Method and system for analyzing influence rule of business expansion change on electricity demand

Abstract

The patent application of the invention provides a method and system for analyzing the influence rule of business expansion change on power demand, and the method comprises the steps: classifying the user groups of business expansion change according to the business type of business expansion change; based on the business expansion change data, obtained after classification, of the various user groups, an actual commissioning proportion calculation formula is adopted to calculate the actual commissioning proportion of the various user groups; according to the actual commissioning proportion of each user group, a characteristic decomposition technology is combined with a regression analysis method to calculate the monthly trend commissioning proportion and the target production time of each user group; according to the month-by-month trend commissioning proportion and the target production time of each type of user group, summarizing an influence rule of each type of business expansion change on the electricity demand; according to the method, the influence of the business expansion change business on the increase of the electricity demand is reflected, the sequential relationship between various business expansion changes and the electricity demand is analyzed, the change trend of the electricity demand is extracted and analyzed, and a scientific basis is provided for accurately summarizing the business expansion influence rule of different user groups.

Description

Technical Field

[0001] The patent application of this invention belongs to the field of electricity, and specifically relates to a method and system for analyzing the impact of business expansion changes on electricity demand. Background Art

[0002] In recent years, with the continuous development of the power market and the advancement of energy transformation, the power industry is facing urgent problems to be solved. Among them, the digital analysis of power load and the comprehensive understanding of the changing trends of electricity consumption caused by business expansion and installation have become key challenges. Traditional load analysis methods mainly stay at the statistical analysis level of the macro level, which cannot deeply explore the load characteristics of different user groups in the power system, limiting the accurate grasp of the changing trends of electricity demand. At the same time, the growth of business expansion and installation business has brought more complexity to the power system, and effective methods are needed to extract and quantify the growth trends of electricity consumption in different industries or customers.

[0003] In this context, the patent with publication number CN117056780A proposes a digital analysis method for power load, which realizes a detailed analysis of power load by extracting four characteristic data: peak power consumption ratio, load rate, valley power coefficient and daily power consumption. This helps to understand the user's power consumption characteristics more comprehensively and solve the shortcomings of traditional load analysis. On the other hand, the patent with publication number CN115186895A proposes a method for predicting industry power consumption based on industry expansion and installation, by calculating the correlation between the two time series of the installed capacity TC (trend cycle) item and the TC item of power consumption, and performing regression analysis to predict power consumption.

[0004] The above two patent application documents conducted statistical analysis on load-related data, but failed to fully consider the impact of business expansion changes on changes in electricity load. Secondly, current research has not fully considered multiple factors such as different business expansion types, voltage levels, industry types, and user regions, resulting in deficiencies in multi-dimensional factor considerations. In addition, there are still deficiencies in the research on extracting and analyzing the changing trends of electricity demand, and the research on the laws of electricity load after business expansion changes needs to be further explored. The analysis of time series relationships has not yet been fully carried out, which limits the comprehensive understanding of the laws of expansion impact. Summary of the invention

[0005] In order to overcome the above-mentioned deficiencies of the prior art, the patent application of the present invention proposes a method for analyzing the influence of business expansion changes on electricity demand, including:

[0006] Classify the user groups of business expansion changes according to the business types of business expansion changes;

[0007] Based on the business expansion change data of various user groups obtained after classification, the actual operation ratio of each user group is calculated using the actual operation ratio calculation formula;

[0008] According to the actual operation ratio of each user group, the characteristic decomposition technology combined with the regression analysis method is used to calculate the monthly trend operation ratio and production time of each user group;

[0009] Based on the monthly trend of the operation ratio and the time to reach full capacity of each type of user groups, the impact of various business expansion changes on electricity demand is summarized.

[0010] Preferably, the service type includes at least one or more of the following: a new installation service type, a capacity increase service type, a capacity reduction service type, an account cancellation service type, a suspension service type, and a suspension and restoration service type.

[0011] Preferably, the actual operation ratio includes the actual load operation ratio and the actual power operation ratio; the business expansion change data of each user group obtained after classification, using the actual operation ratio calculation formula to calculate the actual operation ratio of each user group, includes:

[0012] Determine the user groups of the newly installed business type who completed business archiving in the archiving month and the user groups of other business types except the newly installed business type who completed business archiving in the month before archiving;

[0013] According to the newly installed business data of the user group of the newly installed business type who completed the business archiving in the archiving month, the actual load operation ratio and the actual power operation ratio of the user group of the newly installed business type are calculated using the actual operation ratio calculation formula;

[0014] Based on other business data of user groups of other business types except for the newly installed business type that completed business archiving one month before archiving, the actual load operation ratio and actual power operation ratio of user groups of other business types except for the newly installed business type are calculated using the actual operation ratio calculation formula;

[0015] Among them, the newly installed business data includes at least one or more of the following: the total load generated by the total reported capacity of archiving month n in the mth month, the total reported capacity of archiving month n, the total electricity consumption generated by the total reported capacity of archiving month n in the mth month and the number of days in the mth month.

[0016] Preferably, the calculation formula for the actual power supply ratio of the user group of the newly installed service type is as follows:

[0017]

[0018] Among them, η n,m P is the actual load operation ratio of the total installed capacity of the user group of the new service type in the archive month n in the mth month, n,m C is the total load generated by the total reported capacity in archive month n in month m,n The total reported capacity for archive month n.

[0019] Preferably, the calculation formula for the actual power supply ratio of the user group of the newly installed service type is as follows:

[0020]

[0021] Among them, λ n,m Q is the actual power supply ratio of the total installed capacity of the user group of the new service type in the archive month n in the mth month. n,m is the total electricity consumption generated by the total installed capacity in archive month n in month m, C n is the total reported capacity of archive month n, and g is the number of days in month m.

[0022] Preferably, the method of calculating the monthly trend operation ratio and production reaching time of each user group by using the characteristic decomposition technology combined with the regression analysis method according to the actual operation ratio of each user group includes:

[0023] Determine whether the business type data of various business expansion changes reaches a preset business type data threshold, and define the business type data that reaches the business type data threshold as business type data with sufficient data, and define the business type data that does not reach the business type data threshold as business type data with insufficient data;

[0024] For business type data with sufficient data, based on the actual monthly operation ratio of the corresponding user group in the current archiving month or the month before the current archiving, feature decomposition and regression analysis are performed to calculate the monthly trend operation ratio and production time of the user group corresponding to the business type data with sufficient data;

[0025] For business type data with insufficient data, statistics are collected and calculated for the average actual operation ratio of the same type of user groups that have completed archiving for multiple archiving months or multiple months before archiving. Based on the average actual operation ratio, feature decomposition and regression analysis are performed to calculate the monthly trend operation ratio and production time of the user groups corresponding to the business type data with insufficient data;

[0026] The monthly trend operation ratio and production completion time of the user groups corresponding to the business type data with sufficient data and the monthly trend operation ratio and production completion time of the user groups corresponding to the business type data with insufficient data are summarized to generate the monthly trend operation ratio and production completion time of each type of user groups.

[0027] Preferably, for the business type data with sufficient data, based on the actual monthly operation ratio of the corresponding user group in the current archiving month or the month before the current archiving, feature decomposition and regression analysis are performed to calculate the monthly trend operation ratio and production reaching time of the user group corresponding to the business type data with sufficient data, including:

[0028] For business type data with sufficient data, draw an actual operation ratio curve based on the actual operation ratio of the corresponding user group in the current archiving month or the month before the current archiving month;

[0029] Using characteristic decomposition technology, extracting the curve trend component from the actual operation ratio curve, and fitting the curve trend component using regression analysis method, to determine the monthly trend operation ratio and production time of the user group corresponding to the business type data with sufficient data;

[0030] The characteristic decomposition technology adopts one of the following: STL decomposition algorithm, wavelet decomposition algorithm, empirical mode decomposition algorithm and X-12 seasonal adjustment method; the regression analysis method adopts one of the following: logistic regression model, polynomial regression model.

[0031] Preferably, for the business type data with insufficient data, statistics are counted and calculated for the average monthly actual operation ratio of the same type of user groups that have completed archiving in multiple archiving months or multiple months before archiving, and based on the average monthly actual operation ratio, feature decomposition and regression analysis are performed to calculate the monthly trend operation ratio and production time of the user group corresponding to the business type data with insufficient data, including:

[0032] For business type data with insufficient data, statistics and calculation are conducted on the average actual operation ratio of the same type of user groups that have completed archiving for multiple archiving months or multiple months before archiving;

[0033] According to the average value of the actual operation ratio on a monthly basis, a mean value curve is drawn, and the characteristic decomposition technology is used to extract the curve trend component of the mean value curve, and the regression analysis method is used to fit the curve trend component to determine the monthly trend operation ratio and production reaching time of the user group corresponding to the business type data with insufficient data.

[0034] Preferably, after classifying the user groups of the business expansion change according to the service type of the business expansion change, the method further includes:

[0035] According to the refined classification types, the classified user groups are divided into categories to determine the refined types of each user group;

[0036] Among them, the refined classification includes at least one or more of the following: regional type, voltage level type and industry category.

[0037] Based on the same inventive concept, the patent application of the present invention also provides a system for analyzing the influence of business expansion changes on electricity demand, including: a business type group classification module, an actual operation ratio calculation module, a monthly trend operation ratio production time calculation module and an influence rule summary module;

[0038] The business type group classification module is used to classify the user group of the business expansion change according to the business type of the business expansion change;

[0039] The actual operation ratio calculation module is used to calculate the actual operation ratio of each user group based on the business expansion change data of each user group obtained after classification, using the actual operation ratio calculation formula;

[0040] The monthly trend operation ratio and production time calculation module is used to calculate the monthly trend operation ratio and production time of each user group based on the actual operation ratio of each user group by using the characteristic decomposition technology combined with the regression analysis method;

[0041] The impact rule summary module is used to summarize the impact rules of various business expansion changes on electricity demand based on the monthly trend of the operation ratio and the time to reach full production of each type of user group.

[0042] Preferably, the service type includes at least one or more of the following: a new installation service type, a capacity increase service type, a capacity reduction service type, an account cancellation service type, a suspension service type, and a suspension and restoration service type.

[0043] Preferably, the actual operation ratio includes the actual load operation ratio and the actual power operation ratio; the actual operation ratio calculation module includes:

[0044] The user group determination submodule is used to determine the user group of the newly installed business type who completed the business archiving in the archiving month and the user group of other business types except the newly installed business type who completed the business archiving in the month before the archiving;

[0045] The submodule for calculating the actual operation ratio corresponding to the newly installed business type is used to calculate the actual load operation ratio and the actual power operation ratio of the user group of the newly installed business type using the actual operation ratio calculation formula according to the newly installed business data of the user group of the newly installed business type who completed the business archiving in the archiving month;

[0046] The submodule for calculating the actual operation ratio corresponding to other business types is used to calculate the actual load operation ratio and actual power operation ratio of user groups of other business types except the newly installed business type based on other business data of user groups of other business types except the newly installed business type that completed business archiving in the previous month, using the actual operation ratio calculation formula;

[0047] Among them, the newly installed business data includes at least one or more of the following: the total load generated by the total reported capacity of archiving month n in the mth month, the total reported capacity of archiving month n, the total electricity consumption generated by the total reported capacity of archiving month n in the mth month and the number of days in the mth month.

[0048] Preferably, the calculation formula for the actual power supply ratio of the user group of the newly installed service type is as follows:

[0049]

[0050] Among them, η n,m P is the actual load operation ratio of the total installed capacity of the user group of the new service type in the archive month n in the mth month, n,m C is the total load generated by the total reported capacity in archive month n in month m, n The total reported capacity for archive month n.

[0051] Preferably, the calculation formula for the actual power supply ratio of the user group of the newly installed service type is as follows:

[0052]

[0053] Among them, λ n,m Q is the actual power supply ratio of the total installed capacity of the user group of the new service type in the archive month n in the mth month. n,m is the total electricity consumption generated by the total installed capacity in archive month n in month m, C n is the total reported capacity of archive month n, and g is the number of days in month m.

[0054] Preferably, the monthly trend operation ratio production reaching time calculation module includes:

[0055] The business type data judgment submodule is used to judge whether the business type data of various business expansion changes reaches a preset business type data threshold, and defines the business type data that reaches the business type data threshold as business type data with sufficient data, and defines the business type data that does not reach the business type data threshold as business type data with insufficient data;

[0056] The submodule for calculating the monthly trend operation ratio and production time with sufficient data is used to perform feature decomposition and regression analysis on the business type data with sufficient data, based on the actual monthly operation ratio of the corresponding user group in the current archive month or the month before the current archive, and calculate the monthly trend operation ratio and production time of the user group corresponding to the business type data with sufficient data;

[0057] The submodule for calculating the trend operation ratio and production time of insufficient data is used to count and calculate the average monthly actual operation ratio of the same type of user groups that have completed archiving in multiple archiving months or multiple months before archiving for business type data with insufficient data, and perform feature decomposition and regression analysis based on the average monthly actual operation ratio to calculate the monthly trend operation ratio and production time of the user group corresponding to the business type data with insufficient data;

[0058] The trend operation ratio and production reaching time summary submodule is used to summarize the monthly trend operation ratio and production reaching time of the user groups corresponding to the business type data with sufficient data and the monthly trend operation ratio and production reaching time of the user groups corresponding to the business type data with insufficient data, and generate the monthly trend operation ratio and production reaching time of each type of user groups.

[0059] Preferably, the submodule for calculating the monthly trend operation ratio and the time to reach full capacity with sufficient data is specifically used for:

[0060] For business type data with sufficient data, draw an actual operation ratio curve based on the actual operation ratio of the corresponding user group in the current archiving month or the month before the current archiving month;

[0061] Using characteristic decomposition technology, extracting the curve trend component from the actual operation ratio curve, and fitting the curve trend component using regression analysis method, to determine the monthly trend operation ratio and production time of the user group corresponding to the business type data with sufficient data;

[0062] The characteristic decomposition technology adopts one of the following: STL decomposition algorithm, wavelet decomposition algorithm, empirical mode decomposition algorithm and X-12 seasonal adjustment method; the regression analysis method adopts one of the following: logistic regression model, polynomial regression model.

[0063] Preferably, the submodule for calculating the trend operation ratio and the time to reach full capacity with insufficient data is specifically used for:

[0064] For business type data with insufficient data, statistics and calculation are conducted on the average actual operation ratio of the same type of user groups that have completed archiving for multiple archiving months or multiple months before archiving;

[0065] According to the average value of the actual operation ratio on a monthly basis, a mean value curve is drawn, and the characteristic decomposition technology is used to extract the curve trend component of the mean value curve, and the regression analysis method is used to fit the curve trend component to determine the monthly trend operation ratio and production reaching time of the user group corresponding to the business type data with insufficient data.

[0066] Preferably, it also includes: a refined classification module, which is used to:

[0067] According to the refined classification types, the classified user groups are divided into categories to determine the refined types of each user group;

[0068] Among them, the refined classification includes at least one or more of the following: regional type, voltage level type and industry category.

[0069] Based on the same inventive concept, the patent application of the present invention also provides an electronic device, comprising: at least one processor and a memory; the memory and the processor are connected via a bus;

[0070] The memory is used to store one or more programs;

[0071] When the one or more programs are executed by the at least one processor, a method for analyzing the influence of business expansion changes on electricity demand as described above is implemented.

[0072] Based on the same inventive concept, the patent application of the present invention also provides a readable storage medium on which an execution program is stored. When the execution program is executed, a method for analyzing the influence of business expansion changes on electricity demand as described above is implemented.

[0073] Compared with the closest prior art, the patent application of this invention has the following beneficial effects:

[0074] The patent application of the present invention provides a method and system for analyzing the influence of business expansion changes on electricity demand, including: classifying user groups of business expansion changes according to the business type of business expansion changes; based on the business expansion change data of each type of user group obtained after classification, using the actual operation ratio calculation formula to calculate the actual operation ratio of each type of user group; according to the actual operation ratio of each type of user group, using the characteristic decomposition technology combined with the regression analysis method to calculate the monthly trend operation ratio and production time of each type of user group; according to the monthly trend operation ratio and production time of each type of user group, summarizing the influence of various types of business expansion changes on electricity demand; the present invention analyzes the influence of various types of business expansion changes on electricity demand according to the business type of business expansion changes. The user groups of various business expansion changes were analyzed, and the actual operation ratio of each user group was calculated, reflecting the impact of business expansion changes on the growth of electricity demand. The characteristic decomposition technology was combined with the regression analysis method to deeply mine the actual operation ratio data of various user groups, analyze the time series relationship between various business expansion changes and electricity demand, extract and analyze the changing trend of electricity demand, and provide a scientific basis for accurately summarizing the impact rules of different user groups. This not only helps power companies to plan and provide power supply more effectively and improve electricity efficiency, but also provides strong support for formulating scientific operation strategies and promotes the power industry to move towards sustainable development. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 A flow chart of a method for analyzing the influence of business expansion changes on electricity demand provided in the patent application of the present invention;

[0076] Figure 2 When the data provided for the invention patent application is relatively sufficient, a structural diagram of the business expansion impact law analysis method using new installation as an example;

[0077] Figure 3 When the data provided for the invention patent application is insufficient, a structural diagram of the business expansion impact law analysis method using new installation as an example;

[0078] Figure 4 A schematic diagram of the trend component and fitting curve of the mean value of the new equipment industry expansion load operation ratio in the automobile manufacturing industry in 2022 provided for the patent application of this invention;

[0079] Figure 5 A schematic diagram of a system for analyzing the influence of business expansion changes on electricity demand provided in the patent application of the present invention;

[0080] Figure 6 A schematic structural diagram of an electronic device provided for the patent application of the present invention. DETAILED DESCRIPTION

[0081] The specific implementation methods of the patent application of the present invention are further described in detail below in conjunction with the accompanying drawings.

[0082] Embodiment 1:

[0083] The patent application of this invention provides a method for analyzing the influence of business expansion changes on electricity demand. Figure 1 As shown, including:

[0084] Step 1: Classify the user groups of business expansion changes according to the business types of business expansion changes;

[0085] Step 2: Based on the business expansion change data of each user group obtained after classification, the actual operation ratio of each user group is calculated using the actual operation ratio calculation formula;

[0086] Step 3: Based on the actual operation ratio of each user group, the characteristic decomposition technology combined with the regression analysis method is used to calculate the monthly trend operation ratio and production time of each user group;

[0087] Step 4: Based on the monthly trend of the operation ratio and the time to reach full capacity of each type of user group, summarize the impact of various business expansion changes on electricity demand.

[0088] In one implementation of step 1 above, the service type includes at least one or more of the following: a new service type, a capacity increase service type, a capacity reduction service type, an account cancellation service type, a suspension service type, and a suspension and restoration service type.

[0089] For example, the user groups of business expansion changes are classified according to the business types of business expansion changes (new installation, capacity increase, capacity reduction, account cancellation, suspension, suspension recovery); the present invention classifies the user groups of business expansion changes from multiple dimensions, laying a multi-dimensional data foundation for in-depth analysis.

[0090] In one implementation, after classifying the user groups of the business expansion change according to the service type of the business expansion change, the method further includes:

[0091] According to the refined classification types, the classified user groups are divided into categories to determine the refined types of each user group;

[0092] Among them, the refined classification includes at least one or more of the following: regional type, voltage level type and industry category.

[0093] For example, users of the same business expansion change service type are further refinedly classified, and the classification basis mainly includes the region, voltage level, industry category, etc.; the present invention divides the user group of each business expansion change more finely, and obtains the refined type of user group of each business expansion change.

[0094] The actual operation ratio in step 2 includes the actual load operation ratio and the actual power operation ratio; the business expansion change data of various user groups obtained after classification, using the actual operation ratio calculation formula to calculate the actual operation ratio of various user groups, including:

[0095] Determine the user groups of the newly installed business type who completed business archiving in the archiving month and the user groups of other business types except the newly installed business type who completed business archiving in the month before archiving;

[0096] According to the newly installed business data of the user group of the newly installed business type who completed the business archiving in the archiving month, the actual load operation ratio and the actual power operation ratio of the user group of the newly installed business type are calculated using the actual operation ratio calculation formula;

[0097] For example, when analyzing from the perspective of load change, sort out the user groups of new business expansion and change services that have completed business archiving in the same month (set as archiving month n), and calculate the total reported capacity C of this user group n and the total monthly load E in the subsequent months n,m ;

[0098] For example, in January 2022, there are new users A and B. User A's new installed capacity is 100 kVA, and the load generated in March 2022 is 20 kW; User B's capacity is 150 kVA, and the load generated in March 2022 is 50 kW. Then the total new installed capacity in January 2022 is C1=100+150=250 kVA. The total new installed capacity of the new users in January 2022 generates a total load E in March. 1,3 =20+50=70 kilowatts.

[0099] When analyzing from the perspective of power consumption changes, sort out the new business expansion and change business users who have completed business archiving in the same month (set as archiving month n), and calculate the total registered capacity C of this user group n And the total electricity consumption per month in the subsequent months G n,m .

[0100] For example, in January 2022, there are new users A and B. User A's new installed capacity is 100 kVA, and the electricity consumption generated in March 2022 is 20 kWh; User B's capacity is 150 kVA, and the electricity consumption generated in March 2022 is 50 kWh. Then the total new installed capacity in January 2022 is C1=100+150=250 kVA. The total electricity consumption generated by the total new installed capacity of the new users in January 2022 in March is G 1,3 =20+50=70 kWh.

[0101] Based on other business data of user groups of other business types except for the newly installed business type that completed business archiving one month before archiving, the actual load operation ratio and actual power operation ratio of user groups of other business types except for the newly installed business type are calculated using the actual operation ratio calculation formula;

[0102] For example, business expansion changes other than new installations, such as capacity increase, capacity reduction, account cancellation, suspension, and suspension and resumption, should use the load (electricity) of the month before archiving as the benchmark value to calculate the actual load operation ratio and actual electricity operation ratio of the corresponding user group.

[0103] Among them, the newly installed business data includes at least one or more of the following: the total load generated by the total reported capacity of archiving month n in the mth month, the total reported capacity of archiving month n, the total electricity consumption generated by the total reported capacity of archiving month n in the mth month and the number of days in the mth month.

[0104] In one implementation, the calculation formula for the actual power supply operation ratio of the user group of the newly installed service type is as follows:

[0105]

[0106] Among them, η n,m P is the actual load operation ratio of the total installed capacity of the user group of the new service type in the archive month n in the mth month, n,m C is the total load generated by the total reported capacity in archive month n in month m, n The total reported capacity for archive month n.

[0107] For example, P n,m The calculation formula is as follows:

[0108] P n,m =∑P n,m,k

[0109] Among them, P n,m P is the total load generated by the total reported capacity in archive month n in month m, n,m,k is the average daily maximum load generated by user k in month m in archive month n;

[0110] C n The calculation formula is as follows:

[0111]

[0112] Among them, C n is the total reported capacity of archive month n, C n,k The reported capacity for user k in archive month n.

[0113] In one implementation, the calculation formula for the actual power supply operation ratio of the user group of the newly installed service type is as follows:

[0114]

[0115] Among them, λ n,m Q is the actual power supply ratio of the total installed capacity of the user group of the new service type in the archive month n in the mth month. n,m is the total electricity consumption generated by the total installed capacity in archive month n in month m, C n is the total reported capacity of archive month n, and g is the number of days in month m.

[0116] Example, Q n,m The calculation formula is as follows:

[0117] Q n,m =∑Q n,m,k

[0118] Among them, Q n,m,k It is the electricity consumption generated by user k in the archived month n in the mth month. By calculating the actual operation ratio of each type of user group, the present invention can count the growth of electricity demand of the same type of user group and realize a comprehensive understanding of the user's electricity demand after the business change.

[0119] According to the actual operation ratio of each user group, the characteristic decomposition technology is combined with the regression analysis method to calculate the monthly trend operation ratio and production time of each user group, including:

[0120] Determine whether the business type data of various business expansion changes reaches a preset business type data threshold, and define the business type data that reaches the business type data threshold as business type data with sufficient data, and define the business type data that does not reach the business type data threshold as business type data with insufficient data;

[0121] For business type data with sufficient data, based on the actual monthly operation ratio of the corresponding user group in the current archiving month or the month before the current archiving, feature decomposition and regression analysis are performed to calculate the monthly trend operation ratio and production time of the user group corresponding to the business type data with sufficient data;

[0122] For business type data with insufficient data, statistics are collected and calculated for the average actual operation ratio of the same type of user groups that have completed archiving for multiple archiving months or multiple months before archiving. Based on the average actual operation ratio, feature decomposition and regression analysis are performed to calculate the monthly trend operation ratio and production time of the user groups corresponding to the business type data with insufficient data;

[0123] The monthly trend operation ratio and production completion time of the user groups corresponding to the business type data with sufficient data and the monthly trend operation ratio and production completion time of the user groups corresponding to the business type data with insufficient data are summarized to generate the monthly trend operation ratio and production completion time of each type of user groups.

[0124] In one implementation, for the business type data with sufficient data, feature decomposition and regression analysis are performed based on the actual monthly operation ratio of the corresponding user group in the current archive month or the month before the current archive month, and the monthly trend operation ratio and production reaching time of the user group corresponding to the business type data with sufficient data are calculated, including:

[0125] For business type data with sufficient data, draw an actual operation ratio curve based on the actual operation ratio of the corresponding user group in the current archiving month or the month before the current archiving month;

[0126] Using characteristic decomposition technology, extracting the curve trend component from the actual operation ratio curve, and fitting the curve trend component using regression analysis method, to determine the monthly trend operation ratio and production time of the user group corresponding to the business type data with sufficient data;

[0127] For example, for the newly installed business type data with sufficient data, the actual operation ratio curve of the user group is drawn according to the actual operation ratio on a monthly basis, the characteristic decomposition technology is used to extract the trend component of the curve, and the regression analysis method is used to fit the trend component to determine the monthly trend operation ratio and production reaching time of the user group of this new installation type in the archive month n.

[0128] By averaging the monthly trend operation ratio and production time of the same type of user groups that completed archiving in different months, the monthly trend operation ratio and production time of this type of user group are finally determined. The calculation formula for the production time is as follows:

[0129] T eh =∑T e,n / h

[0130] Among them, T eh is the production reaching time of this user group, T e,n is the production reaching time of this type of user group that completes archiving in archiving month n, and h is the number of archiving months counted.

[0131] There are two definitions of the production time:

[0132] Definition 1: The time when the business expansion business reaches the point where the load (or power consumption) tends to be stable (as reflected by the slope of the monthly operation ratio curve tending to 0) is called the production reaching time;

[0133] Definition 2: The time when the monthly operation ratio reaches a certain threshold (such as 40%) is called the production reaching time, and this threshold can be adjusted according to actual conditions.

[0134] Among them, the characteristic decomposition technology adopts one of the following: STL (Seasonal and Trend decomposition using Loess, time series) decomposition algorithm, wavelet decomposition algorithm, empirical mode decomposition algorithm and X-12 seasonal adjustment method; the regression analysis method adopts one of the following: logistic regression model, polynomial regression model.

[0135] For example, the STL decomposition algorithm is a smoothed seasonal and trend decomposition algorithm based on a locally weighted scatter plot.

[0136] In one implementation, for the business type data with insufficient data, statistics are counted and calculated for the average monthly actual operation ratio of the same type of user groups that have completed archiving in multiple archiving months or multiple months before archiving, and based on the average monthly actual operation ratio, feature decomposition and regression analysis are performed to calculate the monthly trend operation ratio and production time of the user group corresponding to the business type data with insufficient data, including:

[0137] For business type data with insufficient data, statistics and calculation are conducted on the average actual operation ratio of the same type of user groups that have completed archiving for multiple archiving months or multiple months before archiving;

[0138] According to the average value of the actual operation ratio on a monthly basis, a mean value curve is drawn, and the characteristic decomposition technology is used to extract the curve trend component of the mean value curve, and the regression analysis method is used to fit the curve trend component to determine the monthly trend operation ratio and production reaching time of the user group corresponding to the business type data with insufficient data.

[0139] For example, for newly installed business type data with insufficient data, the actual monthly operation ratio of the newly installed type of user groups that completed archiving in different months is counted and analyzed, the average monthly actual operation ratio is calculated, the average curve is drawn, the characteristic decomposition technology is used to extract the trend component of the curve, and the regression analysis method is used to fit the trend component to determine the monthly trend operation ratio and production reaching time of the newly installed type of user groups; the present invention uses the characteristic decomposition technology to extract the changing trend of electricity demand and eliminate the interference of external factors, so as to provide a scientific basis for accurately evaluating the changes in users' electricity demand after business archiving.

[0140] The above step 4 includes selecting data from multiple archived months or the month before the archived month for analysis, and finally summarizing the relevant analysis results to summarize and determine the influence of business expansion of various user groups on electricity demand, and also includes fine classification according to dimensions such as business type, voltage level, industry type, and user area, and finely analyzing the influence of business expansion changes on load of the same user group on a monthly basis; in view of the problem that the impact of business expansion changes of users in the power industry on electricity demand is unclear and difficult to accurately evaluate, the present invention provides a more comprehensive, detailed and in-depth business expansion change user classification and analysis method, which better solves the complexity of load fluctuations and business changes faced by the power industry. The present invention adopts a multi-dimensional classification method to classify business expansion change users by comprehensively considering the electricity usage habits, policies, economic environment and other factors of different users. Subsequently, by statistically analyzing the growth of electricity demand of the same type of business expansion change user group, and extracting and analyzing the trend of electricity demand changes, a comprehensive grasp of the law of business change impact is achieved. Finally, the business impact law of business expansion change of the user group is determined by regression analysis and other methods; the present invention can be used in the power industry, especially for power companies or relevant management agencies to scientifically evaluate the impact of different types of business expansion changes on electricity demand, so as to better plan power supply, improve electricity efficiency, and achieve sustainable development of the power industry. First, the present invention uses multi-dimensional classification to consider factors such as user electricity usage habits, policies, and economic environment, and accurately divides the user group of business expansion change, laying the foundation for in-depth analysis. Secondly, by statistically analyzing the growth of electricity demand of the same type of user group month by month, a comprehensive understanding of the user's electricity demand after the business change is achieved. Subsequently, the characteristic decomposition technology is used to extract the trend of electricity demand changes, eliminate the interference of external factors, and provide a scientific basis for accurately evaluating the changes in user electricity demand after business archiving. Finally, by analyzing multiple archived months, we summarize the business impact patterns of business expansion and change for different user groups, such as Figure 2 The following is a structural diagram of the business expansion impact analysis method using new installations as an example when data is sufficient; Figure 3 The following is a structural diagram of the business expansion impact law analysis method using new installation as an example when data is insufficient; Figure 2 and Figure 3 It mainly consists of four steps: classification of users of business expansion changes, statistics on the impact of business expansion changes on the growth of electricity demand, extraction and analysis of changing trends in electricity demand, and determination of the impact of business expansion changes on user groups. This will not only help power companies to more effectively plan and provide power supply and improve electricity efficiency, but also provide strong support for the formulation of scientific operation strategies and promote the power industry to move towards sustainable development.

[0141] The present invention analyzes the user groups of each business expansion change according to the business type of the business expansion change, calculates the actual operation ratio of each user group of the business expansion change, reflects the impact of the business expansion change on the growth of electricity demand, and uses characteristic decomposition technology combined with regression analysis method to conduct in-depth mining of the actual operation ratio data of each user group, analyze the time series relationship between various business expansion changes and electricity demand, extract and analyze the changing trend of electricity demand, improve the comprehensiveness and accuracy of the evaluation of the business expansion impact law of different user groups, and improve electricity efficiency.

[0142] Example 2

[0143] According to the business expansion data of automobile manufacturing users from January 2022 to December 2023 provided by a certain place, new high-voltage users with a voltage level of 10kV in the automobile manufacturing industry were selected, and the business expansion impact law analysis method proposed in this application was used to analyze the production capacity reaching law of new business expansion load in the industry.

[0144] First, extract the newly installed users archived in different months from January to December 2022, and obtain the installed capacity C for each month from January to December 2022 n ; Calculate the total load P generated by new users in each subsequent month from January to December 2022 n,m and monthly operation ratio η n,m ; The trend component of the monthly operation ratio curve is extracted by wavelet decomposition algorithm, and the monthly operation ratio and production time T are obtained according to the trend component curve. e , based on the statistical analysis results of several months, the average production time T of the industry is finally determined. eh And the proportion of operation when reaching full capacity. The analysis results are as follows Figure 4 As shown in Figure 2, when m=14, the slope of the curve approaches 0, so the average time for Hubei automobile manufacturing industry to reach full capacity is T eh =14, the operation ratio at full capacity is 36.74%. See Table 1 for specific calculation values.

[0145] Table 1 Monthly operation ratio of new users in the automobile manufacturing industry η n,m (%) Details (Partial load in 2022)

[0146]

[0147] The time T for new business expansion to reach full capacity in the industry in Table 1 e =13.58≈14 months is the average monthly operation ratio η n,m It can be calculated after the curve trend is extracted, or it can be obtained by taking the average value of the time for the new business expansion to reach full capacity from January to December. There will be a slight difference between the two. From the perspective of load, the average value of the time for the new business expansion to reach full capacity in 2022 T e=13.58≈14 months.

[0148] Embodiment 3:

[0149] Based on the same inventive concept, the patent application of the present invention also provides a system for analyzing the influence of business expansion changes on electricity demand. Figure 5 As shown, it includes: business type group classification module, actual operation ratio calculation module, monthly trend operation ratio production time calculation module and impact rule summary module;

[0150] The business type group classification module is used to classify the user group of the business expansion change according to the business type of the business expansion change;

[0151] The actual operation ratio calculation module is used to calculate the actual operation ratio of each user group based on the business expansion change data of each user group obtained after classification, using the actual operation ratio calculation formula;

[0152] The monthly trend operation ratio and production time calculation module is used to calculate the monthly trend operation ratio and production time of each user group based on the actual operation ratio of each user group by using the characteristic decomposition technology combined with the regression analysis method;

[0153] The impact rule summary module is used to summarize the impact rules of various business expansion changes on electricity demand based on the monthly trend of the operation ratio and the time to reach full production of each type of user group.

[0154] Preferably, the service type includes at least one or more of the following: a new installation service type, a capacity increase service type, a capacity reduction service type, an account cancellation service type, a suspension service type, and a suspension and restoration service type.

[0155] Preferably, the actual operation ratio includes the actual load operation ratio and the actual power operation ratio; the actual operation ratio calculation module includes:

[0156] The user group determination submodule is used to determine the user group of the newly installed business type who completed the business archiving in the archiving month and the user group of other business types except the newly installed business type who completed the business archiving in the month before the archiving;

[0157] The submodule for calculating the actual operation ratio corresponding to the newly installed business type is used to calculate the actual load operation ratio and the actual power operation ratio of the user group of the newly installed business type using the actual operation ratio calculation formula according to the newly installed business data of the user group of the newly installed business type who completed the business archiving in the archiving month;

[0158] The submodule for calculating the actual operation ratio corresponding to other business types is used to calculate the actual load operation ratio and actual power operation ratio of user groups of other business types except the newly installed business type based on other business data of user groups of other business types except the newly installed business type that completed business archiving in the previous month, using the actual operation ratio calculation formula;

[0159] Among them, the newly installed business data includes at least one or more of the following: the total load generated by the total reported capacity of archiving month n in the mth month, the total reported capacity of archiving month n, the total electricity consumption generated by the total reported capacity of archiving month n in the mth month and the number of days in the mth month.

[0160] Preferably, the calculation formula for the actual power supply ratio of the user group of the newly installed service type is as follows:

[0161]

[0162] Among them, η n,m P is the actual load operation ratio of the total installed capacity of the user group of the new service type in the archive month n in the mth month, n,m C is the total load generated by the total reported capacity in archive month n in month m, n The total reported capacity for archive month n.

[0163] Preferably, the calculation formula for the actual power supply ratio of the user group of the newly installed service type is as follows:

[0164]

[0165] Among them, λ n,m Q is the actual power supply ratio of the total installed capacity of the user group of the new service type in the archive month n in the mth month. n,m is the total electricity consumption generated by the total installed capacity in archive month n in month m, C n is the total reported capacity of archive month n, and g is the number of days in month m.

[0166] Preferably, the monthly trend operation ratio production reaching time calculation module includes:

[0167] The business type data judgment submodule is used to judge whether the business type data of various business expansion changes reaches a preset business type data threshold, and defines the business type data that reaches the business type data threshold as business type data with sufficient data, and defines the business type data that does not reach the business type data threshold as business type data with insufficient data;

[0168] The submodule for calculating the monthly trend operation ratio and production time with sufficient data is used to perform feature decomposition and regression analysis on the business type data with sufficient data, based on the actual monthly operation ratio of the corresponding user group in the current archive month or the month before the current archive, and calculate the monthly trend operation ratio and production time of the user group corresponding to the business type data with sufficient data;

[0169] The submodule for calculating the trend operation ratio and production time of insufficient data is used to count and calculate the average monthly actual operation ratio of the same type of user groups that have completed archiving in multiple archiving months or multiple months before archiving for business type data with insufficient data, and perform feature decomposition and regression analysis based on the average monthly actual operation ratio to calculate the monthly trend operation ratio and production time of the user group corresponding to the business type data with insufficient data;

[0170] The trend operation ratio and production reaching time summary submodule is used to summarize the monthly trend operation ratio and production reaching time of the user groups corresponding to the business type data with sufficient data and the monthly trend operation ratio and production reaching time of the user groups corresponding to the business type data with insufficient data, and generate the monthly trend operation ratio and production reaching time of each type of user groups.

[0171] Preferably, the submodule for calculating the monthly trend operation ratio and the time to reach full capacity with sufficient data is specifically used for:

[0172] For business type data with sufficient data, draw an actual operation ratio curve based on the actual operation ratio of the corresponding user group in the current archiving month or the month before the current archiving month;

[0173] Using characteristic decomposition technology, extracting the curve trend component from the actual operation ratio curve, and fitting the curve trend component using regression analysis method, to determine the monthly trend operation ratio and production time of the user group corresponding to the business type data with sufficient data;

[0174] The characteristic decomposition technology adopts one of the following: STL decomposition algorithm, wavelet decomposition algorithm, empirical mode decomposition algorithm and X-12 seasonal adjustment method; the regression analysis method adopts one of the following: logistic regression model, polynomial regression model.

[0175] Preferably, the submodule for calculating the trend operation ratio and the time to reach full capacity with insufficient data is specifically used for:

[0176] For business type data with insufficient data, statistics and calculation are conducted on the average actual operation ratio of the same type of user groups that have completed archiving for multiple archiving months or multiple months before archiving;

[0177] According to the average value of the actual operation ratio on a monthly basis, a mean value curve is drawn, and the characteristic decomposition technology is used to extract the curve trend component of the mean value curve, and the regression analysis method is used to fit the curve trend component to determine the monthly trend operation ratio and production reaching time of the user group corresponding to the business type data with insufficient data.

[0178] Preferably, it also includes: a refined classification module, which is used to:

[0179] According to the refined classification types, the classified user groups are divided into categories to determine the refined types of each user group;

[0180] Among them, the refined classification includes at least one or more of the following: regional type, voltage level type and industry category.

[0181] Example 4

[0182] like Figure 6 As shown, the present invention also provides an electronic device, which may be a computer device, a single-chip device, an intelligent mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, the processor, and the transceiver component are connected via a bus; the memory may be used to store an execution program, and an exemplary execution program may include instructions; the processor is used to execute the instructions stored in the memory. The memory may also be used to store data, which may be called and / or modified when the instructions are executed.

[0183] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in a storage medium to implement corresponding method flows or corresponding functions, so as to implement the steps of a method for analyzing the influence of business expansion changes on electricity demand in the above-mentioned embodiment.

[0184] Example 5

[0185] Based on the same inventive concept, the present invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory), which is a memory device in an electronic device for storing programs and data. It can be understood that the storage medium here can include both built-in storage media in electronic devices and, of course, extended storage media supported by electronic devices. The storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more execution programs (including program codes). It should be noted that the storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor loads and executes one or more instructions stored in the storage medium, which can implement the steps of a method for analyzing the influence of business expansion changes on power demand in the above embodiment.

[0186] It should be understood by those skilled in the art that the embodiments of the present invention patent application can be provided as methods, systems, or computer program products. Therefore, the present invention patent application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention patent application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0187] The present invention patent application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present invention patent application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0188] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0189] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0190] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the patent application of the present invention rather than to limit its protection scope. Although the patent application of the present invention is described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that after reading the patent application of the present invention, those skilled in the art can still make various changes, modifications or equivalent substitutions to the specific implementation methods of the application, but these changes, modifications or equivalent substitutions are all within the protection scope of the claims to be approved.

Claims

1. A method for analyzing the influence of business expansion and change on electricity demand, characterized in that: include: Classify the user groups of business expansion changes according to the business types of business expansion changes; Based on the business expansion change data of various user groups obtained after classification, the actual operation ratio of each user group is calculated using the actual operation ratio calculation formula; According to the actual operation ratio of each user group, the characteristic decomposition technology combined with the regression analysis method is used to calculate the monthly trend operation ratio and production time of each user group; Based on the monthly trend of the operation ratio and the time to reach full capacity of each type of user groups, the impact of various business expansion changes on electricity demand is summarized.

2. The method according to claim 1, characterized in that The service type includes at least one or more of the following: a new installation service type, a capacity increase service type, a capacity reduction service type, an account cancellation service type, a suspension service type, and a suspension and restoration service type.

3. The method according to claim 2, characterized in that The actual operation ratio includes the actual load operation ratio and the actual power operation ratio; The business expansion change data of each user group obtained after classification is calculated by using the actual operation ratio calculation formula to calculate the actual operation ratio of each user group, including: Determine the user groups of the newly installed business type who completed business archiving in the archiving month and the user groups of other business types except the newly installed business type who completed business archiving in the month before archiving; According to the newly installed business data of the user group of the newly installed business type who completed the business archiving in the archiving month, the actual load operation ratio and the actual power operation ratio of the user group of the newly installed business type are calculated using the actual operation ratio calculation formula; Based on other business data of user groups of other business types except for the newly installed business type that completed business archiving one month before archiving, the actual load operation ratio and actual power operation ratio of user groups of other business types except for the newly installed business type are calculated using the actual operation ratio calculation formula; Among them, the newly installed business data includes at least one or more of the following: the total load generated by the total reported capacity of archiving month n in the mth month, the total reported capacity of archiving month n, the total electricity consumption generated by the total reported capacity of archiving month n in the mth month and the number of days in the mth month.

4. The method according to claim 3, characterized in that The calculation formula for the actual power supply operation ratio of the user group of the new service type is as follows: Among them, η n,m P is the actual load operation ratio of the total installed capacity of the user group of the new service type in the archive month n in the mth month, n,m C is the total load generated by the total reported capacity in archive month n in month m, n The total reported capacity for archive month n.

5. The method according to claim 3, characterized in that The calculation formula for the actual power supply operation ratio of the user group of the new service type is as follows: Among them, λ n,m Q is the actual power supply ratio of the total installed capacity of the user group of the new service type in the archive month n in the mth month. n,m is the total electricity consumption generated by the total installed capacity in archive month n in month m, C n is the total reported capacity of archive month n, and g is the number of days in month m.

6. The method according to claim 1, characterized in that According to the actual operation ratio of each user group, the characteristic decomposition technology is combined with the regression analysis method to calculate the monthly trend operation ratio and production time of each user group, including: Determine whether the business type data of various business expansion changes reaches a preset business type data threshold, and define the business type data that reaches the business type data threshold as business type data with sufficient data, and define the business type data that does not reach the business type data threshold as business type data with insufficient data; For business type data with sufficient data, based on the actual monthly operation ratio of the corresponding user group in the current archiving month or the month before the current archiving, feature decomposition and regression analysis are performed to calculate the monthly trend operation ratio and production time of the user group corresponding to the business type data with sufficient data; For business type data with insufficient data, statistics are collected and calculated for the average actual operation ratio of the same type of user groups that have completed archiving for multiple archiving months or multiple months before archiving. Based on the average actual operation ratio, feature decomposition and regression analysis are performed to calculate the monthly trend operation ratio and production time of the user groups corresponding to the business type data with insufficient data; The monthly trend operation ratio and production completion time of the user groups corresponding to the business type data with sufficient data and the monthly trend operation ratio and production completion time of the user groups corresponding to the business type data with insufficient data are summarized to generate the monthly trend operation ratio and production completion time of each type of user groups.

7. The method according to claim 6, characterized in that For the business type data with sufficient data, feature decomposition and regression analysis are performed based on the actual monthly operation ratio of the corresponding user group in the current archiving month or the month before the current archiving, and the monthly trend operation ratio and production reaching time of the user group corresponding to the business type data with sufficient data are calculated, including: For business type data with sufficient data, draw an actual operation ratio curve based on the actual operation ratio of the corresponding user group in the current archiving month or the month before the current archiving month; Using characteristic decomposition technology, extracting the curve trend component from the actual operation ratio curve, and fitting the curve trend component using regression analysis method, to determine the monthly trend operation ratio and production time of the user group corresponding to the business type data with sufficient data; The characteristic decomposition technology adopts one of the following: STL decomposition algorithm, wavelet decomposition algorithm, empirical mode decomposition algorithm and X-12 seasonal adjustment method; the regression analysis method adopts one of the following: logistic regression model, polynomial regression model.

8. The method according to claim 6, characterized in that For the business type data with insufficient data, statistics are collected and calculated for the average monthly actual operation ratio of the same type of user groups that have completed archiving in multiple archiving months or multiple months before archiving, and based on the average monthly actual operation ratio, feature decomposition and regression analysis are performed to calculate the monthly trend operation ratio and production time of the user group corresponding to the business type data with insufficient data, including: For business type data with insufficient data, statistics and calculation are conducted on the average actual operation ratio of the same type of user groups that have completed archiving for multiple archiving months or multiple months before archiving; According to the average value of the actual operation ratio on a monthly basis, a mean value curve is drawn, and the characteristic decomposition technology is used to extract the curve trend component of the mean value curve, and the regression analysis method is used to fit the curve trend component to determine the monthly trend operation ratio and production reaching time of the user group corresponding to the business type data with insufficient data.

9. The method according to claim 1, characterized in that After classifying the user groups of the business expansion change according to the business type of the business expansion change, the method further includes: According to the refined classification types, the classified user groups are divided into categories to determine the refined types of each user group; Among them, the refined classification includes at least one or more of the following: regional type, voltage level type and industry category.

10. A system for analyzing the influence of business expansion and change on electricity demand, characterized in that: include: Business type group classification module, actual operation ratio calculation module, monthly trend operation ratio and production time calculation module, and impact rule summary module; The business type group classification module is used to classify the user group of the business expansion change according to the business type of the business expansion change; The actual operation ratio calculation module is used to calculate the actual operation ratio of each user group based on the business expansion change data of each user group obtained after classification, using the actual operation ratio calculation formula; The monthly trend operation ratio and production time calculation module is used to calculate the monthly trend operation ratio and production time of each user group based on the actual operation ratio of each user group by using the characteristic decomposition technology combined with the regression analysis method; The impact rule summary module is used to summarize the impact rules of various business expansion changes on electricity demand based on the monthly trend of the operation ratio and the time to reach full production of each type of user group.

11. The system according to claim 10, characterized in that The service type includes at least one or more of the following: a new installation service type, a capacity increase service type, a capacity reduction service type, an account cancellation service type, a suspension service type, and a suspension and restoration service type.

12. The system according to claim 11, characterized in that The actual operation ratio includes the actual load operation ratio and the actual power operation ratio; The actual operation ratio calculation module includes: The user group determination submodule is used to determine the user group of the newly installed business type who completed the business archiving in the archiving month and the user group of other business types except the newly installed business type who completed the business archiving in the month before the archiving; The submodule for calculating the actual operation ratio corresponding to the newly installed business type is used to calculate the actual load operation ratio and the actual power operation ratio of the user group of the newly installed business type using the actual operation ratio calculation formula according to the newly installed business data of the user group of the newly installed business type who completed the business archiving in the archiving month; The submodule for calculating the actual operation ratio corresponding to other business types is used to calculate the actual load operation ratio and actual power operation ratio of user groups of other business types except the newly installed business type based on other business data of user groups of other business types except the newly installed business type that completed business archiving in the previous month, using the actual operation ratio calculation formula; Among them, the newly installed business data includes at least one or more of the following: the total load generated by the total reported capacity of archiving month n in the mth month, the total reported capacity of archiving month n, the total electricity consumption generated by the total reported capacity of archiving month n in the mth month and the number of days in the mth month.

13. The system of claim 12, wherein: The calculation formula for the actual power supply operation ratio of the user group of the new service type is as follows: Among them, η n,m P is the actual load operation ratio of the total installed capacity of the user group of the new service type in the archive month n in the mth month, n,m C is the total load generated by the total reported capacity in archive month n in month m, n The total reported capacity for archive month n.

14. The system of claim 12, wherein: The calculation formula for the actual power supply operation ratio of the user group of the new service type is as follows: Among them, λ n,m Q is the actual power supply ratio of the total installed capacity of the user group of the new service type in the archive month n in the mth month. n,m is the total electricity consumption generated by the total installed capacity in archive month n in month m, C n is the total reported capacity of archive month n, and g is the number of days in month m.

15. The system of claim 10, wherein: The monthly trend operation ratio production reaching time calculation module includes: The business type data judgment submodule is used to judge whether the business type data of various business expansion changes reaches a preset business type data threshold, and defines the business type data that reaches the business type data threshold as business type data with sufficient data, and defines the business type data that does not reach the business type data threshold as business type data with insufficient data; The submodule for calculating the monthly trend operation ratio and production time with sufficient data is used to perform feature decomposition and regression analysis on the business type data with sufficient data, based on the actual monthly operation ratio of the corresponding user group in the current archive month or the month before the current archive, and calculate the monthly trend operation ratio and production time of the user group corresponding to the business type data with sufficient data; The submodule for calculating the trend operation ratio and production time of insufficient data is used to count and calculate the average monthly actual operation ratio of the same type of user groups that have completed archiving in multiple archiving months or multiple months before archiving for business type data with insufficient data, and perform feature decomposition and regression analysis based on the average monthly actual operation ratio to calculate the monthly trend operation ratio and production time of the user group corresponding to the business type data with insufficient data; The trend operation ratio and production reaching time summary submodule is used to summarize the monthly trend operation ratio and production reaching time of the user groups corresponding to the business type data with sufficient data and the monthly trend operation ratio and production reaching time of the user groups corresponding to the business type data with insufficient data, and generate the monthly trend operation ratio and production reaching time of each type of user groups.

16. The system of claim 15, wherein: The submodule for calculating the monthly trend operation ratio and the time to reach full capacity with sufficient data is specifically used for: For business type data with sufficient data, draw an actual operation ratio curve based on the actual operation ratio of the corresponding user group in the current archiving month or the month before the current archiving month; Using characteristic decomposition technology, extracting the curve trend component from the actual operation ratio curve, and fitting the curve trend component using regression analysis method, to determine the monthly trend operation ratio and production time of the user group corresponding to the business type data with sufficient data; The characteristic decomposition technology adopts one of the following: STL decomposition algorithm, wavelet decomposition algorithm, empirical mode decomposition algorithm and X-12 seasonal adjustment method; the regression analysis method adopts one of the following: logistic regression model, polynomial regression model.

17. The system of claim 16, wherein: The submodule for calculating the trend operation ratio and the time to reach full capacity of insufficient data is specifically used for: For business type data with insufficient data, statistics and calculation are conducted on the average actual operation ratio of the same type of user groups that have completed archiving for multiple archiving months or multiple months before archiving; According to the average value of the actual operation ratio on a monthly basis, a mean value curve is drawn, and the characteristic decomposition technology is used to extract the curve trend component of the mean value curve, and the regression analysis method is used to fit the curve trend component to determine the monthly trend operation ratio and production reaching time of the user group corresponding to the business type data with insufficient data.

18. The system of claim 10, wherein: Also includes: Refined classification module, used for: According to the refined classification types, the classified user groups are divided into categories to determine the refined types of each user group; Among them, the refined classification includes at least one or more of the following: regional type, voltage level type and industry category.

19. An electronic device, characterized in that: include: at least one processor and memory; The memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, a method for analyzing the influence of business expansion changes on electricity demand as described in any one of claims 1 to 9 is implemented.

20. A readable storage medium, characterized in that: An execution program is stored thereon, and when the execution program is executed, a method for analyzing the influence of business expansion changes on electricity demand as described in any one of claims 1 to 9 is implemented.

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