A Method and System for Analyzing the Sensitivity of Load Influence on Temperature Based on Regional Characteristics

Abstract

This invention relates to the field of temperature forecasting technology, and more particularly to a method and system for analyzing the temperature sensitivity of load impact based on regional characteristic analysis. The method includes the following steps: acquiring historical power load data, temperature data, geographic information data, and energy usage ratio data corresponding to a target area, and performing time-series synchronous preprocessing and quantification of regional characteristic indicators to obtain corresponding power regional characteristic indicators within the same sub-region; performing regional classification impact assessment and load sensitivity calculation on the corresponding geographic sub-regions within the target area to obtain the temperature sensitivity coefficients of power load in different categories of regions; and performing power resource allocation management on the corresponding geographic sub-regions within the target area to generate power resource allocation management strategies for different temperature-sensitive areas, thereby executing corresponding power resource optimization allocation management work. This invention enables more accurate load forecasting and optimized power resource allocation.

Description

Technical Field

[0001] This invention relates to the field of temperature prediction technology, and in particular to a method and system for analyzing the sensitivity of load to temperature based on regional characteristic analysis. Background Technology

[0002] In the operation and management of power systems, accurately grasping the changing patterns of power load is crucial for ensuring the stability and economy of power supply. Temperature is one of the important factors affecting power load, and the sensitivity of power load to temperature varies significantly across different regions. For example, in some industrialized areas, where industrial load accounts for a large proportion, the sensitivity to temperature differs greatly from that in areas dominated by residential load. Moreover, the load response to temperature also changes in different seasons and time periods. However, most existing load-temperature sensitivity analysis methods use simple linear regression or models based on global data, neglecting the impact of geographical, climatic, industrial structure, and energy consumption habits of different regions on the load-temperature relationship. These methods cannot accurately reflect the details of load changes within a region, leading to significant errors in load forecasting and power resource dispatch. Summary of the Invention

[0003] Therefore, it is necessary for the present invention to provide a method and system for analyzing the sensitivity of load to temperature based on regional characteristic analysis, in order to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, a method for analyzing the sensitivity of load to temperature based on regional characteristic analysis is proposed, comprising the following steps:

[0005] Step S1: Obtain historical power load data, temperature data, geographic information data and energy usage ratio data corresponding to the target area, and perform time-series synchronization preprocessing on the historical power load data, temperature data and energy usage ratio data corresponding to the target area based on the geographic sub-regions corresponding to the geographic information data and according to the time series, to obtain the standard sequence data of power load, temperature and energy usage under the same sub-region.

[0006] Step S2: Quantify the regional characteristic indicators of the standard sequence data of power load, temperature and energy use in the same sub-region to obtain the regional characteristic indicators of power in the same sub-region, including the power consumption indicators of industrial structure load, temperature fluctuation stability and energy use indicators in the same geographical sub-region.

[0007] Step S3: Based on the corresponding power area characteristic indicators under the same sub-region, conduct regional classification impact assessment on the corresponding geographical sub-regions within the target area to obtain the load-temperature nonlinear impact index or load-temperature linear impact index for different categories of areas; obtain the load response speed and change magnitude of temperature in different categories of areas and combine the load-temperature nonlinear impact index or load-temperature linear impact index to calculate the load sensitivity of the corresponding geographical sub-regions within the target area to obtain the power load sensitivity coefficient to temperature for different categories of areas;

[0008] Step S4: Based on the sensitivity coefficient of power load to temperature in different types of regions, perform power resource allocation management for the corresponding geographical sub-regions within the target area, generate power resource allocation management strategies for different temperature-sensitive regions, and execute the corresponding power resource optimization allocation management work.

[0009] Furthermore, step S1 includes the following steps:

[0010] Step S11: Obtain historical power load data corresponding to the target area from the power company's database;

[0011] Step S12: Obtain the temperature data corresponding to the target area through the meteorological monitoring station;

[0012] Step S13: Obtain geographic information data corresponding to the target area through the geographic information database, including the terrain, landforms and urban topography distribution of the target area;

[0013] Step S14: Obtain the energy usage ratio data corresponding to the target area through the power energy usage records, including the usage amount and ratio of various energy sources;

[0014] Step S15: Divide the corresponding geographic sub-regions based on the terrain, landforms, and urban topography distribution corresponding to the geographic information data. Then, based on the geographic sub-regions, divide the historical power load data, temperature data, and energy usage ratio data of the target area into sub-region data to obtain the power load data, temperature data, and energy usage data corresponding to the same sub-region. By performing time-series synchronization and data preprocessing on the power load data, temperature data, and energy usage data corresponding to the same sub-region according to the time series, the corresponding data are unified in the same time range. Then, clean, remove outliers and missing values, and standardize them to obtain the standard sequence data of power load, temperature, and energy usage corresponding to the same sub-region.

[0015] Furthermore, the geographical sub-regions mentioned in step S15 include mountainous areas, plains, industrial areas, agricultural areas, and residential areas.

[0016] Furthermore, step S2 includes the following steps:

[0017] Step S21: Obtain the corresponding regional industrial structure through the geographical sub-region, and quantify the regional electricity consumption ratio of the standard sequence data of the corresponding power load under the same sub-region based on the regional industrial structure, so as to obtain the electricity load ratio of different industrial structures and obtain the industrial structure load electricity consumption index of the same geographical sub-region.

[0018] Step S22: Quantify the regional temperature fluctuation index of the corresponding temperature standard sequence data in the same sub-region to obtain the corresponding seasonal temperature fluctuation amplitude and fluctuation frequency, and obtain the temperature fluctuation stability index corresponding to the same geographical sub-region.

[0019] Step S23: Quantify the regional energy proportion index by using the usage and proportion of various energy types in the corresponding energy use standard sequence data within the same sub-region, so as to obtain the corresponding clean energy usage proportion and non-clean energy usage proportion, and obtain the energy use index corresponding to the same geographical sub-region.

[0020] Furthermore, step S3 includes the following steps:

[0021] Step S31: Perform principal component characteristic dimensionality reduction on the power region characteristic indicators corresponding to the same sub-region, so as to use principal component analysis technology to perform dimensionality reduction operation and extract the indicators corresponding to the main characteristics of the geographical sub-region to obtain the power region dimensionality reduction indicators corresponding to the same sub-region.

[0022] Step S32: Perform a regional characteristic similarity measurement on the corresponding power region dimensionality reduction indicators under each sub-region to obtain the similarity between the characteristic indicators under each sub-region;

[0023] Step S33: Classify the corresponding geographic sub-regions in the target area based on the similarity between characteristic indicators under each sub-region. If the similarity between characteristic indicators under each sub-region is greater than or equal to 90%, the corresponding geographic sub-region is classified as a type of region with complex load changes. If the similarity between characteristic indicators under each sub-region is less than 90%, the corresponding geographic sub-region is classified as another type of region with relatively simple load changes.

[0024] Step S34: Use a nonlinear model to assess the impact of power load and temperature changes in areas with complex load changes, so as to obtain the load-temperature nonlinear impact index corresponding to areas with complex load categories; use a linear regression model to assess the impact of power load and temperature changes in areas with relatively simple load changes, so as to obtain the load-temperature linear impact index corresponding to areas with simple load categories.

[0025] Step S35: Obtain the response speed and change range of load to temperature in different categories of areas, and combine the load-temperature nonlinear influence index or the load-temperature linear influence index to calculate the load sensitivity of the corresponding geographical sub-regions in the target area, so as to obtain the sensitivity coefficient of power load to temperature in different categories of areas.

[0026] Furthermore, step S35 includes the following steps:

[0027] Step S351: Statistical analysis of response speed is performed on the corresponding power load and temperature changes in different categories of areas to obtain the load response speed to temperature in different categories of areas;

[0028] Step S352: Perform a temperature change trend analysis on the relationship between the power load and temperature change in different categories of regions to obtain the temperature change trend lines under power load conditions in different categories of regions;

[0029] Step S353: Based on the temperature change trend lines corresponding to the power load conditions in different categories of regions, calculate the corresponding temperature change amplitude to obtain the load-temperature change amplitude in different categories of regions;

[0030] Step S354: Based on the response speed and change range of load to temperature in different categories of regions, and combined with the load-temperature nonlinear influence index or the load-temperature linear influence index, the load sensitivity calculation formula is used to calculate the load sensitivity of the corresponding geographical sub-regions in the target region, and obtain the power load sensitivity coefficient to temperature in different categories of regions.

[0031] Furthermore, the specific formula for calculating the load temperature sensitivity mentioned in step S354 is as follows:

[0032] Areas with complex load variations: ;

[0033] Areas where load changes are relatively simple: ;

[0034] In the formula, This is the sensitivity coefficient of regional power load to temperature. The response speed of the load in the region to temperature. The magnitude of temperature change in the regional load. These are the temperature variation parameters corresponding to the geographic sub-region. The load-temperature nonlinear effect index. This is the load-temperature linear influence index.

[0035] Furthermore, step S4 includes the following steps:

[0036] Step S41: Based on the sensitivity coefficients of power load to temperature in different categories of regions, perform regional sensitivity statistical characteristic analysis on the corresponding geographical sub-regions within the target region to obtain the mean, variance, and peak statistical characteristics of temperature sensitivity coefficients in different categories of regions at different time periods.

[0037] Step S42: Based on the statistical characteristics of the mean, variance and peak values ​​of the temperature sensitivity coefficients of different categories of regions in different time periods, perform temperature sensitivity difference analysis on the corresponding geographical sub-regions within the target area to obtain the load temperature sensitivity differences of different categories of regions.

[0038] Step S43: Based on the differences in load temperature sensitivity of different types of regions, perform power resource allocation management for the corresponding geographical sub-regions within the target area, generate power resource allocation management strategies for different temperature-sensitive regions, and execute the corresponding power resource optimization allocation management work.

[0039] Furthermore, the power resource allocation management strategy for different temperature-sensitive areas described in step S43 is as follows: If it is determined that the sensitivity coefficient difference is large and the load growth is fast within the corresponding geographical sub-region, it is planned as a load temperature-sensitive area. Constraints are set for power resource allocation, including grid transmission capacity limits and power generation equipment output limits. Simulated annealing algorithm is used to find the optimal power resource allocation result while satisfying the constraints. If it is determined that the sensitivity coefficient difference is small and the load growth is slow within the corresponding geographical sub-region, it is planned as a load temperature-insensitive area. Power loss adjustment and load control measures are implemented during high-temperature or low-temperature periods to achieve optimized utilization of power resources and energy conservation and emission reduction.

[0040] Furthermore, the present invention also provides a load impact temperature sensitivity analysis system based on regional characteristic analysis, used to perform the load impact temperature sensitivity analysis method based on regional characteristic analysis as described above. The load impact temperature sensitivity analysis system based on regional characteristic analysis includes:

[0041] The regional data time series synchronization module is used to acquire historical power load data, temperature data, geographic information data and energy usage ratio data corresponding to the target area, and perform time series synchronization preprocessing on the historical power load data, temperature data and energy usage ratio data corresponding to the target area based on the geographic sub-regions corresponding to the geographic information data and according to the time series, so as to obtain the standard sequence data of power load, temperature and energy usage under the same sub-region.

[0042] The regional characteristic index quantification module is used to quantify the regional characteristic indexes of the standard sequence data of power load, temperature and energy use in the same sub-region, thereby obtaining the regional characteristic indexes of power in the same sub-region, including the industrial structure load power consumption index, temperature fluctuation stability index and energy use index of the same geographical sub-region.

[0043] The regional load sensitivity calculation module is used to perform regional classification impact assessment on the corresponding geographical sub-regions within the target area based on the corresponding power regional characteristic indicators under the same sub-region, so as to obtain the load-temperature nonlinear impact index or load-temperature linear impact index for different categories of regions; obtain the response speed and change magnitude of load to temperature in different categories of regions and combine the load-temperature nonlinear impact index or load-temperature linear impact index to perform load sensitivity calculation on the corresponding geographical sub-regions within the target area, thereby obtaining the power load sensitivity coefficient to temperature for different categories of regions;

[0044] The power resource allocation management module is used to manage the allocation of power resources to corresponding geographical sub-regions within the target area based on the sensitivity coefficient of power load to temperature in different categories of regions. It generates power resource allocation management strategies for different temperature-sensitive regions to execute corresponding power resource optimization allocation management work.

[0045] The beneficial effects of this invention are:

[0046] 1. The load impact temperature sensitivity analysis method based on regional characteristic analysis proposed in this invention has the following advantages over existing technologies: it provides basic data for subsequent analysis by acquiring historical power load data, temperature data, geographic information data, and energy usage ratio data of the target area. Furthermore, it ensures that these data can be analyzed on the same time axis through time-series synchronization preprocessing. The key to this processing step lies in the consistency and accuracy of the data's temporal sequence. Different data sources often have different time scales and update frequencies. Power load data is updated hourly, daily, or monthly, while temperature data is provided hourly, and energy usage ratio data relies on quarterly or annual statistics. Time-series synchronization processing can adjust the time scale of the data through interpolation or other methods, thereby ensuring that various types of data are comparable at the same point in time. This step not only improves data consistency but also reduces errors caused by time asynchrony, providing a reliable data foundation for subsequent analysis. Secondly, by quantifying regional characteristic indicators such as electricity load, temperature fluctuations, and energy use structure in different geographical sub-regions of the target area, this step is crucial in that it transforms complex regional characteristics into actionable data indicators through the quantification of key factors, facilitating analysis and decision-making. Specifically, the industrial structure load electricity consumption indicator can reveal the electricity demand of different industries in different sub-regions, reflecting the relationship between regional economic development and electricity consumption, thus providing a theoretical basis for electricity demand forecasting and optimization; the temperature fluctuation stability indicator helps identify the degree of impact of temperature changes on electricity load, providing a reference for climate change adaptation measures; and the energy use efficiency indicator reflects the rationality and sustainability of the regional energy structure, helping to assess the energy use efficiency of different regions. These quantified indicators not only provide important reference data for electricity load forecasting and energy management but also provide a more accurate basis for subsequent decision-making in regional electricity management. Then, by analyzing the nonlinear or linear relationship between load and temperature, we can better analyze the impact of geographical, climatic, industrial structure, and energy use characteristics of different regions on the load-temperature relationship. This analysis, by evaluating the nonlinear or linear impact index of load-temperature, can also help identify the response speed and magnitude of temperature to the power load in different regions, and then calculate the power load sensitivity coefficient of the region. The power load sensitivity coefficient is an important indicator for assessing the degree of response of power demand to temperature changes. It helps regional managers assess the degree of fluctuation in power load under temperature changes. The calculation of this indicator helps to accurately predict changes in power demand in the region and provides a scientific basis for coping with the power pressure brought by extreme weather or climate change. It can provide more accurate risk assessment for subsequent power resource management.Finally, by formulating targeted power resource allocation and management strategies based on the sensitivity coefficients of power load to temperature in different geographical sub-regions, the key aspect of this step lies in its ability to effectively optimize power resource allocation and ensure the stable operation of the power system. This is especially important when temperatures fluctuate significantly. Due to the different temperature sensitivities of different regions, the response characteristics of regional power loads vary considerably. This necessitates considering the specific needs of each region when allocating power resources. For example, in regions with large temperature fluctuations, simulated annealing algorithms are used to find the optimal power resource allocation result while satisfying constraints. Therefore, it is necessary to increase power supply capacity during these periods. In contrast, in regions with smaller temperature fluctuations, power supply can be reduced during high or low temperature periods to conserve power resources. This strategy can avoid resource waste or power shortages while improving the load carrying capacity and reliability of power resource dispatch within the power system.

[0047] 2. The load impact temperature sensitivity analysis system based on regional characteristic analysis proposed in this invention is composed of a regional data time-series synchronization module, a regional characteristic index quantification module, a regional load sensitivity calculation module, and a power resource allocation management module. It can realize any load impact temperature sensitivity analysis method based on regional characteristic analysis as described in this invention. It is used to combine the operations between the computer programs running on each module to realize the load impact temperature sensitivity analysis method based on regional characteristic analysis. The internal structure of the system cooperates with each other, which can greatly reduce repetitive work and manpower input, and can quickly and effectively provide a more accurate and efficient load impact temperature sensitivity analysis process based on regional characteristic analysis, thereby simplifying the operation process of the load impact temperature sensitivity analysis system based on regional characteristic analysis. Attached Figure Description

[0048] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0049] Figure 1 This is a schematic diagram of the steps in the method for analyzing the sensitivity of load to temperature based on regional characteristic analysis according to the present invention.

[0050] Figure 2 for Figure 1 A detailed flowchart of step S1;

[0051] Figure 3 for Figure 1 A detailed flowchart of step S2. Detailed Implementation

[0052] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0053] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0054] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0055] To achieve the above objectives, please refer to Figures 1 to 3 This invention provides a method for analyzing the sensitivity of load to temperature based on regional characteristic analysis, the method comprising the following steps:

[0056] Step S1: Obtain historical power load data, temperature data, geographic information data and energy usage ratio data corresponding to the target area, and perform time-series synchronization preprocessing on the historical power load data, temperature data and energy usage ratio data corresponding to the target area based on the geographic sub-regions corresponding to the geographic information data and according to the time series, to obtain the standard sequence data of power load, temperature and energy usage under the same sub-region.

[0057] Step S2: Quantify the regional characteristic indicators of the standard sequence data of power load, temperature and energy use in the same sub-region to obtain the regional characteristic indicators of power in the same sub-region, including the power consumption indicators of industrial structure load, temperature fluctuation stability and energy use indicators in the same geographical sub-region.

[0058] Step S3: Based on the corresponding power area characteristic indicators under the same sub-region, conduct regional classification impact assessment on the corresponding geographical sub-regions within the target area to obtain the load-temperature nonlinear impact index or load-temperature linear impact index for different categories of areas; obtain the load response speed and change magnitude of temperature in different categories of areas and combine the load-temperature nonlinear impact index or load-temperature linear impact index to calculate the load sensitivity of the corresponding geographical sub-regions within the target area to obtain the power load sensitivity coefficient to temperature for different categories of areas;

[0059] Step S4: Based on the sensitivity coefficient of power load to temperature in different types of regions, perform power resource allocation management for the corresponding geographical sub-regions within the target area, generate power resource allocation management strategies for different temperature-sensitive regions, and execute the corresponding power resource optimization allocation management work.

[0060] In the embodiments of this invention, please refer to Figure 1 The diagram shown is a flowchart illustrating the steps of the load-temperature sensitivity analysis method based on regional characteristic analysis according to the present invention. In this example, the load-temperature sensitivity analysis method based on regional characteristic analysis includes the following steps:

[0061] Step S1: Obtain historical power load data, temperature data, geographic information data and energy usage ratio data corresponding to the target area, and perform time-series synchronization preprocessing on the historical power load data, temperature data and energy usage ratio data corresponding to the target area based on the geographic sub-regions corresponding to the geographic information data and according to the time series, to obtain the standard sequence data of power load, temperature and energy usage under the same sub-region.

[0062] In this embodiment of the invention, historical power load data, temperature data, geographic information data, and energy usage ratio data of the target area are acquired. Specifically, power load data can be obtained from historical grid data provided by the power company or extracted from the smart meter system, ensuring coverage of all sub-regions of the target area. The data range should include historical data for at least 3 years, or a longer period of historical data depending on the actual situation of the target area. Temperature data can be obtained from the meteorological department's climate monitoring system, selecting a time period that matches the power load data to ensure timeliness and regional correspondence. Geographic information data should include the administrative divisions of the target area, regional boundaries, and the specific spatial locations of each sub-region, and can typically be extracted from a Geographic Information System (GIS) database. The energy usage ratio data includes the usage proportion of different energy types (such as coal power, wind power, solar power, etc.), which is usually provided by the energy authorities. Through GIS tools and database systems, the electricity load data, temperature data and energy usage ratio data are preprocessed for time series synchronization. The specific operation steps are as follows: First, for each sub-region, the electricity load data, temperature data and energy usage ratio data are matched according to the same time point based on the geographic information data, and missing values, outliers and inconsistencies are handled. Second, interpolation is performed according to the time series to align the time points of different data sources, and finally the standard sequence data of electricity load, temperature and energy usage are obtained in the same sub-region.

[0063] Step S2: Quantify the regional characteristic indicators of the standard sequence data of power load, temperature and energy use in the same sub-region to obtain the regional characteristic indicators of power in the same sub-region, including the power consumption indicators of industrial structure load, temperature fluctuation stability and energy use indicators in the same geographical sub-region.

[0064] In this embodiment of the invention, regional characteristic indicators are quantified by analyzing standard sequence data of electricity load, standard sequence data of temperature, and standard sequence data of energy use. Specifically, firstly, the standard sequence data of electricity load needs to be classified according to the industrial structure of each geographical sub-region, considering the demand characteristics of different industries for electricity load, and calculating the electricity consumption index of industrial structure load. This index can reflect the contribution ratio of each industry (such as manufacturing, service industry, agriculture, etc.) to the electricity load, and can be quantified by historical data of industrial and commercial electricity use. Secondly, the temperature fluctuation stability index should calculate the fluctuation range and frequency of temperature data, and use statistical methods such as standard deviation and root mean square fluctuation to quantify the stability of temperature fluctuation, thereby obtaining the temperature stability index of each geographical sub-region. The energy use index is calculated by analyzing the energy use ratio data and combining the energy consumption characteristics of each sub-region, calculating the usage ratio of different energy sources (including clean energy and non-clean energy) in each region, thereby quantifying the energy use index.

[0065] Step S3: Based on the corresponding power area characteristic indicators under the same sub-region, conduct regional classification impact assessment on the corresponding geographical sub-regions within the target area to obtain the load-temperature nonlinear impact index or load-temperature linear impact index for different categories of areas; obtain the load response speed and change magnitude of temperature in different categories of areas and combine the load-temperature nonlinear impact index or load-temperature linear impact index to calculate the load sensitivity of the corresponding geographical sub-regions within the target area to obtain the power load sensitivity coefficient to temperature for different categories of areas;

[0066] In this embodiment of the invention, regional classification impact assessment is performed based on power area characteristic indicators. Specifically, based on standard sequence data of power load, standard sequence data of temperature, and standard sequence data of energy use, cluster analysis methods (such as K-means clustering or hierarchical clustering) can be used to classify various geographical sub-regions within the target area, dividing them into regions with different temperature sensitivity categories. The power load-temperature nonlinear or linear impact index corresponding to each category of region can be calculated using regression analysis. Linear regression analysis calculates the linear response relationship of power load to temperature changes in regions with relatively simple load changes; nonlinear regression analysis can handle the nonlinear relationship between power load and temperature in regions with complex load changes. Through the parameters of the regression model, the load-temperature nonlinear or linear impact index corresponding to each region can be obtained, further evaluating the load's response speed and change magnitude to temperature. Based on the regression analysis results, the load sensitivity coefficient of each geographical sub-region can be calculated, specifically: Load sensitivity coefficient = The ratio of the magnitude of the response of the power load to the temperature change to the magnitude of the temperature change is multiplied by the corresponding impact index. These coefficients are used to assess the sensitivity of the power load of different categories of areas within the target region to temperature changes, and finally obtain the sensitivity coefficient of the power load of different categories of areas to temperature.

[0067] Step S4: Based on the sensitivity coefficient of power load to temperature in different types of regions, perform power resource allocation management for the corresponding geographical sub-regions within the target area, generate power resource allocation management strategies for different temperature-sensitive regions, and execute the corresponding power resource optimization allocation management work.

[0068] In this embodiment of the invention, power resource allocation management is carried out based on the sensitivity coefficient of power load to temperature in different categories of regions. First, by analyzing the results of regional classification impact assessment, regions with high sensitivity of power load to temperature changes are defined as temperature-sensitive regions. For these regions, the power resource allocation management strategy should be optimized according to the characteristics of load changes and temperature fluctuations, prioritizing the power demand of these regions during peak temperature periods. For example, energy allocation can be dynamically adjusted through an intelligent dispatch system, prioritizing the dispatch of low-carbon or renewable energy sources (such as solar and wind power) to reduce dependence on traditional fossil fuels and improve the system's energy efficiency and environmental friendliness. Specifically, optimization algorithms (such as linear algorithms) can be used. The system uses algorithms such as integer programming to calculate the optimal power resource allocation scheme. Combined with regional temperature change trends, it implements power demand forecasting and load regulation in temperature-sensitive areas. By adjusting the power supply and optimizing resource allocation, it ensures that the power load will not experience power shortages under conditions of large temperature fluctuations or extreme weather, thereby ensuring the stability and reliability of the power system. For areas where the load is not sensitive to temperature, the power resource allocation strategy is different. These areas have small differences in temperature sensitivity coefficients and slow load growth, so temperature changes have little impact on them. Therefore, power loss adjustment and load control measures can be implemented during high or low temperature periods to achieve energy conservation and emission reduction, and finally, the corresponding power resource optimization allocation management work is carried out.

[0069] Furthermore, step S1 includes the following steps:

[0070] Step S11: Obtain historical power load data corresponding to the target area from the power company's database;

[0071] Step S12: Obtain the temperature data corresponding to the target area through the meteorological monitoring station;

[0072] Step S13: Obtain geographic information data corresponding to the target area through the geographic information database, including the terrain, landforms and urban topography distribution of the target area;

[0073] Step S14: Obtain the energy usage ratio data corresponding to the target area through the power energy usage records, including the usage amount and ratio of various energy sources;

[0074] Step S15: Divide the corresponding geographic sub-regions based on the terrain, landforms, and urban topography distribution corresponding to the geographic information data. Then, based on the geographic sub-regions, divide the historical power load data, temperature data, and energy usage ratio data of the target area into sub-region data to obtain the power load data, temperature data, and energy usage data corresponding to the same sub-region. By performing time-series synchronization and data preprocessing on the power load data, temperature data, and energy usage data corresponding to the same sub-region according to the time series, the corresponding data are unified in the same time range. Then, clean, remove outliers and missing values, and standardize them to obtain the standard sequence data of power load, temperature, and energy usage corresponding to the same sub-region.

[0075] As an embodiment of the present invention, reference is made to... Figure 2 As shown, Figure 1 A detailed flowchart of step S1 is shown below. In this embodiment, step S1 includes the following steps:

[0076] Step S11: Obtain historical power load data corresponding to the target area from the power company's database;

[0077] In this embodiment of the invention, historical power load data of the target area is obtained from the power company's database. First, based on the geographical boundaries of the target area (such as administrative divisions like cities, counties, and districts), the corresponding historical load data is obtained through the power company's load management system or smart meter system. This data includes specific time series of historical loads, usually in hourly or daily units, covering electricity consumption under different seasons and weather conditions. The data acquisition methods used can be API interfaces, database queries, or batch data export. After obtaining the data, it is necessary to ensure its time coverage, accuracy, and compatibility with other data sources. Through database management tools (such as MySQL, PostgreSQL, etc.), data query operations are performed to extract and confirm the accuracy of the data, ultimately obtaining the historical power load data corresponding to the target area.

[0078] Step S12: Obtain the temperature data corresponding to the target area through the meteorological monitoring station;

[0079] In this embodiment of the invention, temperature data of the target area is obtained from meteorological monitoring stations or the National Meteorological Administration. This data can be obtained through the API interface of the government meteorological department, the open meteorological data platform, or the regularly published meteorological reports. The temperature data includes parameters such as historical daily average temperature, maximum temperature, and minimum temperature. It is usually collected by automatic weather stations. The time range of the data should be aligned with historical power load data, and the time accuracy of the data should reach the hour level for subsequent time series analysis. For long-term series data, the temperature data is usually stored in a Geographic Information System (GIS) database. Spatial query methods can be used to obtain the corresponding temperature data based on the latitude and longitude information of the target area, and finally obtain the temperature data corresponding to the target area.

[0080] Step S13: Obtain geographic information data corresponding to the target area through the geographic information database, including the terrain, landforms and urban topography distribution of the target area;

[0081] In this embodiment of the invention, geographic information data of the target area is obtained through a geographic information database. The specific content includes the topography, landforms, and urban terrain distribution of the area. The data in the geographic information database is usually obtained through remote sensing technology, satellite imagery, or field surveys. The data format can be vector data or raster data. First, based on the administrative boundaries or specific regional divisions of the target area, relevant topographic and landform maps are retrieved. The data includes the spatial distribution of natural geographic features such as mountains, hills, rivers, and lakes, as well as urban terrain information such as urban elevation differences and transportation networks. Because geographic factors (such as temperature differences between mountainous areas and plains) have a significant impact on electricity demand, the data is visualized and spatially analyzed using GIS software (such as ArcGIS or QGIS) to accurately extract geographic feature information within the target area, and finally, the geographic information data corresponding to the target area is obtained.

[0082] Step S14: Obtain the energy usage ratio data corresponding to the target area through the power energy usage records, including the usage amount and ratio of various energy sources;

[0083] In this embodiment of the invention, energy usage records of the target area are obtained through power companies or local energy management departments. These records typically include consumption data for different types of energy, such as coal, electricity, natural gas, oil, and renewable energy (wind, solar, etc.). The data sources include energy consumption reports provided by power companies or real-time data collected from smart metering devices (such as smart meters, heat meters, etc.). These data can reflect the usage ratios of various energy types and their changing trends. Based on the statistics of energy consumption, the proportions of different energy types are calculated using a weighted average method or ratio, such as the proportion of coal-fired power and the proportion of clean energy. By analyzing the energy usage data, the characteristics of energy use in the target area and its progress in the clean energy transition can be identified, ultimately yielding the corresponding energy usage ratio data for the target area.

[0084] Step S15: Divide the corresponding geographic sub-regions based on the terrain, landforms, and urban topography distribution corresponding to the geographic information data. Then, based on the geographic sub-regions, divide the historical power load data, temperature data, and energy usage ratio data of the target area into sub-region data to obtain the power load data, temperature data, and energy usage data corresponding to the same sub-region. By performing time-series synchronization and data preprocessing on the power load data, temperature data, and energy usage data corresponding to the same sub-region according to the time series, the corresponding data are unified in the same time range. Then, clean, remove outliers and missing values, and standardize them to obtain the standard sequence data of power load, temperature, and energy usage corresponding to the same sub-region.

[0085] In this embodiment of the invention, by analyzing the topography, landforms and urban terrain information of the target area, multiple geographical sub-regions are divided, such as mountainous areas, plains, industrial areas, agricultural areas and residential areas. These areas are spatially analyzed and divided through a GIS platform. The division of each geographical sub-region is based on the geographical features, urban planning and distribution of regional economic activities of the region. After the division is completed, data is divided and matched according to the historical power load data, temperature data and energy usage data corresponding to each sub-region. At this time, SQL queries or data processing libraries in Python (such as Pandas) can be used to extract the data subsets corresponding to each sub-region. Next, time-series synchronization and data preprocessing are required for data from different sub-regions. First, ensure that the time ranges of power load data, temperature data, and energy usage data are consistent. Use time series matching algorithms to align the data and ensure that the data items corresponding to each time point are consistent. During data cleaning, remove outliers and missing values, and use interpolation methods (such as linear interpolation, spline interpolation, etc.) to supplement missing values ​​to ensure data integrity. Subsequently, standardization is performed to unify the dimensions of different data sources, so that data such as power load, temperature, and energy usage can be compared on the same scale. Finally, standard sequence data of power load, temperature, and energy usage are obtained in the same sub-region.

[0086] Furthermore, the geographical sub-regions mentioned in step S15 include mountainous areas, plains, industrial areas, agricultural areas, and residential areas.

[0087] Furthermore, step S2 includes the following steps:

[0088] Step S21: Obtain the corresponding regional industrial structure through the geographical sub-region, and quantify the regional electricity consumption ratio of the standard sequence data of the corresponding power load under the same sub-region based on the regional industrial structure, so as to obtain the electricity load ratio of different industrial structures and obtain the industrial structure load electricity consumption index of the same geographical sub-region.

[0089] Step S22: Quantify the regional temperature fluctuation index of the corresponding temperature standard sequence data in the same sub-region to obtain the corresponding seasonal temperature fluctuation amplitude and fluctuation frequency, and obtain the temperature fluctuation stability index corresponding to the same geographical sub-region.

[0090] Step S23: Quantify the regional energy proportion index by using the usage and proportion of various energy types in the corresponding energy use standard sequence data within the same sub-region, so as to obtain the corresponding clean energy usage proportion and non-clean energy usage proportion, and obtain the energy use index corresponding to the same geographical sub-region.

[0091] As an embodiment of the present invention, reference is made to... Figure 3 As shown, Figure 1 A detailed flowchart of step S2 is shown below. In this embodiment, step S2 includes the following steps:

[0092] Step S21: Obtain the corresponding regional industrial structure through the geographical sub-region, and quantify the regional electricity consumption ratio of the standard sequence data of the corresponding power load under the same sub-region based on the regional industrial structure, so as to obtain the electricity load ratio of different industrial structures and obtain the industrial structure load electricity consumption index of the same geographical sub-region.

[0093] In this embodiment of the invention, industrial structure data of various industries within a sub-region are obtained through geographic sub-region information. Specifically, an established Geographic Information System (GIS) or regional division database is used to retrieve the proportion data of each industry in the sub-region based on administrative divisions or economic activity area divisions. For example, data from the statistics bureau or relevant industry reports can be used to analyze the output value and employment of different industries in the region to obtain detailed information on industrial distribution. Next, the industrial structure data is combined with the corresponding standard sequence data of electricity load. By comparing the correlation between historical electricity consumption and the output value of each industry, the proportion of electricity load corresponding to different industries in the sub-region is obtained. Using statistical regression analysis methods or machine learning algorithms (such as linear regression, support vector machines, etc.), a mathematical model can be established between the electricity load data and the industrial structure data to quantify the contribution of each industry to the electricity load. The result is the electricity load proportion index of different industrial structures in the sub-region, and finally, the electricity load index of the industrial structure corresponding to the same geographic sub-region is obtained.

[0094] Step S22: Quantify the regional temperature fluctuation index of the corresponding temperature standard sequence data in the same sub-region to obtain the corresponding seasonal temperature fluctuation amplitude and fluctuation frequency, and obtain the temperature fluctuation stability index corresponding to the same geographical sub-region.

[0095] In this embodiment of the invention, temperature data of the geographical sub-region is collected. This data can typically be obtained from meteorological bureaus or related meteorological data service platforms. The temperature standard sequence data should include daily temperature variation data of the region over a long period (e.g., at least 10 years). For each sub-region, based on the temperature data, seasonal temperature fluctuation analysis is first performed to identify the temperature fluctuation amplitude of the region in different seasons. This involves calculating the temperature standard deviation, the difference between the maximum and minimum values ​​of the temperature in different months or quarters of the region, and thus obtaining the temperature fluctuation amplitude index. Then, based on time series analysis methods (such as Fourier transform or wavelet transform), frequency domain analysis is performed on the temperature data to identify the frequency characteristics of temperature fluctuations and further quantify the frequency of temperature fluctuations. Through these quantitative indicators, the seasonal fluctuation amplitude and frequency characteristics of regional temperature fluctuations can be obtained, and finally, the temperature fluctuation stability index of the sub-region is obtained.

[0096] Step S23: Quantify the regional energy proportion index by using the usage and proportion of various energy types in the corresponding energy use standard sequence data within the same sub-region, so as to obtain the corresponding clean energy usage proportion and non-clean energy usage proportion, and obtain the energy use index corresponding to the same geographical sub-region.

[0097] In this embodiment of the invention, energy usage data, particularly the usage and proportion of different types of energy, is collected within the sub-region. This data can be obtained from local energy management departments, energy statistical reports, or industrial energy consumption databases. Next, the usage proportion of each type of energy in the region is quantified according to energy type (e.g., coal, natural gas, oil, electricity, renewable energy, etc.). For example, statistical analysis of energy consumption can be used to calculate the ratio of the usage of each type of energy to the total energy consumption, thereby obtaining the usage proportion of each type of energy within the region. By distinguishing between clean energy (e.g., wind, solar, hydropower, etc.) and non-clean energy (e.g., coal, oil, etc.), their proportions are calculated separately. To ensure data accuracy, a weighted average method or multiple regression analysis based on energy consumption can be used to obtain a more accurate ratio of clean and non-clean energy usage, ultimately yielding energy usage indicators corresponding to the same geographical sub-region.

[0098] Furthermore, step S3 includes the following steps:

[0099] Step S31: Perform principal component characteristic dimensionality reduction on the power region characteristic indicators corresponding to the same sub-region, so as to use principal component analysis technology to perform dimensionality reduction operation and extract the indicators corresponding to the main characteristics of the geographical sub-region to obtain the power region dimensionality reduction indicators corresponding to the same sub-region.

[0100] In this embodiment of the invention, by selecting power regional characteristic indicators within the same geographical sub-region, including but not limited to industrial structure load electricity consumption indicators, temperature fluctuation stability indicators, and energy use indicators, principal component analysis (PCA) is applied to these multidimensional datasets to extract a smaller set of indicators that represent the core characteristics of the region. Specifically, the power regional characteristic indicators are first standardized to have a mean of 0 and a standard deviation of 1 to ensure that all indicators participate in the analysis on the same scale. Then, the covariance matrix between the indicators is calculated, and the corresponding principal components are extracted through eigenvalue decomposition or singular value decomposition techniques. Each principal component represents a major direction of change in the regional characteristics. The principal components are sorted according to their contribution rate, and the top few principal components with a cumulative contribution rate of over 80% are retained. These principal components will serve as the characteristic indicators of the power region after dimensionality reduction, reflecting the main influencing factors of electricity demand and climate change within the geographical sub-region, and finally obtaining the power regional dimensionality reduction indicators corresponding to the same sub-region.

[0101] Step S32: Perform a regional characteristic similarity measurement on the corresponding power region dimensionality reduction indicators under each sub-region to obtain the similarity between the characteristic indicators under each sub-region;

[0102] In this embodiment of the invention, by calculating the similarity between the dimensionality-reduced indicators of the power region within different geographical sub-regions, firstly, using Euclidean distance, cosine similarity, or other suitable similarity measurement methods, the degree of similarity between each geographical sub-region in the principal component space after dimensionality reduction is measured. Through these similarity measurement methods, a similarity matrix between each sub-region can be obtained, where each element in the matrix represents the characteristic similarity of the corresponding sub-region. Specifically, for each pair of sub-regions, the distance or angle between their dimensionality-reduced indicator vectors is calculated to obtain a value between 0 and 1, where 1 represents complete similarity and 0 represents complete dissimilarity. By performing this process on all sub-regions of the entire region, the regional characteristic similarity between each sub-region can be further analyzed, and finally, the similarity between characteristic indicators under each sub-region can be obtained.

[0103] Step S33: Classify the corresponding geographic sub-regions in the target area based on the similarity between characteristic indicators under each sub-region. If the similarity between characteristic indicators under each sub-region is greater than or equal to 90%, the corresponding geographic sub-region is classified as a type of region with complex load changes. If the similarity between characteristic indicators under each sub-region is less than 90%, the corresponding geographic sub-region is classified as another type of region with relatively simple load changes.

[0104] In this embodiment of the invention, regional characteristics are classified based on the similarity matrix of the characteristic indicators of each sub-region obtained previously. If the similarity between the dimensionality-reduced characteristic indicators of a certain sub-region is greater than or equal to 90%, it indicates that the relationship between the power load change and the temperature change in that sub-region is relatively stable, and the load change is relatively complex. In this case, the sub-region is classified as a "complex load change region". Conversely, if the similarity is less than 90%, the power load change in that sub-region is relatively simple, and it is classified as a "relatively simple load change region". In implementation, clustering algorithms (such as K-means, hierarchical clustering, etc.) are used to classify each sub-region according to similarity. Thresholds are set to ensure the accuracy of classification. Furthermore, the validity and accuracy of the classification results are ensured by statistically analyzing and verifying the load data of each type of region.

[0105] Step S34: Use a nonlinear model to assess the impact of power load and temperature changes in areas with complex load changes, so as to obtain the load-temperature nonlinear impact index corresponding to areas with complex load categories; use a linear regression model to assess the impact of power load and temperature changes in areas with relatively simple load changes, so as to obtain the load-temperature linear impact index corresponding to areas with simple load categories.

[0106] In this embodiment of the invention, for "areas with complex load changes," a nonlinear model (such as support vector machine regression, neural network, etc.) is used to assess the impact of power load and temperature changes in the area. Specifically, historical power load data and corresponding temperature data for the area are first collected. Then, a nonlinear regression model is trained, using power load and temperature as inputs and outputs. After training, if the model can output the response relationship of power load under temperature changes, then the nonlinear relationship between load and temperature is considered. This is typically analyzed using multinomial regression or other nonlinear models, and its calculation formula can be expressed as: Nonlinear Impact Index = ,in: These are regression coefficients (usually obtained by fitting historical data). It's the temperature. The order of the nonlinear polynomial can be selected according to the complexity of the data, thereby generating the load-temperature nonlinear impact index. For "regions with relatively simple load changes", a linear regression model is used for impact assessment. By establishing a linear relationship model between load and temperature, the load-temperature linear impact index of this type of region is obtained, which is the slope of the load and temperature in the linear regression. The specific steps include data preprocessing, feature selection, model training, and model evaluation and tuning to obtain the impact index of various regions, and finally obtain the load-temperature nonlinear impact index and the load-temperature linear impact index.

[0107] Step S35: Obtain the response speed and change range of load to temperature in different categories of areas, and combine the load-temperature nonlinear influence index or the load-temperature linear influence index to calculate the load sensitivity of the corresponding geographical sub-regions in the target area, so as to obtain the sensitivity coefficient of power load to temperature in different categories of areas.

[0108] In this embodiment of the invention, by acquiring the response speed and magnitude of various regional loads to temperature changes, and combining the nonlinear or linear influence index from the aforementioned steps for sensitivity calculation, firstly, the response speed of each sub-regional load to temperature changes is calculated. This can be achieved by performing differential analysis on load and temperature data to obtain the load change rate corresponding to temperature changes. Then, by combining the load-temperature nonlinear influence index or linear influence index, the load sensitivity coefficient of each sub-region is further calculated. In this process, in areas with complex load changes, quantitative calculations are performed by combining the corresponding response speed, temperature change magnitude, temperature change value, and load-temperature nonlinear influence index. In areas with relatively simple load changes, calculations are performed by combining the corresponding response speed, temperature change magnitude, temperature change value, and load-temperature linear influence index. This ensures that the sensitivity calculation results accurately reflect the power load response characteristics of each region under temperature changes, and finally, the sensitivity coefficients of power loads in different categories of regions to temperature are obtained.

[0109] Furthermore, step S35 includes the following steps:

[0110] Step S351: Statistical analysis of response speed is performed on the corresponding power load and temperature changes in different categories of areas to obtain the load response speed to temperature in different categories of areas;

[0111] In this embodiment of the invention, power load and temperature change data for different categories of regions are collected. The data can come from real-time meteorological monitoring and power load data acquisition systems. The classification of regions generally includes urban areas, suburbs, and rural areas. By selecting load data and temperature change data for a specific time period, the correlation between power load and temperature change in each category of region is calculated. Specifically, time series analysis methods can be used to analyze the ratio of temperature change value to the difference in response time between load and temperature, thereby obtaining the response speed of load to temperature change in each category of region. In statistical analysis, regression analysis methods can be used to establish a quantitative relationship between load and temperature change, thereby determining the response speed of different categories of regions at different time scales, and finally obtaining the response speed of load to temperature in different categories of regions.

[0112] Step S352: Perform a temperature change trend analysis on the relationship between the power load and temperature change in different categories of regions to obtain the temperature change trend lines under power load conditions in different categories of regions;

[0113] In this embodiment of the invention, by comparing power load data and temperature data of different categories of regions, the trend of temperature change is analyzed based on the load level of different categories of regions. For each category of region, based on the collected temperature data, a linear or nonlinear regression model is used to fit the trend line of temperature change. Specifically, the least squares method, moving average method, and other methods can be used to fit the temperature data to obtain the trend line of temperature change under different power load conditions. Through trend analysis, it is determined whether there is a significant difference in the trend of temperature change during peak load periods and off-peak periods, thereby revealing the influence pattern of load on temperature change in different regions, and finally obtaining the corresponding temperature change trend line of different categories of regions under power load conditions.

[0114] Step S353: Based on the temperature change trend lines corresponding to the power load conditions in different categories of regions, calculate the corresponding temperature change amplitude to obtain the load-temperature change amplitude in different categories of regions;

[0115] In this embodiment of the invention, the magnitude of temperature change with load within a specific time range is calculated based on a previously obtained temperature change trend line. For example, by selecting a period of one day or one week, the change data of power load is recorded, and combined with the temperature trend line, the magnitude of temperature during load change is calculated. The calculation formula can be obtained by dividing the temperature change by the load change to obtain a rate of change. For each category of area, this process will provide quantitative data on the magnitude of temperature change under different load levels. It should be noted that the magnitude of temperature change is not only directly affected by power load, but also regulated by external factors such as weather and terrain. Therefore, the possible interference of these factors on the magnitude of temperature change should be comprehensively considered during the calculation process. In addition, the stability and volatility of the magnitude of temperature change can be analyzed by multiple calculations over multiple time periods, and finally the magnitude of temperature change due to load in different categories of areas can be obtained.

[0116] Step S354: Based on the response speed and change range of load to temperature in different categories of regions, and combined with the load-temperature nonlinear influence index or the load-temperature linear influence index, the load sensitivity calculation formula is used to calculate the load sensitivity of the corresponding geographical sub-regions in the target region, and obtain the power load sensitivity coefficient to temperature in different categories of regions.

[0117] In this embodiment of the invention, in areas with complex load changes, quantitative calculations are performed by combining the corresponding response speed, temperature change amplitude, temperature change value, and load-temperature nonlinear influence index. In areas with relatively simple load changes, calculations are performed by combining the corresponding response speed, temperature change amplitude, temperature change value, and load-temperature linear influence index. The load-temperature nonlinear influence index considers the complex influence pattern of load changes on temperature changes and is suitable for calculations under high load and extreme temperature conditions. The load-temperature linear influence index is suitable for areas where load and temperature changes have a linear relationship. Based on this, the sensitivity of geographical sub-regions within the target area is calculated using the load-temperature sensitivity calculation formula. The calculation formula includes multiple parameters such as the amplitude of load changes and the amplitude of temperature changes. By calculating different sub-regions, the load sensitivity coefficient of each region is obtained, and finally, the sensitivity coefficients of power load to temperature for different categories of regions are obtained. In addition, the load-temperature sensitivity calculation formula can also use any temperature sensitivity detection algorithm in the field to replace the load sensitivity calculation process, and is not limited to this load-temperature sensitivity calculation formula.

[0118] Furthermore, the specific formula for calculating the load temperature sensitivity mentioned in step S354 is as follows:

[0119] Areas with complex load variations: ;

[0120] Areas where load changes are relatively simple: ;

[0121] In the formula, This is the sensitivity coefficient of regional power load to temperature. The response speed of the load in the region to temperature. The magnitude of temperature change in the regional load. These are the temperature variation parameters corresponding to the geographic sub-region. The load-temperature nonlinear effect index. This is the load-temperature linear influence index.

[0122] This invention, through the use of a specific mathematical model and verification, derives a load-temperature sensitivity calculation formula for calculating the load sensitivity of corresponding geographical sub-regions within a target area. This formula provides a way to quantitatively measure how electricity load responds to temperature changes using a mathematical model. This helps to understand the impact of temperature changes on electricity demand at the system level, especially in different regions or different types of geographical areas. By employing two different formulas (a nonlinear formula for complex regions and a linear formula for simple regions), the different response characteristics of electricity load to temperature in different regions can be more accurately reflected. For example, in complex regions, where the relationship between load and temperature changes is more complex, the nonlinear formula is more effective; while in simple regions, where the relationship between temperature and electricity load is more direct, the linear formula is more applicable. By calculating the sensitivity coefficients for different regions, better electricity load forecasting and power system dispatch optimization can be achieved. Especially in areas with large temperature variations, the sensitivity coefficients can help decision-makers understand which regions have a more significant impact on load due to temperature changes, thereby enabling targeted measures, such as rationally allocating energy resources during hot or cold weather to avoid power supply overload. Using load-temperature nonlinear and linear load-temperature impact indices can help further optimize the calculation process, making it closer to actual climate conditions and electricity demand response. These indices can be adjusted according to different environmental conditions or historical data, thereby improving the model's accuracy and adaptability. In summary, this formula fully considers the sensitivity coefficient of regional electricity load to temperature. The response speed of regional load to temperature The magnitude of temperature change in regional load Temperature variation parameters corresponding to the geographic sub-region Load-temperature nonlinear influence index Load-temperature linear influence index In order to combine the corresponding response speed in areas with complex load variations Temperature variation range Temperature change value and the load-temperature nonlinear influence index Quantization calculations were performed to establish a functional relationship. In areas where load changes are relatively simple, by combining the corresponding stress rate Temperature variation range Temperature change value and the load-temperature linear influence index Calculations were performed to establish another functional relationship. This formula enables the calculation of load sensitivity for corresponding geographical sub-regions within the target area, thereby improving the accuracy and applicability of the load temperature sensitivity calculation formula.

[0123] Furthermore, step S4 includes the following steps:

[0124] Step S41: Based on the sensitivity coefficients of power load to temperature in different categories of regions, perform regional sensitivity statistical characteristic analysis on the corresponding geographical sub-regions within the target region to obtain the mean, variance, and peak statistical characteristics of temperature sensitivity coefficients in different categories of regions at different time periods.

[0125] In this embodiment of the invention, by acquiring power load data and corresponding temperature data of different geographical sub-regions within the target area, these data are divided into different categories according to region. Combining the actual geographical characteristics and climate conditions of the region, the previously quantified load temperature sensitivity coefficient is extracted. Then, by calculating the mean, variance, and peak statistical characteristics of the temperature sensitivity coefficient of each geographical sub-region in different time periods, the temperature sensitivity characteristics of each geographical sub-region are analyzed in detail. The mean reflects the load change trend of the geographical region under different temperatures; the variance is used to measure the impact of temperature changes on load fluctuations in different time periods; and the peak represents the extreme case of load response when the temperature changes in the region. These statistical characteristics provide basic data for subsequent analysis. Specifically, the calculation can be performed using statistical analysis tools such as MATLAB or the Pandas library in Python, combined with time series data of temperature and load, to finally obtain the mean, variance, and peak statistical characteristics of the temperature sensitivity coefficient of different categories of regions in different time periods.

[0126] Step S42: Based on the statistical characteristics of the mean, variance and peak values ​​of the temperature sensitivity coefficients of different categories of regions in different time periods, perform temperature sensitivity difference analysis on the corresponding geographical sub-regions within the target area to obtain the load temperature sensitivity differences of different categories of regions.

[0127] In this embodiment of the invention, by utilizing previously obtained statistical characteristic data of temperature sensitivity coefficients in different regions, further analysis of temperature sensitivity differences is conducted. Based on the mean, variance, and peak value of the sensitivity coefficient of each geographical sub-region, the degree of difference between regions is calculated, with particular attention paid to regions with large differences in sensitivity coefficients. By comparing the temperature sensitivity data of different categories of regions, the differences in load response under their respective temperature conditions are analyzed, thereby identifying regions where temperature changes have a significant impact on power load. In practice, cluster analysis or principal component analysis can be used to process the data to clarify the temperature sensitivity differences between different geographical regions. This operation can be performed using the scikit-learn library in Python for cluster analysis, or using the PCA function in MATLAB for principal component analysis to identify temperature-sensitive and non-sensitive regions, ultimately obtaining the load temperature sensitivity differences corresponding to different categories of regions.

[0128] Step S43: Based on the differences in load temperature sensitivity of different types of regions, perform power resource allocation management for the corresponding geographical sub-regions within the target area, generate power resource allocation management strategies for different temperature-sensitive regions, and execute the corresponding power resource optimization allocation management work.

[0129] In this embodiment of the invention, based on the temperature sensitivity difference analysis results obtained from previous analysis, the power resource allocation management strategy for different geographical sub-regions within the target area is determined. If the temperature sensitivity coefficient of a certain geographical sub-region is significantly different and the load growth is rapid, then the region is planned as a load-temperature-sensitive region. Such regions will be significantly affected by extreme temperature changes, resulting in load surges. Therefore, it is necessary to rationally allocate power resources for optimized management. When allocating power resources, the grid transmission capacity limit and the power generation equipment output limit must be considered. By using the simulated annealing algorithm, combined with the actual constraints of the power system, such as grid load and power plant output, a global optimization search is performed to find the optimal power resource allocation scheme. The simulated annealing algorithm gradually approaches the optimal solution by simulating the physical annealing process. Through multiple iterations, it escapes the local optimum and finally reaches the global optimum. In implementation, the simulated annealing algorithm can be implemented using the SciPy library in Python, combined with grid constraints for power resource optimization. For areas where the load is not sensitive to temperature, the power resource allocation strategy is different. These areas have small differences in temperature sensitivity coefficients and slow load growth, so temperature changes have little impact on them. Therefore, power loss adjustment and load control measures can be implemented during high or low temperature periods to achieve energy conservation and emission reduction. In these areas, load management focuses on achieving the economical use of power resources by adjusting power loss and optimizing load control. Specifically, demand response management (DRM) technology can be used to dynamically adjust the power load in the area based on actual load demand and temperature changes, thereby optimizing resource allocation, improving the overall operating efficiency of the power system, and ultimately executing the corresponding power resource optimization allocation management work.

[0130] Furthermore, the power resource allocation management strategy for different temperature-sensitive areas described in step S43 is as follows: If it is determined that the sensitivity coefficient difference is large and the load growth is fast within the corresponding geographical sub-region, it is planned as a load temperature-sensitive area. Constraints are set for power resource allocation, including grid transmission capacity limits and power generation equipment output limits. Simulated annealing algorithm is used to find the optimal power resource allocation result while satisfying the constraints. If it is determined that the sensitivity coefficient difference is small and the load growth is slow within the corresponding geographical sub-region, it is planned as a load temperature-insensitive area. Power loss adjustment and load control measures are implemented during high-temperature or low-temperature periods to achieve optimized utilization of power resources and energy conservation and emission reduction.

[0131] Furthermore, the present invention also provides a load impact temperature sensitivity analysis system based on regional characteristic analysis, used to perform the load impact temperature sensitivity analysis method based on regional characteristic analysis as described above. The load impact temperature sensitivity analysis system based on regional characteristic analysis includes:

[0132] The regional data time series synchronization module is used to acquire historical power load data, temperature data, geographic information data and energy usage ratio data corresponding to the target area, and perform time series synchronization preprocessing on the historical power load data, temperature data and energy usage ratio data corresponding to the target area based on the geographic sub-regions corresponding to the geographic information data and according to the time series, so as to obtain the standard sequence data of power load, temperature and energy usage under the same sub-region.

[0133] The regional characteristic index quantification module is used to quantify the regional characteristic indexes of the standard sequence data of power load, temperature and energy use in the same sub-region, thereby obtaining the regional characteristic indexes of power in the same sub-region, including the industrial structure load power consumption index, temperature fluctuation stability index and energy use index of the same geographical sub-region.

[0134] The regional load sensitivity calculation module is used to perform regional classification impact assessment on the corresponding geographical sub-regions within the target area based on the corresponding power regional characteristic indicators under the same sub-region, so as to obtain the load-temperature nonlinear impact index or load-temperature linear impact index for different categories of regions; obtain the response speed and change magnitude of load to temperature in different categories of regions and combine the load-temperature nonlinear impact index or load-temperature linear impact index to perform load sensitivity calculation on the corresponding geographical sub-regions within the target area, thereby obtaining the power load sensitivity coefficient to temperature for different categories of regions;

[0135] The power resource allocation management module is used to manage the allocation of power resources to corresponding geographical sub-regions within the target area based on the sensitivity coefficient of power load to temperature in different categories of regions. It generates power resource allocation management strategies for different temperature-sensitive regions to execute corresponding power resource optimization allocation management work.

[0136] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0137] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. 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 invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for analyzing the sensitivity of load to temperature based on regional characteristic analysis, characterized in that, Includes the following steps: Step S1: Obtain historical power load data, temperature data, geographic information data and energy usage ratio data corresponding to the target area, and perform time-series synchronization preprocessing on the historical power load data, temperature data and energy usage ratio data corresponding to the target area based on the geographic sub-regions corresponding to the geographic information data and according to the time series, to obtain the standard sequence data of power load, temperature and energy usage under the same sub-region. Step S2: Quantify the regional characteristic indicators of the standard sequence data of power load, temperature and energy use in the same sub-region to obtain the regional characteristic indicators of power in the same sub-region, including the power consumption indicators of industrial structure load, temperature fluctuation stability and energy use indicators in the same geographical sub-region. Step S3: Based on the corresponding power area characteristic indicators within the same sub-region, conduct a regional classification impact assessment of the corresponding geographical sub-regions within the target area to obtain the load-temperature nonlinear impact index or load-temperature linear impact index for different categories of regions; obtain the load response speed and change magnitude to temperature within different categories of regions, and combine the load-temperature nonlinear impact index or load-temperature linear impact index to calculate the load sensitivity of the corresponding geographical sub-regions within the target area, obtaining the power load sensitivity coefficient to temperature for different categories of regions; Step S3 includes the following steps: Step S31: Perform principal component characteristic dimensionality reduction on the power region characteristic indicators corresponding to the same sub-region, so as to use principal component analysis technology to perform dimensionality reduction operation and extract the indicators corresponding to the main characteristics of the geographical sub-region to obtain the power region dimensionality reduction indicators corresponding to the same sub-region. Step S32: Perform a regional characteristic similarity measurement on the corresponding power region dimensionality reduction indicators under each sub-region to obtain the similarity between the characteristic indicators under each sub-region; Step S33: Classify the corresponding geographic sub-regions in the target area based on the similarity between characteristic indicators under each sub-region. If the similarity between characteristic indicators under each sub-region is greater than or equal to 90%, the corresponding geographic sub-region is classified as a type of region with complex load changes. If the similarity between characteristic indicators under each sub-region is less than 90%, the corresponding geographic sub-region is classified as another type of region with relatively simple load changes. Step S4: Based on the sensitivity coefficient of power load to temperature in different types of regions, perform power resource allocation management for the corresponding geographical sub-regions within the target area, generate power resource allocation management strategies for different temperature-sensitive regions, and execute the corresponding power resource optimization allocation management work.

2. The method for analyzing the sensitivity of load impact to temperature based on regional characteristic analysis according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Obtain historical power load data corresponding to the target area from the power company's database; Step S12: Obtain the temperature data corresponding to the target area through the meteorological monitoring station; Step S13: Obtain geographic information data corresponding to the target area through the geographic information database, including the terrain, landforms and urban topography distribution of the target area; Step S14: Obtain the energy usage ratio data corresponding to the target area through the power energy usage records, including the usage amount and ratio of various energy sources; Step S15: Divide the corresponding geographic sub-regions based on the terrain, landforms, and urban topography distribution corresponding to the geographic information data. Then, based on the geographic sub-regions, divide the historical power load data, temperature data, and energy usage ratio data of the target area into sub-region data to obtain the power load data, temperature data, and energy usage data corresponding to the same sub-region. By performing time-series synchronization and data preprocessing on the power load data, temperature data, and energy usage data corresponding to the same sub-region according to the time series, the corresponding data are unified in the same time range. Then, clean, remove outliers and missing values, and standardize them to obtain the standard sequence data of power load, temperature, and energy usage corresponding to the same sub-region.

3. The method for analyzing the sensitivity of load impact to temperature based on regional characteristic analysis according to claim 2, characterized in that, The geographical sub-regions mentioned in step S15 include mountainous areas, plains, industrial areas, agricultural areas, and residential areas.

4. The method for analyzing the sensitivity of load impact to temperature based on regional characteristic analysis according to claim 1, characterized in that, Step S2 includes the following steps: Step S21: Obtain the corresponding regional industrial structure through the geographical sub-region, and quantify the regional electricity consumption ratio of the standard sequence data of the corresponding power load under the same sub-region based on the regional industrial structure, so as to obtain the electricity load ratio of different industrial structures and obtain the industrial structure load electricity consumption index of the same geographical sub-region. Step S22: Quantify the regional temperature fluctuation index of the corresponding temperature standard sequence data in the same sub-region to obtain the corresponding seasonal temperature fluctuation amplitude and fluctuation frequency, and obtain the temperature fluctuation stability index corresponding to the same geographical sub-region. Step S23: Quantify the regional energy proportion index by using the usage and proportion of various energy types in the corresponding energy use standard sequence data within the same sub-region, so as to obtain the corresponding clean energy usage proportion and non-clean energy usage proportion, and obtain the energy use index corresponding to the same geographical sub-region.

5. The method for analyzing the sensitivity of load impact to temperature based on regional characteristic analysis according to claim 1, characterized in that, Step S33 is followed by the following steps: Step S34: Use a nonlinear model to assess the impact of power load and temperature changes in areas with complex load changes, so as to obtain the load-temperature nonlinear impact index corresponding to areas with complex load categories; use a linear regression model to assess the impact of power load and temperature changes in areas with relatively simple load changes, so as to obtain the load-temperature linear impact index corresponding to areas with simple load categories. Step S35: Obtain the response speed and change range of load to temperature in different categories of areas, and combine the load-temperature nonlinear influence index or the load-temperature linear influence index to calculate the load sensitivity of the corresponding geographical sub-regions in the target area, so as to obtain the sensitivity coefficient of power load to temperature in different categories of areas.

6. The method for analyzing the sensitivity of load impact to temperature based on regional characteristic analysis according to claim 5, characterized in that, Step S35 includes the following steps: Step S351: Statistical analysis of response speed is performed on the corresponding power load and temperature changes in different categories of areas to obtain the load response speed to temperature in different categories of areas; Step S352: Perform a temperature change trend analysis on the relationship between the power load and temperature change in different categories of regions to obtain the temperature change trend lines under power load conditions in different categories of regions; Step S353: Based on the temperature change trend lines corresponding to the power load conditions in different categories of regions, calculate the corresponding temperature change amplitude to obtain the load-temperature change amplitude in different categories of regions; Step S354: Based on the response speed and change range of load to temperature in different categories of regions, and combined with the load-temperature nonlinear influence index or the load-temperature linear influence index, the load sensitivity calculation formula is used to calculate the load sensitivity of the corresponding geographical sub-regions in the target region, and obtain the power load sensitivity coefficient to temperature in different categories of regions.

7. The method for analyzing the sensitivity of load impact to air temperature based on regional characteristic analysis according to claim 6, characterized in that, The specific formula for calculating the load temperature sensitivity mentioned in step S354 is as follows: Areas with complex load variations: ; Areas where load changes are relatively simple: ; In the formula, This is the sensitivity coefficient of regional power load to temperature. The response speed of the load in the region to temperature. The magnitude of temperature change in the regional load. These are the temperature variation parameters corresponding to the geographic sub-region. The load-temperature nonlinear effect index. This is the load-temperature linear influence index.

8. The method for analyzing the sensitivity of load impact to temperature based on regional characteristic analysis according to claim 1, characterized in that, Step S4 includes the following steps: Step S41: Based on the sensitivity coefficients of power load to temperature in different categories of regions, perform regional sensitivity statistical characteristic analysis on the corresponding geographical sub-regions within the target region to obtain the mean, variance, and peak statistical characteristics of temperature sensitivity coefficients in different categories of regions at different time periods. Step S42: Based on the statistical characteristics of the mean, variance and peak values ​​of the temperature sensitivity coefficients of different categories of regions in different time periods, perform temperature sensitivity difference analysis on the corresponding geographical sub-regions within the target area to obtain the load temperature sensitivity differences of different categories of regions. Step S43: Based on the differences in load temperature sensitivity of different types of regions, perform power resource allocation management for the corresponding geographical sub-regions within the target area, generate power resource allocation management strategies for different temperature-sensitive regions, and execute the corresponding power resource optimization allocation management work.

9. The method for analyzing the sensitivity of load impact to temperature based on regional characteristic analysis according to claim 8, characterized in that, The power resource allocation management strategy for different temperature-sensitive areas described in step S43 is as follows: If it is determined that the sensitivity coefficient difference is large and the load growth is fast within the corresponding geographical sub-region, it is planned as a load temperature-sensitive area. Constraints are set for power resource allocation, including grid transmission capacity limits and power generation equipment output limits. Simulated annealing algorithm is used to find the optimal power resource allocation result while satisfying the constraints. If it is determined that the sensitivity coefficient difference is small and the load growth is slow within the corresponding geographical sub-region, it is planned as a load non-temperature-sensitive area. Power loss adjustment and load control measures are implemented during high-temperature or low-temperature periods to achieve optimized utilization of power resources and energy conservation and emission reduction.

10. A system for analyzing the sensitivity of load impact to air temperature based on regional characteristic analysis, characterized in that, For performing the load impact temperature sensitivity analysis method based on regional characteristic analysis as described in claim 1, the load impact temperature sensitivity analysis system based on regional characteristic analysis includes: The regional data time series synchronization module is used to acquire historical power load data, temperature data, geographic information data and energy usage ratio data corresponding to the target area, and perform time series synchronization preprocessing on the historical power load data, temperature data and energy usage ratio data corresponding to the target area based on the geographic sub-regions corresponding to the geographic information data and according to the time series, so as to obtain the standard sequence data of power load, temperature and energy usage under the same sub-region. The regional characteristic index quantification module is used to quantify the regional characteristic indexes of the standard sequence data of power load, temperature and energy use in the same sub-region, thereby obtaining the regional characteristic indexes of power in the same sub-region, including the industrial structure load power consumption index, temperature fluctuation stability index and energy use index of the same geographical sub-region. The regional load sensitivity calculation module is used to perform regional classification impact assessment on the corresponding geographical sub-regions within the target area based on the corresponding power regional characteristic indicators under the same sub-region, so as to obtain the load-temperature nonlinear impact index or load-temperature linear impact index for different categories of regions; obtain the response speed and change magnitude of load to temperature in different categories of regions and combine the load-temperature nonlinear impact index or load-temperature linear impact index to perform load sensitivity calculation on the corresponding geographical sub-regions within the target area, thereby obtaining the power load sensitivity coefficient to temperature for different categories of regions; The power resource allocation management module is used to manage the allocation of power resources to corresponding geographical sub-regions within the target area based on the sensitivity coefficient of power load to temperature in different categories of regions. It generates power resource allocation management strategies for different temperature-sensitive regions to execute corresponding power resource optimization allocation management work.

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