highest temperature compared to the previous day, the cumulative daily change in the highest temperature compared to the previous day, the daily change in the highest temperature compared to the previous day, and the daily maximum grid load within the first preset time period, calculate the third predicted value of the daily maximum grid load within the first preset time period under the influence of the first target influence coefficient, the second target influence coefficient, and the third influence coefficient. Based on the third predicted value of the daily maximum grid load and the daily maximum grid load, calculate the third deviation value until the third deviation value is less than or equal to the third preset deviation value. Then, determine the third influence coefficient corresponding to the third deviation value at this time as the third target influence coefficient.

[0097] S15. Based on the maximum temperature change value, the first target influence coefficient, the cumulative value of the maximum temperature change, the second target influence coefficient, the change value of the maximum temperature change, the third target influence coefficient, and the daily maximum grid load within the first preset time period, determine the predicted value of the maximum grid load within the second preset time period.

[0098] Optionally, after determining the first target influence coefficient, the second target influence coefficient, and the third target influence coefficient, the maximum power grid load forecast value within the second preset time period is determined according to the following formula:

[0099] in, This represents the predicted maximum grid load for tomorrow. This represents the influence coefficient of the first objective. This indicates the predicted high temperature for tomorrow. Compared to today's highest temperature The change value, This represents the influence coefficient of the second objective. This indicates the predicted high temperature for tomorrow. Compared to today's highest temperature The cumulative value of the change in value, This represents the influence coefficient of the third objective. This indicates the change in tomorrow's maximum temperature compared to today's. This indicates the maximum grid load today.

[0100] The power grid load forecasting method provided in this disclosure obtains the daily maximum temperature and daily maximum power grid load within a first preset time period; and obtains the predicted maximum temperature within a second preset time period. The first preset time period includes multiple historical time windows, and the second preset time period includes one future time window. Based on the daily maximum temperature within the first preset time period and the predicted maximum temperature within the second preset time period, the method calculates the daily change in maximum temperature compared to the previous day, the cumulative change in maximum temperature, and the change in maximum temperature. It then determines a first target influence coefficient, a second target influence coefficient, and a third target influence coefficient. The first target influence coefficient represents the degree of influence of temperature change on power grid load, the second target influence coefficient represents the degree of influence of the cumulative change in temperature on power grid load, and the third target influence coefficient represents the degree of influence of the change in temperature on power grid load. Finally, based on the maximum temperature change, the first target influence coefficient, the cumulative change in maximum temperature, the second target influence coefficient, the change in maximum temperature, the third target influence coefficient, and the daily maximum power grid load within the first preset time period, the method determines the predicted maximum power grid load within the second preset time period. By analyzing the daily variation, cumulative variation, and trend of temperature, the method can more precisely capture the impact of temperature on power grid load, thereby improving the accuracy of forecasting. In extreme weather conditions, accurate load forecasting can help power companies respond more quickly to surges in electricity demand and improve their emergency response capabilities.

[0101] This disclosure provides a power grid load forecasting device for executing any of the power grid load forecasting methods provided in the above embodiments, and possesses the corresponding beneficial effects of the power grid load forecasting method.

[0102] Figure 2 This is a schematic diagram of the structure of a power grid load forecasting device provided in an embodiment of the present disclosure, as shown below. Figure 2 As shown, the power grid load forecasting device includes: The data collection module 210 is used to obtain the daily maximum temperature and the daily maximum power grid load within a first preset time period; the first preset time period includes multiple historical time windows; The acquisition module 220 is used to acquire the predicted maximum temperature within a second preset time period; the second preset time period includes a future time window; The calculation module 230 is used to calculate the daily change in maximum temperature compared to the previous day, the cumulative change in maximum temperature, and the change in maximum temperature based on the daily maximum temperature during the first preset time period and the predicted maximum temperature during the second preset time period. The determination module 240 is used to determine a first target influence coefficient, a second target influence coefficient, and a third target influence coefficient; the first target influence coefficient is used to represent the degree of influence of temperature change on grid load; the second target influence coefficient is used to represent the degree of influence of the cumulative value of temperature change on grid load; and the third target influence coefficient is used to represent the degree of influence of the change value of temperature change on grid load. The prediction module 250 is used to determine the predicted value of the maximum grid load in the second preset time period based on the maximum temperature change value, the first target influence coefficient, the cumulative value of the maximum temperature change, the second target influence coefficient, the change value of the maximum temperature change, the third target influence coefficient, and the daily maximum grid load in the first preset time period.

[0103] As an optional implementation of this disclosure, the computing module includes: The first calculation unit is used to calculate the daily change in the highest temperature compared to the previous day based on the daily highest temperature during the first preset time period and the predicted highest temperature during the second preset time period. The second calculation unit is used to sum the daily changes in the highest temperature compared to the previous day, and calculate the cumulative value of the daily changes in the highest temperature compared to the previous day. The third calculation unit is used to calculate the change in the daily maximum temperature compared to the previous day, based on the daily change in the maximum temperature compared to the previous day.

[0104] As an optional implementation of this disclosure, the determining module is specifically used for: Initialize the first influence coefficient; Based on the daily maximum temperature variation and the daily maximum grid load within the first preset time period, calculate the first predicted value of the daily maximum grid load within the first preset time period under the influence of the first influence coefficient. Based on the first predicted value of the daily maximum grid load and the daily maximum grid load, a first deviation value is calculated; the first deviation value is the proportion of the average absolute deviation between the first predicted value and the actual value of the daily maximum grid load to the total actual grid load. If the first deviation value is less than or equal to the first preset deviation value, then the first influence coefficient corresponding to the first deviation value is determined to be the first target influence coefficient; If the first deviation value is greater than the first preset deviation value, then the first influence coefficient is updated to the sum of the first influence coefficient and the first preset value, and the process of calculating the first predicted value of the daily maximum grid load under the first influence coefficient based on the change value of the daily maximum temperature and the daily maximum grid load within the first preset time period is repeated; and the first deviation value is calculated based on the first predicted value of the daily maximum grid load and the daily maximum grid load.

[0105] As an optional implementation of this disclosure, the determining module is further specifically used for: Initialize the second influence coefficient; Based on the daily change in maximum temperature compared to the previous day, the cumulative change in daily maximum temperature compared to the previous day, and the daily maximum grid load within the first preset time period, calculate the second predicted value of the daily maximum grid load within the first preset time period under the influence of the first target influence coefficient and the second influence coefficient. A second deviation value is obtained based on the second predicted value of the daily maximum grid load and the daily maximum grid load; the second deviation value is the proportion of the average absolute deviation between the second predicted value and the actual value of the daily maximum grid load to the total actual grid load. If the second deviation value is less than or equal to the second preset deviation value, then the second influence coefficient corresponding to the second deviation value is determined to be the second target influence coefficient; If the second deviation value is greater than the second preset deviation value, then the second influence coefficient is updated to the sum of the second influence coefficient and the second preset value. The process of calculating the second predicted value of the daily maximum grid load in the first preset time period under the action of the first target influence coefficient and the second influence coefficient is repeated based on the daily change value of the highest temperature compared to the previous day, the cumulative value of the daily change value of the highest temperature compared to the previous day, and the daily maximum grid load in the first preset time period. The second deviation value is obtained based on the second predicted value of the daily maximum grid load and the daily maximum grid load.

[0106] As an optional implementation of this disclosure, the determining module is further specifically used for: Initialize the third influence coefficient; Based on the daily change in maximum temperature compared to the previous day, the cumulative change in daily maximum temperature compared to the previous day, the change in daily maximum temperature compared to the previous day, and the daily maximum grid load within the first preset time period, the third predicted value of the daily maximum grid load within the first preset time period is calculated under the influence of the first target influence coefficient, the second target influence coefficient, and the third influence coefficient. A third deviation value is obtained based on the third predicted value of the daily maximum grid load and the daily maximum grid load; the third deviation value is the proportion of the average absolute deviation between the third predicted value and the actual value of the daily maximum grid load to the total actual grid load. If the third deviation value is less than or equal to the third preset deviation value, then the third influence coefficient corresponding to the third deviation value is determined to be the third target influence coefficient; If the third deviation value is greater than the third preset deviation value, then the third influence coefficient is updated to the sum of the third influence coefficient and the third preset value. The process of calculating the third predicted value of the daily maximum grid load in the first preset time period under the influence of the first target influence coefficient, the second target influence coefficient, and the third influence coefficient is repeated based on the daily maximum temperature change value compared to the previous day, the cumulative value of the daily maximum temperature change compared to the previous day, the change value of the daily maximum temperature change compared to the previous day, and the daily maximum grid load in the first preset time period. The third deviation value is obtained based on the third predicted value of the daily maximum grid load and the daily maximum grid load.

[0107] As an optional implementation of this disclosure, the first computing unit is specifically used for: Calculate the daily change in maximum temperature compared to the previous day using the following formula:

[0108] in, Indicates the first Compared to the first The change in the highest temperature of the previous day. This represents the predicted highest temperature within the second preset time period, i.e., the [missing information]. The predicted highest temperature of the day, This represents the daily maximum temperature within the first preset time period, i.e., the... The highest temperature of the day before yesterday. It is an integer greater than or equal to 0.

[0109] As an optional implementation of this disclosure, the second computing unit is specifically used for: The cumulative change in daily maximum temperature compared to the previous day is calculated using the following formula:

[0110] in, Indicates the first Compared to the first The cumulative value of the highest temperature change of the previous day. Indicates the first Compared to the first The change in the highest temperature of the previous day. For integers greater than or equal to 0, It is an integer greater than or equal to 0.

[0111] As an optional implementation of this disclosure, the third computing unit is specifically used for: Calculate the daily change in maximum temperature compared to the previous day using the following formula:

[0112] in, Indicates the first Compared to the first The change in the highest temperature of the previous day. Indicates the first Compared to the first The change in the highest temperature of the previous day. Indicates the first The day before the day compared to the day The change in the highest temperature of the day before and after.

[0113] The power grid load forecasting device provided in this embodiment acquires the daily maximum temperature and daily maximum power grid load within a first preset time period; and acquires the predicted maximum temperature within a second preset time period. The first preset time period includes multiple historical time windows, and the second preset time period includes one future time window. Based on the daily maximum temperature within the first preset time period and the predicted maximum temperature within the second preset time period, the device calculates the daily change in maximum temperature compared to the previous day, the cumulative change in maximum temperature, and the change in maximum temperature. It then determines a first target influence coefficient, a second target influence coefficient, and a third target influence coefficient. The first target influence coefficient represents the degree of influence of temperature change on power grid load, the second target influence coefficient represents the degree of influence of the cumulative change in temperature on power grid load, and the third target influence coefficient represents the degree of influence of the change in temperature on power grid load. Finally, based on the maximum temperature change, the first target influence coefficient, the cumulative change in maximum temperature, the second target influence coefficient, the change in maximum temperature, the third target influence coefficient, and the daily maximum power grid load within the first preset time period, it determines the predicted maximum power grid load within the second preset time period. By analyzing the daily variation, cumulative variation, and trend of temperature, the device can more precisely capture the impact of temperature on power grid load, thereby improving the accuracy of forecasting. In extreme weather conditions, accurate load forecasting can help power companies respond more quickly to surges in electricity demand and improve their emergency response capabilities.

[0114] Specific limitations regarding the power grid load forecasting device can be found in the limitations of the power grid load forecasting method described above, and will not be repeated here. Each module in the aforementioned power grid load forecasting device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in the electronic device, or stored in the memory of the electronic device as software, so that the processor can call and execute the corresponding operations of each module.

[0115] In one embodiment, an electronic device is provided, the internal structure of which can be shown as follows: Figure 3 As shown, the electronic device includes a processor, memory, and a communication interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage medium. The communication interface is used for wired or wireless communication with external electronic devices; wireless communication can be achieved through WiFi, carrier networks, near-field communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a power grid load forecasting method.

[0116] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present disclosure and does not constitute a limitation on the electronic device to which the present disclosure is applied. A specific electronic device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0117] In one embodiment, the power grid load forecasting device provided in this disclosure can be implemented as a computer program, which can be implemented in, for example... Figure 3 The electronic device shown is in operation. The memory of the electronic device can store the various program modules that make up the power grid load forecasting device of the electronic device.

[0118] In one embodiment, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by a processor, implements the steps in the above method embodiments.

[0119] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, databases, or other media used in the embodiments provided in this disclosure can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static random access memory (SRAM) and dynamic random access memory (DRAM), etc.

[0120] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0121] The embodiments described above are merely illustrative of several implementations of this disclosure, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this disclosure, and these all fall within the protection scope of this disclosure. Therefore, the protection scope of this patent should be determined by the appended claims.

Claims

1. A power grid load forecasting method, characterized in that, The method includes: Obtain the daily maximum temperature and daily maximum power grid load within a first preset time period; the first preset time period includes multiple historical time windows; Obtain the predicted maximum temperature within a second preset time period; the second preset time period includes a future time window; Based on the daily maximum temperature within the first preset time period and the predicted maximum temperature within the second preset time period, calculate the daily change in maximum temperature compared to the previous day, the cumulative change in maximum temperature, and the change in maximum temperature. Determine a first target influence coefficient, a second target influence coefficient, and a third target influence coefficient; the first target influence coefficient is used to represent the degree of influence of temperature change on grid load, the second target influence coefficient is used to represent the degree of influence of the cumulative value of temperature change on grid load, and the third target influence coefficient is used to represent the degree of influence of the change value of temperature change on grid load; Based on the maximum temperature change value, the first target influence coefficient, the cumulative value of the maximum temperature change, the second target influence coefficient, the change value of the maximum temperature change, the third target influence coefficient, and the daily maximum grid load within the first preset time period, the predicted value of the maximum grid load within the second preset time period is determined.

2. The method according to claim 1, characterized in that, The step of calculating the daily maximum temperature change compared to the previous day, the cumulative maximum temperature change, and the change in maximum temperature based on the daily maximum temperature within the first preset time period and the predicted maximum temperature within the second preset time period includes: Based on the daily maximum temperature within the first preset time period and the predicted maximum temperature within the second preset time period, calculate the daily change in maximum temperature compared to the previous day; The daily changes in maximum temperature compared to the previous day are summed to calculate the cumulative value of the daily changes in maximum temperature compared to the previous day. Based on the daily change in the highest temperature compared to the previous day, calculate the daily change in the highest temperature compared to the previous day.

3. The method according to claim 1, characterized in that, The determination of the influence coefficients of the first target, the second target, and the third target includes: Initialize the first influence coefficient; Based on the daily maximum temperature variation and the daily maximum grid load within the first preset time period, calculate the first predicted value of the daily maximum grid load within the first preset time period under the influence of the first influence coefficient. Based on the first predicted value of the daily maximum grid load and the daily maximum grid load, a first deviation value is calculated; the first deviation value is the proportion of the average absolute deviation between the first predicted value and the actual value of the daily maximum grid load to the total actual grid load. If the first deviation value is less than or equal to the first preset deviation value, then the first influence coefficient corresponding to the first deviation value is determined to be the first target influence coefficient; If the first deviation value is greater than the first preset deviation value, then the first influence coefficient is updated to the sum of the first influence coefficient and the first preset value, and the process of calculating the first predicted value of the daily maximum grid load under the first influence coefficient based on the change value of the daily maximum temperature and the daily maximum grid load within the first preset time period is repeated; and the first deviation value is calculated based on the first predicted value of the daily maximum grid load and the daily maximum grid load.

4. The method according to claim 3, characterized in that, The determination of the influence coefficients of the first target, the second target, and the third target includes: Initialize the second influence coefficient; Based on the daily change in maximum temperature compared to the previous day, the cumulative change in daily maximum temperature compared to the previous day, and the daily maximum grid load within the first preset time period, calculate the second predicted value of the daily maximum grid load within the first preset time period under the influence of the first target influence coefficient and the second influence coefficient. A second deviation value is obtained based on the second predicted value of the daily maximum grid load and the daily maximum grid load; the second deviation value is the proportion of the average absolute deviation between the second predicted value and the actual value of the daily maximum grid load to the total actual grid load. If the second deviation value is less than or equal to the second preset deviation value, then the second influence coefficient corresponding to the second deviation value is determined to be the second target influence coefficient; If the second deviation value is greater than the second preset deviation value, then the second influence coefficient is updated to the sum of the second influence coefficient and the second preset value. The process of calculating the second predicted value of the daily maximum grid load in the first preset time period under the action of the first target influence coefficient and the second influence coefficient is repeated based on the daily change value of the highest temperature compared to the previous day, the cumulative value of the daily change value of the highest temperature compared to the previous day, and the daily maximum grid load in the first preset time period. The second deviation value is obtained based on the second predicted value of the daily maximum grid load and the daily maximum grid load.

5. The method according to claim 4, characterized in that, The determination of the influence coefficients of the first target, the second target, and the third target includes: Initialize the third influence coefficient; Based on the daily change in maximum temperature compared to the previous day, the cumulative change in daily maximum temperature compared to the previous day, the change in daily maximum temperature compared to the previous day, and the daily maximum grid load within the first preset time period, the third predicted value of the daily maximum grid load within the first preset time period is calculated under the influence of the first target influence coefficient, the second target influence coefficient, and the third influence coefficient. A third deviation value is obtained based on the third predicted value of the daily maximum grid load and the daily maximum grid load; the third deviation value is the proportion of the average absolute deviation between the third predicted value and the actual value of the daily maximum grid load to the total actual grid load. If the third deviation value is less than or equal to the third preset deviation value, then the third influence coefficient corresponding to the third deviation value is determined to be the third target influence coefficient; If the third deviation value is greater than the third preset deviation value, then the third influence coefficient is updated to the sum of the third influence coefficient and the third preset value. The process of calculating the third predicted value of the daily maximum grid load in the first preset time period under the influence of the first target influence coefficient, the second target influence coefficient, and the third influence coefficient is repeated based on the daily maximum temperature change value compared to the previous day, the cumulative value of the daily maximum temperature change compared to the previous day, the change value of the daily maximum temperature change compared to the previous day, and the daily maximum grid load in the first preset time period. The third deviation value is obtained based on the third predicted value of the daily maximum grid load and the daily maximum grid load.

6. The method according to claim 2, characterized in that, The step of calculating the daily maximum temperature change compared to the previous day based on the daily maximum temperature within the first preset time period and the predicted maximum temperature within the second preset time period includes: Calculate the daily change in maximum temperature compared to the previous day using the following formula: in, Indicates the first Compared to the first The change in the highest temperature of the previous day. This represents the predicted highest temperature within the second preset time period, i.e., the [missing information]. The predicted highest temperature of the day, This represents the daily maximum temperature within the first preset time period, i.e., the... The highest temperature of the day before yesterday. It is an integer greater than or equal to 0.

7. The method according to claim 2, characterized in that, The step of summing up the daily changes in maximum temperature compared to the previous day to calculate the cumulative value of the daily changes in maximum temperature compared to the previous day includes: The cumulative change in daily maximum temperature compared to the previous day is calculated using the following formula: in, Indicates the first Compared to the first The cumulative value of the highest temperature change of the previous day. Indicates the first Compared to the first The change in the highest temperature of the previous day. For integers greater than or equal to 0, It is an integer greater than or equal to 0.

8. The method according to claim 2, characterized in that, The calculation of the daily change in the highest temperature compared to the previous day, based on the daily change in the highest temperature compared to the previous day, includes: Calculate the daily change in maximum temperature compared to the previous day using the following formula: in, Indicates the first Compared to the first The change in the highest temperature of the previous day. Indicates the first Compared to the first The change in the highest temperature of the previous day. Indicates the first The day before the day compared to the day The change in the highest temperature of the day before and after.

9. A power grid load forecasting device, characterized in that, include: The data collection module is used to obtain the daily maximum temperature and the daily maximum power grid load within a first preset time period; The first preset time period includes multiple historical time windows; The acquisition module is used to acquire the predicted maximum temperature within a second preset time period; The second preset time period includes a future time window; The calculation module is used to calculate the daily change in maximum temperature compared to the previous day, the cumulative change in maximum temperature, and the change in maximum temperature based on the daily maximum temperature during the first preset time period and the predicted maximum temperature during the second preset time period. The determination module is used to determine a first target influence coefficient, a second target influence coefficient, and a third target influence coefficient; the first target influence coefficient is used to represent the degree of influence of temperature change on grid load; the second target influence coefficient is used to represent the degree of influence of the cumulative value of temperature change on grid load; and the third target influence coefficient is used to represent the degree of influence of the change value of temperature change on grid load. The prediction module is used to determine the predicted value of the maximum grid load in the second preset time period based on the change value of the maximum temperature, the first target influence coefficient, the cumulative value of the change of the maximum temperature, the second target influence coefficient, the change value of the change of the maximum temperature, the third target influence coefficient, and the daily maximum grid load in the first preset time period.

10. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the power grid load forecasting method according to any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the power grid load forecasting method according to any one of claims 1-8.

Images