A GIS spatial service collaborative analysis method for electricity statistics

Abstract

The present invention discloses a GIS spatial service collaborative analysis method for electricity statistics, which relates to the technical field of power supply collaborative analysis, and comprises the following steps: collecting electricity data and GIS data; matching the electricity data with a power distribution map based on the electricity data and the GIS data and drawing a load distribution map; identifying high energy consumption areas in the power distribution map; performing a comprehensive analysis in combination with weather data and population density, evaluating the impact of different factors on electricity consumption in high energy consumption areas, and correcting the impact parameters of weather factors according to the change in population density through the analysis results; the present invention is used to solve the problem that the existing power supply collaborative analysis technology still has insufficient comprehensive and in-depth analysis of influencing factors, resulting in large errors in power supply prediction results.

Description

Technical Field

[0001] The present invention relates to the technical field of power supply collaborative analysis, and in particular to a GIS spatial service collaborative analysis method for electricity statistics. Background Art

[0002] Power supply collaborative analysis technology refers to the use of information technology and data analysis methods to comprehensively analyze and optimize data such as power supply equipment, power grid operation status, load demand, etc. in the power system to achieve efficient, stable and safe operation of the power system. By collecting, processing and analyzing data from all aspects of the power system, power supply collaborative analysis technology can help power system operation managers better understand the operation of the power grid, or make advance predictions on power supply to ensure the stable operation of the power system.

[0003] Existing power supply collaborative analysis technology usually predicts power consumption in future time periods through collaborative analysis of historical power supply data and historical weather data, thereby predicting power supply. When analyzing through historical power supply data and historical weather data, it is usually necessary to use years as units, that is, to obtain historical data in the past few years. When using years as units, the main factors affecting power consumption are not only weather, but also population density. The population density of a region changes every year, and changes in population density will also lead to changes in power consumption. For example, in a patent application with publication number CN112381266A, a system and method for predicting future power supply based on historical power supply and weather data are disclosed. This solution is to predict power consumption in future time periods through collaborative analysis of historical power supply data and historical weather data, thereby predicting power supply. This method only considers weather reasons when performing collaborative analysis, which will lead to large differences in prediction results. The existing power supply collaborative analysis technology also has the problem that the analysis of influencing factors is not comprehensive and in-depth enough, resulting in large errors in the prediction results of power supply. Summary of the invention

[0004] The present invention aims to solve one of the technical problems in the prior art to at least a certain extent, by collecting electricity data and GIS data, matching the electricity meter with the building in the electricity distribution map based on the GIS data, and then drawing the load distribution map based on the matching result and the electricity data, and then identifying the high energy consumption area in the electricity distribution map, and then obtaining the historical electricity consumption data and historical weather data of the high energy consumption area, and at the same time obtaining the population density of the high energy consumption area, and analyzing and evaluating the impact of population density and weather on the electricity consumption in the high energy consumption area based on the historical electricity consumption data, historical weather data and population density, and finally, based on the impact of population density and weather on the electricity consumption in the high energy consumption area, analyzing and predicting the estimated electricity consumption of the high energy consumption area, so as to solve the problem that the existing power supply collaborative analysis technology still has the problem that the analysis of the influencing factors is not comprehensive and in-depth enough, resulting in large errors in the prediction results of power supply.

[0005] To achieve the above objectives, in a first aspect, the present application provides a GIS spatial service collaborative analysis method for electricity statistics, comprising the following steps:

[0006] Collect electricity data and GIS data;

[0007] Based on the electricity data and GIS data, the electricity data is matched with the power distribution map and the load distribution map is drawn;

[0008] Identify high energy consumption areas in the power distribution map;

[0009] A comprehensive analysis is conducted based on weather data and population density to evaluate the impact of different factors on electricity consumption in high-energy-consuming areas. The analysis results are used to calibrate the parameters affecting weather factors based on changes in population density.

[0010] Furthermore, collecting electricity data and GIS data includes the following sub-steps:

[0011] Acquire electricity data, where the electricity data includes electricity consumption and peak load, where the electricity consumption is the total amount of electricity consumed by an electricity meter in the power supply area in the current month, and the peak load is the maximum value of the electricity load of an electricity meter in the power supply area in the current month;

[0012] The GIS data is obtained, where the GIS data is the geographic location information of the electric meter.

[0013] Furthermore, matching the power data with the power distribution map based on the power data and the GIS data and drawing the load distribution map includes the following sub-steps:

[0014] Matching electricity meters to buildings in the electricity distribution map based on GIS data;

[0015] Draw a load distribution diagram based on the matching results and power data.

[0016] Furthermore, matching the electricity meters with the buildings in the power distribution map based on the GIS data includes the following sub-steps:

[0017] The meter is numbered by the symbol E n Indicates, where n is a positive integer and n is the serial number of E;

[0018] Get the power distribution map, number the buildings in the power distribution map, and use the symbol B m Indicates, where m is a positive integer and m is the serial number of B;

[0019] Find Electricity Meter E n Which building does the GIS data belong to? m , summarize the statistics, and construct the power distribution table for building B m Electricity meter E n Make a record.

[0020] Furthermore, drawing a load distribution diagram based on the matching result and the power data includes the following sub-steps:

[0021] For building B m Electricity meter E n The electricity data of building B is counted and calculated. m Electricity meter E n The total power consumption is marked as the total power consumption, represented by the symbol U m Indicates that U m For B m Total electricity consumption;

[0022] Calculate Building B m Electricity meter E n The sum of the peak loads of each day is marked as the peak sum, and the maximum value in the peak sum is found, marked as the total load peak, and represented by the symbol F m Indicates that F m For B m The total peak load;

[0023] The total power consumption is divided into three levels: low, medium and high. The maximum and minimum values ​​of the total power consumption are obtained, marked as Umax and Umin respectively. (Umax-Umin) / 3 is calculated, and the calculation result is marked as the power consumption division interval, represented by the symbol UG;

[0024] Umin+UG is marked as the first power consumption threshold, Umax-UG is marked as the second power consumption threshold, and U m Compare with the first power usage threshold and the second power usage threshold respectively. If U m If U is less than or equal to the first power consumption threshold, a low power consumption signal is output;m If U is greater than the first power usage threshold and less than or equal to the second power usage threshold, a medium power usage signal is output; m If it is greater than the second power usage threshold, a high power usage signal is output;

[0025] If the output is a low power signal, then mark building B m If the output signal is medium power consumption, then mark building B. m If the output signal is high, then mark building B. m The power distribution map is filled with green, orange and red to represent the low power consumption area, medium power consumption area and high power consumption area respectively, to obtain the power distribution map;

[0026] The total load peak is divided into three levels: low, medium and high. The maximum and minimum values ​​of the total load peak are obtained, marked as Fmax and Fmin respectively. (Fmax-Fmin) / 3 is calculated, and the calculation result is marked as the power consumption division interval, represented by the symbol FG;

[0027] Mark Fmin+FG as the first peak threshold, mark Fmax-FG as the second peak threshold, and mark F m Compare with the first peak threshold and the second peak threshold respectively. If F m is less than or equal to the first peak threshold, a low peak signal is output; if F m If F is greater than the first peak threshold and less than or equal to the second peak threshold, a medium peak signal is output; if F m If the value is greater than the second peak value threshold, a high peak value signal is output;

[0028] If the output is a low peak signal, mark building B m It is a low peak area; if the output is a medium peak signal, the building B is marked m If the output is a high peak signal, the building B is marked. m The peak value area is the high peak value area; the power distribution map is filled with green, orange and red to represent the low peak value area, the medium peak value area and the high peak value area, respectively, to obtain the peak distribution map;

[0029] The power consumption distribution diagram and the peak distribution diagram together constitute a load distribution diagram.

[0030] Furthermore, identifying high energy consumption areas in the power distribution map includes the following sub-steps:

[0031] Remove the green and orange areas in the power consumption distribution map and the peak distribution map, and only keep the red area. Overlap the power consumption distribution map and the peak distribution map to obtain the area where the red area overlaps, and mark it as a high energy consumption area.

[0032] Keep the color fill of the high energy consumption area and remove the color fill of the rest.

[0033] Furthermore, a comprehensive analysis is conducted in combination with weather data and population density to evaluate the impact of different factors on electricity consumption in high energy consumption areas. The analysis results are used to calibrate the parameters affecting weather factors based on changes in population density, including the following sub-steps:

[0034] Obtain historical electricity consumption data and historical weather data for high energy consumption areas, and also obtain population density in high energy consumption areas;

[0035] Evaluate the impact of population density and weather on electricity consumption in high energy consumption areas based on historical electricity consumption data, historical weather data, and population density analysis;

[0036] Based on the impact of population density and weather on electricity consumption in high-energy-consuming areas, the estimated electricity consumption in high-energy-consuming areas is analyzed and predicted.

[0037] Furthermore, obtaining historical electricity consumption data and historical weather data of high energy consumption areas and obtaining population density of high energy consumption areas includes the following sub-steps:

[0038] Read the historical power supply database of the power system to search for historical power consumption data of the high energy consumption area, wherein the historical power consumption data includes the recording date and the historical power consumption, and the historical power consumption is the total power consumption of all buildings in the high energy consumption area in one day;

[0039] Establishing a communication connection with the Internet to obtain historical weather data, wherein the historical weather data includes historical dates and historical temperatures;

[0040] Obtaining the population density of the high energy consumption area, where the population density is updated once a year;

[0041] Based on the recording date of historical electricity consumption data and the historical date of historical weather data, the historical electricity consumption and historical temperature are integrated, and the historical electricity consumption and historical temperature of the same year are merged into a regional historical data table. There are multiple regional historical data tables depending on the year, and each regional historical data table corresponds to different population densities.

[0042] Furthermore, based on historical electricity consumption data, historical weather data and population density analysis, evaluating the impact of population density and weather on electricity consumption in high energy consumption areas includes the following sub-steps:

[0043] Sort and number the regional historical data table in order from the beginning to the end of the year, using the symbol R i Represents, where i is a positive integer and i is the sequence number of R;

[0044] A rectangular coordinate system is established with historical temperature as the X-axis and historical power consumption as the Y-axis, named Weather Impact Trend Chart. i There is a corresponding weather impact trend graph, marked as P i , R i The historical power consumption in P is recorded according to the historical temperature. i middle;

[0045] P i Perform linear regression, name the regression function the i-th weather impact function, and obtain max(i) i-th weather impact functions, where max() is the maximum value operator;

[0046] Get the slope of the i-th weather impact function, marked as C i ; R i The population density is marked as H i ;

[0047] Starting with i=1, calculate C i+1 / C i -1, mark the result as G i ;

[0048] Calculate G i / (H i+1 -H i ), and mark the result as K i , add i+1 and recalculate until i=max(i)-1, and then stop calculating, and get max(i)-1 K i , calculate K i The average value is marked as the floating parameter affected by population density.

[0049] Furthermore, based on the impact of population density and weather on the electricity consumption in high energy consumption areas, analyzing and predicting the estimated electricity consumption in high energy consumption areas includes the following sub-steps:

[0050] When predicting the estimated electricity consumption of a certain area through historical weather data, the estimated electricity consumption is corrected by obtaining the population density of the previous year and the population density of the current year;

[0051] The specific correction process is to calculate the population density of the current year minus the population density of the previous year to obtain the population density difference, then multiply the population density difference by the population density impact floating parameter and add one to obtain the population density impact parameter, and then multiply the population density impact parameter by the weather impact parameter obtained by actual analysis to correct the estimated electricity consumption.

[0052] Beneficial effects of the present invention: The present invention collects electricity data and GIS data, matches the electricity meter with the building in the electricity distribution map based on the GIS data, draws the load distribution map based on the matching result and the electricity data, and then identifies the high energy consumption area in the electricity distribution map. The advantage is that the high energy consumption area is more affected by the population density, and the difference in electricity consumption under the same weather with different population densities can be more intuitively seen, which improves the accuracy and effectiveness of power supply collaborative analysis;

[0053] The present invention obtains historical electricity consumption data and historical weather data of high-energy consumption areas, and simultaneously obtains population density in high-energy consumption areas, analyzes and evaluates the impact of population density and weather on electricity consumption in high-energy consumption areas based on historical electricity consumption data, historical weather data and population density, and finally analyzes and predicts the estimated electricity consumption in high-energy consumption areas based on the impact of population density and weather on electricity consumption in high-energy consumption areas. The advantage of the present invention is that when historical analysis is performed on an annual basis, in addition to weather factors, population density is also an important factor affecting residents' electricity consumption, and there is usually a mobile fluctuation every year. At this time, predictive analysis based on conventional weather factors will lead to a large deviation in the prediction results. On this basis, by correcting it through population density, the deviation of the prediction results can be reduced, thereby improving the accuracy and rationality of power supply collaborative analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 is a flow chart of the steps of the method of the present invention;

[0055] Figure 2 It is a partial power distribution diagram of the present invention;

[0056] Figure 3 It is a partial electricity distribution diagram of the present invention;

[0057] Figure 4 It is a partial peak distribution diagram of the present invention;

[0058] Figure 5 is a schematic diagram of a high energy consumption area of ​​the present invention;

[0059] Figure 6 R of the present invention 1 Weather impact trend chart;

[0060] Figure 7 It is a schematic structural diagram of the electronic device of the present invention. DETAILED DESCRIPTION

[0061] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0062] Example 1, please refer to Figure 1 As shown, the present application provides a GIS spatial service collaborative analysis method for electricity statistics, comprising the following steps:

[0063] Step S1, collecting electricity data and GIS data; Step S1 includes the following sub-steps:

[0064] Step S101, obtaining power data, the power data including power consumption and peak load, the power consumption is the total power consumption of a certain power meter in the power supply area in the current month, and the peak load is the maximum value of the power load of a certain power meter in the power supply area in the current month;

[0065] Step S102, obtaining GIS data, where the GIS data is the geographical location information of the electric meter;

[0066] In the specific implementation, the current time is July 16, 2024, and the electricity consumption represents the total electricity consumption displayed by a certain meter in the power supply area in July 2024, and the peak load represents the maximum value of the electricity load of the meter in July 2024. For example, a certain meter A records a total electricity consumption of 216kWh from July 1 to 16, 2024, and the peak load is 3600kW; the GIS data is the latitude and longitude information of the meter in the power company's records. Due to too much data, this embodiment does not display it in detail. It is only necessary to know that the GIS data is the latitude and longitude information.

[0067] Step S2, matching the power data with the power distribution map based on the power data and the GIS data and drawing a load distribution map; Step S2 includes the following sub-steps:

[0068] Step S201, matching the electric meter with the building in the electric power distribution map based on the GIS data;

[0069] Step S201 includes the following sub-steps:

[0070] Step S201.1, number the meters, using the symbol E n Indicates, where n is a positive integer and n is the serial number of E;

[0071] See also Figure 2As shown, step S201.2, obtain the power distribution map, number the buildings in the power distribution map, and use the symbol B m Indicates, where m is a positive integer and m is the serial number of B;

[0072] Step S201.3, search for the electric meter E n Which building does the GIS data belong to? m , summarize the statistics, and construct the power distribution table for building B m Electricity meter E n Keep records;

[0073] In the specific implementation, some power distribution diagrams are as follows Figure 2 As shown, Figure 2 The hollow circle in the figure is the electric meter. Since they are in the same building, the longitude and latitude information of some electric meters in the electric power company's records are the same, and they will overlap in the power distribution diagram. Due to the large amount of data, it is not convenient to show it in detail in this embodiment. Only part of the data in the power distribution table is used for explanation. Part of the data in the power distribution table is shown in Table 1 below:

[0074] Table 1 Partial data of power distribution table

[0075]

[0076] Table 1 shows the basic structure of the power distribution meter, namely, the meter E n Belongs to Building B m ;

[0077] Step S202, drawing a load distribution diagram based on the matching result and the power data;

[0078] Step S202 includes the following sub-steps:

[0079] Step S202.1, for building B m Electricity meter E n The electricity data of building B is counted and calculated. m Electricity meter E n The total power consumption is marked as the total power consumption, represented by the symbol U m Indicates that U m For B m Total electricity consumption;

[0080] Step S202.2, calculate building B m Electricity meter E n The sum of the peak loads of each day is marked as the peak sum, and the maximum value in the peak sum is found, marked as the total load peak, and represented by the symbol F m Indicates that F m For Bm The total peak load;

[0081] In the specific implementation, B 1 For example, B 1 The electric meters include E 1 To E 24 , so the statistics E 1 To E 24 The sum of the power consumption is U 1 =4988kWh; Calculate building B m Electricity meter E n The sum of the peak loads of each day is calculated because the peak loads of each meter may not be on the same day. For example, on July 1, 2024, only meter E 1 and E 13 If there is a peak load, the total peak value on July 1, 2024 is E 1 The peak load and E 13 The sum of the peak loads of each day from July 1 to 16, 2024 is calculated by analogy. The maximum value is the peak sum on July 12, 2024, which is 12468kW, that is, the total peak load F 1 12468kW;

[0082] Step S202.3, divide the total power consumption into three levels: low, medium and high, obtain the maximum value and the minimum value of the total power consumption, mark them as Umax and Umin respectively, calculate (Umax-Umin) / 3, mark the calculation result as the power consumption division interval, represented by the symbol UG;

[0083] Step S202.4: mark Umin+UG as the first power consumption threshold, mark Umax-UG as the second power consumption threshold, and mark U m Compare with the first power usage threshold and the second power usage threshold respectively. If U m If U is less than or equal to the first power consumption threshold, a low power consumption signal is output; m If U is greater than the first power usage threshold and less than or equal to the second power usage threshold, a medium power usage signal is output; m If it is greater than the second power usage threshold, a high power usage signal is output;

[0084] See also Figure 3 As shown, step S202.5, if a low power consumption signal is output, then mark building B m If the output signal is medium power consumption, then mark building B. m If the output signal is high, then mark building B. mThe power distribution map is filled with green, orange and red to represent the low power consumption area, medium power consumption area and high power consumption area respectively, to obtain the power distribution map;

[0085] In the specific implementation, only building B is listed in this embodiment. 1 The total power consumption and total load peak of building B 2 To B 856 The total power consumption and the total load peak, that is, there is U 1 toU 856 and F 1 To F 856 ; By searching, we can find that Umax and Umin are 13244kWh and 4078kWh respectively. By calculating (Umax-Umin) / 3, we can get the electricity consumption interval UG as 3055kWh. The calculation result is an integer. 4078kWh+3055kWh=7133kWh is marked as the first electricity consumption threshold, and 13244kWh-3055kWh=10189kWh is marked as the second electricity consumption threshold; U m Compare with 7133kWh and 10189kWh respectively, divide the low power consumption area, medium power consumption area and high power consumption area, and draw some power consumption distribution maps as shown below Figure 3 As shown;

[0086] Step S202.6, divide the total load peak into three levels: low, medium and high, obtain the maximum and minimum values ​​of the total load peak, mark them as Fmax and Fmin respectively, calculate (Fmax-Fmin) / 3, mark the calculation result as the power consumption division interval, represented by the symbol FG;

[0087] Step S202.7, mark Fmin+FG as the first peak threshold, mark Fmax-FG as the second peak threshold, and mark F m Compare with the first peak threshold and the second peak threshold respectively. If F m is less than or equal to the first peak threshold, a low peak signal is output; if F m If F is greater than the first peak threshold and less than or equal to the second peak threshold, a medium peak signal is output; if F m If the value is greater than the second peak value threshold, a high peak value signal is output;

[0088] See also Figure 4 As shown, step S202.8, if a low peak signal is output, then mark building B m It is a low peak area; if the output is a medium peak signal, the building B is marked m If the output is a high peak signal, the building B is marked. mThe peak value area is the high peak value area; the power distribution map is filled with green, orange and red to represent the low peak value area, the medium peak value area and the high peak value area, respectively, to obtain the peak distribution map;

[0089] Step S202.9, the power consumption distribution diagram and the peak distribution diagram together form a load distribution diagram;

[0090] In the specific implementation, the obtained Fmax and Fmin are 26358kW and 11856kW respectively, and the power consumption partition interval FG is 4834kW by calculating (Fmax-Fmin) / 3. The calculation result is retained as an integer, and 11856kW+4834kW=16690kW is marked as the first peak threshold, and 26358kW-4834kW=21524kW is marked as the second peak threshold. m Compare with 16690kW and 21524kW respectively, divide the low peak area, medium peak area and high peak area, and draw some peak distribution diagrams as shown below Figure 4 As shown, due to the use of grayscale images, it is not convenient to distinguish regions by color. Figure 3 and Figure 4 Only the red areas, i.e. the high power consumption areas and the high peak areas, are retained, and the rest of the areas are not marked.

[0091] Step S3, identifying high energy consumption areas in the power distribution map; Step S3 includes the following sub-steps:

[0092] Step S301, removing the green area and the orange area in the power consumption distribution map and the peak distribution map, retaining only the red area, overlapping the power consumption distribution map and the peak distribution map, obtaining the area where the red area overlaps, and marking it as a high energy consumption area;

[0093] See also Figure 5 As shown, in step S302, the color filling of the high energy consumption area is retained, and the color filling of the rest of the area is completely removed;

[0094] In specific implementation, Figure 3 and Figure 4 Only the red area is retained. Figure 3 and Figure 4 Overlapping, the area where the shadows overlap is the high energy consumption area, such as Figure 5 As shown, the high energy consumption area includes building B 105 , B 106 , B 107 , B 108 , B 156 , B 157 , B 158 and B 239 .

[0095] Step S4, combining weather data and population density for comprehensive analysis, evaluating the impact of different factors on electricity consumption in high energy consumption areas, and correcting the impact parameters of weather factors based on changes in population density through the analysis results; Step S4 includes the following sub-steps:

[0096] Step S401, obtaining historical electricity consumption data and historical weather data of high energy consumption areas, and simultaneously obtaining population density of high energy consumption areas;

[0097] Step S401 includes the following sub-steps:

[0098] Step S401.1, read the historical power supply database of the power system, and search for historical power consumption data of the high energy consumption area, where the historical power consumption data includes the recording date and the historical power consumption, and the historical power consumption is the total power consumption of all buildings in the high energy consumption area in one day;

[0099] In specific implementation, the historical power supply database records the power consumption information of all buildings. In this embodiment, only the historical power consumption of buildings in high energy consumption areas is selected. Part of the data of the historical power supply database is shown in Table 2 below:

[0100] Table 2 Partial data of historical power supply database

[0101] Record date Historical power consumption (kWh) January 1, 2024 97632 January 2, 2024 99301 January 3, 2024 98455 January 4, 2024 101125

[0102] Table 2 shows only the total historical electricity consumption data of buildings in high energy consumption areas, namely B 105 , B 106 , B 107 , B 108 , B 156 , B 157 , B 158 and B 239 The total amount of electricity used on the recording date;

[0103] Step S401.2, establishing a communication connection with the Internet to obtain historical weather data, the historical weather data including historical dates and historical temperatures;

[0104] Step S401.3, obtaining the population density of high energy consumption areas, which is updated once a year;

[0105] Step S401.4, based on the recording date of the historical electricity consumption data and the historical date of the historical weather data, the historical electricity consumption and the historical temperature are integrated, and the historical electricity consumption and the historical temperature of the same year are merged into a regional historical data table. There are multiple regional historical data tables according to different years, and each regional historical data table corresponds to a different population density;

[0106] In the specific implementation, four regional historical data tables are obtained through integration, namely the 2021 regional historical data table, the 2022 regional historical data table, the 2023 regional historical data table and the 2024 regional historical data table, and their corresponding population densities are 539.31 people / square kilometer, 547.47 people / square kilometer, 555.42 people / square kilometer and 564.39 people / square kilometer, respectively. Taking part of the data of the 2024 regional historical data table as an example to show the components of the regional historical data table, some data of the 2024 regional historical data table are shown in Table 3 below:

[0107] Table 3 Partial data of the 2024 regional historical data table

[0108]

[0109] Historical temperatures are the average temperatures for one day on the date of recording;

[0110] Step S402, analyzing and evaluating the impact of population density and weather on electricity consumption in high energy consumption areas based on historical electricity consumption data, historical weather data, and population density;

[0111] Step S402 includes the following sub-steps:

[0112] Step S402.1, sort and number the regional historical data table in order from the beginning to the end of the year, using the symbol R i Represents, where i is a positive integer and i is the sequence number of R;

[0113] See also Figure 6 As shown in step S402.2, a plane rectangular coordinate system is established with the historical temperature as the X-axis and the historical power consumption as the Y-axis, and the system is named as the weather impact trend chart. i There is a corresponding weather impact trend graph, marked as P i , R i The historical power consumption in P is recorded according to the historical temperature. i middle;

[0114] Step S402.3, P i Perform linear regression, name the regression function the i-th weather impact function, and obtain max(i) i-th weather impact functions, where max() is the maximum value operator;

[0115] In the specific implementation, R 1 To R 4 represents the 2021 regional historical data table, the 2022 regional historical data table, the 2023 regional historical data table, and the 2024 regional historical data table, 1≤i≤4. Taking the weather impact trend chart corresponding to the 2021 regional historical data table as an example, construct P 1 , R1 Data entry in P 1 Get Figure 6 ; Due to the influence of temperature on electricity consumption, the trend is that the closer the temperature is to 25℃, the smaller the electricity consumption is, and the further the temperature is from 25℃, the larger the electricity consumption is. The overall trend is from high to low and then from low to high. Therefore, when performing linear regression, it is necessary to set P i The temperature is divided from 25℃, and the part below 25℃ is subjected to linear regression, and the part greater than or equal to 25℃ is subjected to linear regression. That is, there are two regression functions for each weather influence trend chart. Since the purpose of this embodiment is to correct the temperature influence parameter by the change of population density, it is only necessary to use the regression function below 25℃ or the regression function greater than or equal to 25℃ at the same time. In this embodiment, the regression function below 25℃ is used for analysis, that is, P is eliminated. 1 To P 4 For data with historical temperatures greater than or equal to 25°C, 1 Linear regression is performed to obtain the first weather impact function, which is Y=-3024.8×X+130728, where Y is the historical power consumption and X is the historical temperature. Since there are 4 regional historical data tables, 4 ith weather impact functions are obtained by construction and regression.

[0116] Step S402.4, obtain the slope of the ith weather impact function, marked as C i ; R i The population density is marked as H i ;

[0117] In the specific implementation, the first weather influence function is obtained as Y=-3024.8×X+130728, and the slope is -3024.8, that is, C 1 is -3024.8, and R 1 The corresponding population density is 539.31 people / km2, or H 1 is 539.31, H i No units are required for calculation. Through the analysis of step S402.3, four ith weather impact functions are obtained, and their slopes are respectively -3024.8, -3135.7, -3246.9 and -3363.4, that is, C 1 To C 4 They are -3024.8, -3135.7, -3246.9 and -3363.4 respectively. 1 To H 4 They are 539.31, 547.47, 555.42 and 564.39 respectively;

[0118] Step S402.5, starting with i=1, calculate C i+1 / Ci -1, mark the result as G i ;

[0119] Step S402.6, calculate G i / (H i+1 -H i ), and mark the result as K i , add i+1 and recalculate until i=max(i)-1, and then stop calculating, and get max(i)-1 K i , calculate K i The average value is marked as the floating parameter affected by population density;

[0120] In the specific implementation, when i=1, G is obtained by calculation 1 =-3135.7 / -3024.8-1=0.0367. Keep four decimal places in the calculation result. Then calculate K 1 =0.0367 / (547.47-539.31)=0.004498, the result is rounded to six decimal places, and so on, we can get K 2 and K 3 They are 0.004465 and 0.004002 respectively. K is obtained by calculation i The average value is (0.004498+0.004465+0.004002) / 3=0.004322. The calculation result is rounded to six decimal places, that is, the floating parameter of population density is 0.004322.

[0121] Step S403, analyzing and predicting the estimated power consumption of the high energy consumption area based on the impact of population density and weather on the power consumption of the high energy consumption area;

[0122] Step S403 includes the following sub-steps:

[0123] Step S403.1, when predicting the estimated electricity consumption of a certain area through historical weather data, the estimated electricity consumption is corrected by obtaining the population density of the previous year and the population density of the current year;

[0124] Step S403.2, the specific correction process is to calculate the population density of the current year minus the population density of the previous year to obtain the population density difference, then multiply the population density difference by the population density impact floating parameter and add one to obtain the population density impact parameter, then multiply the population density impact parameter by the weather impact parameter obtained by actual analysis to correct the estimated power consumption;

[0125] In the specific implementation, it is assumed that the power supply prediction function obtained by historical data analysis when the temperature is less than 25°C in the high energy consumption area in this embodiment is Y=-3088.96×X+142385, where X is the temperature, Y is the predicted power supply, and -3088.96 is the temperature influencing parameter. The temperature influencing parameter at this time is inaccurate because the population density of each region changes every year. For example, the population density of the region last year was 534.38 people / square kilometer, while the population density this year is It is 556.42 people / square kilometer. The population density difference is calculated to be 556.42-534.38=22.04. The population density difference is multiplied by the population density impact floating parameter, and the population density impact parameter is 22.04×0.004322+1=1.09525688. Subtract -3088.96 from 1.09525688 to get the corrected temperature impact parameter of -3,383.20. The calculation result retains the same number of digits as the original temperature impact parameter.

[0126] Example 2, please refer to Figure 7 As shown, Figure 7 The structural schematic diagram of an electronic device is illustrated, and the electronic device may include: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus. The memory stores computer-readable instructions, and the processor can call the instructions in the memory. When the computer-readable instructions are executed by the processor, the steps in a GIS spatial service collaborative analysis method for electricity statistics are executed to achieve the following functions: collect electricity data and GIS data; match the electricity data with the power distribution map based on the electricity data and GIS data and draw a load distribution map; identify high-energy consumption areas in the power distribution map; conduct a comprehensive analysis in combination with weather data and population density, evaluate the impact of different factors on the electricity consumption in high-energy consumption areas, and correct the impact parameters of weather factors according to the changes in population density through the analysis results.

[0127] In addition, the logic instructions in the above-mentioned memory can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0128] Embodiment 3. The present application also provides a computer program product, which includes a computer program stored on a computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute a GIS spatial service collaborative analysis method for electricity statistics provided by the above methods, the method including: collecting electricity data and GIS data; matching the electricity data with a power distribution map based on the electricity data and GIS data and drawing a load distribution map; identifying high-energy consumption areas in the power distribution map; conducting a comprehensive analysis in combination with weather data and population density, evaluating the impact of different factors on electricity consumption in high-energy consumption areas, and correcting the impact parameters of weather factors with changes in population density through the analysis results.

[0129] Embodiment 4, the present application also provides a computer-readable storage medium, the present application provides a storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps in the GIS spatial service collaborative analysis method for electricity statistics are executed to achieve the following functions: collect electricity data and GIS data; match the electricity data with the power distribution map based on the electricity data and GIS data and draw a load distribution map; identify high-energy consumption areas in the power distribution map; conduct a comprehensive analysis in combination with weather data and population density, evaluate the impact of different factors on electricity consumption in high-energy consumption areas, and correct the impact parameters of weather factors based on changes in population density through the analysis results.

[0130] Through the description of the above implementation methods, the embodiments of the present invention can be provided as methods, systems or computer program products. Based on such an understanding, the above technical solutions can be essentially or partly contributed to the prior art in the form of software products, which can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and include several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0131] In the embodiments provided in the present application, it should be understood that the disclosed system or method can be implemented in other ways. The embodiments described above are merely schematic. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of systems, modules and units can be electrical, mechanical or other forms.

[0132] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A GIS spatial service collaborative analysis method for electricity statistics, characterized in that: The steps include: Collect electricity data and GIS data; Based on the electricity data and GIS data, the electricity data is matched with the power distribution map and the load distribution map is drawn; Identify high energy consumption areas in the power distribution map; Combine weather data and population density for comprehensive analysis to assess the impact of different factors on electricity consumption in high energy consumption areas. Based on the analysis results, the parameters affecting weather factors are corrected based on changes in population density. Collecting electricity data and GIS data includes the following sub-steps: Acquire electricity data, where the electricity data includes electricity consumption and peak load, where the electricity consumption is the total amount of electricity consumed by an electricity meter in the power supply area in the current month, and the peak load is the maximum value of the electricity load of an electricity meter in the power supply area in the current month; Acquire GIS data, where the GIS data is geographic location information of the electric meter; Matching the power data with the power distribution map based on the power data and GIS data and drawing the load distribution map includes the following sub-steps: Matching electricity meters to buildings in the electricity distribution map based on GIS data; Draw a load distribution diagram based on the matching results and power data; Matching electricity meters with buildings in the power distribution map based on GIS data includes the following sub-steps: The meters are numbered by the symbol E n Indicates, where n is a positive integer and n is the serial number of E; Get the power distribution map, number the buildings in the power distribution map, and use the symbol B m Indicates, where m is a positive integer and m is the serial number of B; Find Electricity Meter E n Which building does the GIS data belong to? m , summarize the statistics, and construct the power distribution table for building B m Electricity meter E n Make a record.

2. The GIS spatial service collaborative analysis method for electricity statistics according to claim 1 is characterized in that: Drawing a load distribution diagram based on the matching results and power data includes the following sub-steps: For building B m Electricity meter E n The electricity data of building B is counted and calculated. m Electricity meter E n The total power consumption is marked as the total power consumption, represented by the symbol U m Indicates that U m For B m Total electricity consumption; Calculate Building B m Electricity meter E n The sum of the peak loads of each day is marked as the peak sum, and the maximum value in the peak sum is found, marked as the total load peak, and represented by the symbol F m Indicates that F m For B m The total peak load; The total power consumption is divided into three levels: low, medium and high. The maximum and minimum values ​​of the total power consumption are obtained, marked as Umax and Umin respectively. (Umax-Umin) / 3 is calculated, and the calculation result is marked as the power consumption division interval, represented by the symbol UG; Umin+UG is marked as the first power consumption threshold, Umax-UG is marked as the second power consumption threshold, and U m Compare with the first power usage threshold and the second power usage threshold respectively. If U m If U is less than or equal to the first power consumption threshold, a low power consumption signal is output; m If the power consumption value is greater than the first power consumption threshold value and less than or equal to the second power consumption threshold value, a medium power consumption signal is output; If U m If it is greater than the second power usage threshold, a high power usage signal is output; If the output is a low power signal, then mark building B m For low electricity consumption areas; If the output uses electrical signals, mark building B m For medium electricity consumption areas; If a high power consumption signal is output, then mark building B m The power distribution map is filled with green, orange and red to represent the low power consumption area, medium power consumption area and high power consumption area respectively, to obtain the power distribution map; The total load peak is divided into three levels: low, medium and high. The maximum and minimum values ​​of the total load peak are obtained, marked as Fmax and Fmin respectively. (Fmax-Fmin) / 3 is calculated, and the calculation result is marked as the power consumption division interval, represented by the symbol FG; Mark Fmin+FG as the first peak threshold, mark Fmax-FG as the second peak threshold, and mark F m Compare with the first peak threshold and the second peak threshold respectively. If F m is less than or equal to the first peak threshold, a low peak signal is output; if F m If F is greater than the first peak threshold and less than or equal to the second peak threshold, a medium peak signal is output; if F m If the value is greater than the second peak value threshold, a high peak value signal is output; If the output is a low peak signal, mark building B m It is a low peak area; if the output is a medium peak signal, the building B is marked m is the middle peak area; If the output is a high peak signal, mark building B m The peak value area is the high peak value area; the power distribution map is filled with green, orange and red to represent the low peak value area, the medium peak value area and the high peak value area, respectively, to obtain the peak distribution map; The power consumption distribution diagram and the peak distribution diagram together constitute a load distribution diagram.

3. The GIS spatial service collaborative analysis method for electricity statistics according to claim 2 is characterized in that: Identifying high energy consumption areas in the power distribution map includes the following sub-steps: Remove the green and orange areas in the power consumption distribution map and the peak distribution map, and only keep the red area. Overlap the power consumption distribution map and the peak distribution map to obtain the area where the red area overlaps, and mark it as a high energy consumption area. Keep the color fill of the high energy consumption area and remove the color fill of the rest.

4. The GIS spatial service collaborative analysis method for electricity statistics according to claim 3 is characterized in that: Combined with weather data and population density, comprehensive analysis is conducted to evaluate the impact of different factors on electricity consumption in high energy consumption areas. The impact parameters of weather factors are corrected based on the analysis results and the changes in population density, including the following sub-steps: Obtain historical electricity consumption data and historical weather data for high energy consumption areas, and also obtain population density in high energy consumption areas; Evaluate the impact of population density and weather on electricity consumption in high energy consumption areas based on historical electricity consumption data, historical weather data, and population density analysis; Based on the impact of population density and weather on electricity consumption in high-energy-consuming areas, the estimated electricity consumption in high-energy-consuming areas is analyzed and predicted.

5. The GIS spatial service collaborative analysis method for electricity statistics according to claim 4 is characterized in that: Obtaining historical electricity consumption data and historical weather data of high energy consumption areas, and obtaining population density of high energy consumption areas includes the following sub-steps: Read the historical power supply database of the power system to search for historical power consumption data of the high energy consumption area, wherein the historical power consumption data includes the recording date and the historical power consumption, and the historical power consumption is the total power consumption of all buildings in the high energy consumption area in one day; Establishing a communication connection with the Internet to obtain historical weather data, wherein the historical weather data includes historical dates and historical temperatures; Obtaining the population density of the high energy consumption area, where the population density is updated once a year; Based on the recording date of historical electricity consumption data and the historical date of historical weather data, the historical electricity consumption and historical temperature are integrated, and the historical electricity consumption and historical temperature of the same year are merged into a regional historical data table. There are multiple regional historical data tables depending on the year, and each regional historical data table corresponds to different population densities.

6. The GIS spatial service collaborative analysis method for electricity statistics according to claim 5 is characterized in that: Based on historical electricity consumption data, historical weather data and population density analysis, the evaluation of the impact of population density and weather on electricity consumption in high energy consumption areas includes the following sub-steps: Sort and number the regional historical data table in order from the beginning to the end of the year, using the symbol R i Represents, where i is a positive integer and i is the sequence number of R; A rectangular coordinate system is established with historical temperature as the X-axis and historical power consumption as the Y-axis, named Weather Impact Trend Chart. i There is a corresponding weather impact trend graph, marked as P i , R i The historical power consumption in P is recorded according to the historical temperature. i middle; P i Perform linear regression, name the regression function the i-th weather impact function, and obtain max(i) i-th weather impact functions, where max() is the maximum value operator; Get the slope of the i-th weather impact function, marked as C i ; R i The population density is marked as H i ; Starting with i=1, calculate C i+1 / C i -1, mark the result as G i ; Calculate G i / (H i+1 -H i ), and mark the result as K i , add i+1 and recalculate until i=max(i)-1, and then stop calculating, and get max(i)-1 K i , calculate K i The average value is marked as the floating parameter affected by population density.

7. The GIS spatial service collaborative analysis method for electricity statistics according to claim 6 is characterized in that: Based on the impact of population density and weather on electricity consumption in high energy consumption areas, the analysis and prediction of estimated electricity consumption in high energy consumption areas includes the following sub-steps: When predicting the estimated electricity consumption of a certain area through historical weather data, the estimated electricity consumption is corrected by obtaining the population density of the previous year and the population density of the current year; The specific correction process is to calculate the population density of the current year minus the population density of the previous year to obtain the population density difference, then multiply the population density difference by the population density impact floating parameter and add one to obtain the population density impact parameter, and then multiply the population density impact parameter by the weather impact parameter obtained by actual analysis to correct the estimated electricity consumption.

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