CN113780861A - Component index evaluation method and system based on user daily electric quantity adjustment value - Google Patents
Component index evaluation method and system based on user daily electric quantity adjustment value Download PDFInfo
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Abstract
The invention provides a composition index evaluation method and system based on a user daily electric quantity adjustment value, which comprises the following steps of S1, acquiring industry user data, and selecting sample users according to a preset screening rule; step S2, obtaining the periodic component of the historical daily electric quantity of the sample user, and calculating the daily electric quantity adjustment value of the sample user according to the periodic component of the historical daily electric quantity; step S3, calculating importance weight, contribution rate weight, unit energy consumption value weight and business expansion potential weight of the sample user at the industry level according to the daily electric quantity adjustment value of the sample user; and step S4, determining the component index of the daily electric quantity adjustment value of the industry user in the base period according to the importance weight, the contribution rate weight, the unit energy consumption production value weight and the industry expansion potential weight of the sample user. The method is based on the daily electric quantity adjustment value of the user, weights of multiple dimensions are given from an industry level and an individual level, the component index of the daily electric quantity adjusted by the user is determined, and data are provided for analyzing the business state of an enterprise and future economic trends.
Description
Technical Field
The invention relates to the technical field of power system automation, in particular to a composition index evaluation method and system based on a user daily electric quantity adjustment value.
Background
With the continuous deep advance of the energy revolution of China, the digital economy is added in the period and the high-efficiency transformation of energy is promoted, and the deep fusion of the two promotes a novel economic development form, namely energy digital economy. The energy digital economy is developed, the digitization is a strategic hand grab, and the data mining is a key way. As a hub for social energy supply, power grid enterprises should fully exert the characteristics of real-time monitoring and intuitive feedback of electric power data, analyze load characteristic changes and further mine deep values of the electric power data.
The electric power is used as the central energy of the social energy production and supply system and is closely related to social production and operation activities. However, the existing load characteristic research is not closely related to the actual production behavior change of the user, and the specific expression is that the random load fluctuation under the disturbance of inherent fluctuation such as user working day effect and objective factors such as weather and working intensity is not identified and distinguished. In addition, the problem of unbalanced development is more prominent while the total economic quantity of China rapidly rises, profit distribution is seriously unbalanced in different industries and enterprises of different scales, and the existing load characteristic research is less in industry and individual difference and is treated in a targeted manner. Therefore, a load fluctuation statistical method based on the daily electric quantity value of the user is urgently needed, and the method has more accurate and visual visibility in distinguishing fluctuation types and explaining production and operation behaviors, so that technical support is provided for a power grid company or an electricity selling company to provide differentiated services for important users such as large enterprises, and a data basis is laid for analyzing the operation state of the enterprises and the future economic trend.
Disclosure of Invention
The invention aims to provide a component index evaluation method and system based on a user daily electric quantity adjustment value.
On one hand, the component index evaluation method based on the daily electric quantity adjustment value of the user is provided, and comprises the following steps:
step S1, acquiring industry user data, and selecting sample users according to a preset screening rule;
step S2, obtaining the periodic component of the historical daily electric quantity of the sample user, and calculating the daily electric quantity adjustment value of the sample user according to the periodic component of the historical daily electric quantity;
step S3, calculating importance weight, contribution rate weight, unit energy consumption value weight and business expansion potential weight of the sample user at the industry level according to the daily electric quantity adjustment value of the sample user;
and step S4, determining the component index of the daily electric quantity adjustment value of the industry user in the base period according to the importance weight, the contribution rate weight, the unit energy consumption production value weight and the industry expansion potential weight of the sample user.
Preferably, in step S1, the preset filtering rule specifically includes:
selecting users meeting preset user standards to form a sampling space, wherein the preset user standards comprise users with electricity consumption duration longer than a preset electricity consumption duration threshold, users without abnormal fluctuation of historical electricity consumption and users without long-time no-load records;
sorting the users in a descending order according to the total power consumption of the users in the sample space in the previous year to obtain a power consumption sorting result;
and acquiring enterprise influence data and a national economy industry classification table in the region, selecting users with top electricity utilization ranks in all industries according to relative proportions according to electricity utilization sequencing results, the enterprise influence data and the national economy industry classification table, and outputting the users as sample users.
Preferably, in step S2, the obtaining of the periodic component of the historical daily power of the sample user specifically includes:
and decomposing the historical daily electric quantity of the sample user into a periodic component and a remainder component according to the following formula:
Dh(t)＝Td+R(t)
wherein, TdRepresents the cycle component value on day d of the week, d ═ 1,2, …, 7; dh(t) represents a user's historical daily electricity quantity; r (t) represents a remainder component; t denotes a user number.
Preferably, in step S2, the daily electricity amount adjustment value of the sample user is calculated according to the following formula:
wherein D' (t) represents a daily electricity amount adjustment value; d (t) represents the daily electricity actual value; d denotes day d of the week.
Preferably, in step S3, the calculating the importance weight of the sample user at the industry level specifically includes:
obtaining the grade results of the status of a plurality of judgment expert judgment sample users related to the industry; the ranking results include slightly important, generally important, more important, very important;
carrying out quantization processing on the grade result to obtain a fuzzy value corresponding to the grade result;
integrating fuzzy numbers corresponding to all expert evaluation grade results in an equal-weight linear weighting mode to obtain an important degree judgment fuzzy number of the industry;
judging the fuzzy number according to the important degree of the industry, and determining the gravity center size of the fuzzy number:
and carrying out normalization processing on the gravity center of the fuzzy number to obtain the importance weight.
Preferably, in step S3, the contribution rate weight of the sample user at the industry level is calculated according to the following formula:
wherein r isCiRepresenting a contribution ratio weight; delta EiThe increment of the added value of the regional industry of the ith industry is represented; Δ GDP represents the regional production total value increment.
Preferably, in step S3, the specific energy consumption value weight of the sample user at the industry level is calculated according to the following formula:
where ρ isiRepresenting a unit energy consumption output value weight; eiA regional industry added value representing the ith industry; diRepresenting the sum of the sample electric quantity of the ith industry; n represents the total number of industries involved with the sample.
Preferably, in step S3, the business expansion potential weight of the sample user at the business level is calculated according to the following formula:
wherein e ispRepresenting a business expansion potential weight; k is a radical ofpRepresenting the annual business expansion ratio; sS,pRepresents the capacity saturation; sp (y)Representing the capacity of the user p y years ago from the current year; etapCapacity usage proportion for user p; omegaiIs the set of users of the ith industry.
Preferably, the step S4 includes:
calculating the comprehensive weight value of the user p in the ith industry according to the following formula:
ωp＝(αsi+βrCi+θρi)·ep(p∈Ωi)
in the formula: omegapRepresents the integrated weight value of user p; siRepresenting an importance weight; r isCiRepresenting a contribution ratio weight; rhoiRepresenting a unit energy consumption output value weight; e.g. of the typepRepresenting a business expansion potential weight; alpha, beta and theta are combined coefficients, and are taken
Adjusting the daily electric quantity adjustment value of each user in the sample space to a basic value, and carrying out weighted summation on the daily electric quantity adjustment value according to the following formula to obtain a composition index in a basic period:
wherein, t0Representing a base period, and selecting a certain day of a year; the base value D' (t) of the daily power adjustment value of the user.
On the other hand, a composition index evaluation system based on the user daily electric quantity adjustment value is also provided, so as to realize the composition index evaluation method based on the user daily electric quantity adjustment value, and the method comprises the following steps:
the sample selection module is used for acquiring industry user data and selecting sample users according to a preset screening rule;
the daily electric quantity adjustment value calculation module is used for acquiring the periodic component of the historical daily electric quantity of the sample user and calculating the daily electric quantity adjustment value of the sample user according to the periodic component of the historical daily electric quantity;
the weight calculation module is used for calculating the importance weight, contribution rate weight, unit energy consumption value weight and business expansion potential weight of the sample user at the industry level according to the daily electric quantity adjustment value of the sample user;
and the component index calculation module is used for determining the component index of the daily electric quantity adjustment value of the industry user in the base period according to the importance weight, the contribution rate weight, the unit energy consumption production value weight and the industry expansion potential weight of the sample user.
In summary, the embodiment of the invention has the following beneficial effects:
according to the component index evaluation method and system based on the user daily electric quantity adjustment value, weights such as importance, contribution rate, unit energy consumption production value and business expansion potential are given from an industry level and an individual level based on the daily electric quantity adjustment value of an industry user, and the component index of the industry user adjustment daily electric quantity is selected and constructed at a base period, so that a data basis is provided for analyzing the business state of an enterprise and future economic trend. Displaying the electric quantity load development condition of an industry user in real time, wherein when the index is greater than 1000, the load is higher than the initial year, and the load lifting condition can be compared by comparing the index values of two days; when the index reflects the load fluctuation condition, the index has more accurate and visual visibility in distinguishing the fluctuation types and analyzing the production behavior change of the user.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is within the scope of the present invention for those skilled in the art to obtain other drawings based on the drawings without inventive exercise.
Fig. 1 is a main flow diagram of a component index evaluation method based on a user daily electricity quantity adjustment value according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of a composition index evaluation system based on a user daily electricity quantity adjustment value according to an embodiment of the present invention.
Fig. 3 is a comparison of the actual daily power of a certain user and the adjusted daily power in the embodiment of the present invention.
Fig. 4 is a composition index curve of the daily electricity amount adjusted based on the industry user in 2020 in the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings.
Fig. 1 is a schematic diagram illustrating an embodiment of a composition index evaluation method based on a user daily electricity adjustment value according to the present invention. In this embodiment, the method comprises the steps of:
step S1, acquiring industry user data, and selecting sample users according to a preset screening rule; it can be understood that the industry representative users with large electricity consumption and complete and abnormal data in the region N are screened out as sample objects.
In a specific embodiment, the preset screening rule specifically includes: selecting users meeting preset user standards to form a sampling space, wherein the preset user standards comprise users with electricity consumption duration longer than a preset electricity consumption duration threshold, users without abnormal fluctuation of historical electricity consumption and users without long-time no-load records; it can be understood that the user needs in the sampling space satisfy the following conditions at the same time: the electricity utilization duration is more than 3 years unless the user reports that the daily average electricity consumption is 10 bits before all users since the installation of the table; the historical electricity consumption has no or few obvious abnormal fluctuations or statistical errors; recording the idle load for a long time;
sorting the users in a descending order according to the total power consumption of the users in the sample space in the previous year to obtain a power consumption sorting result;
and acquiring enterprise influence data and a national economy industry classification table in the region, selecting users with the electricity consumption higher in each industry according to relative proportions according to the electricity consumption sequencing result, the enterprise influence data and the national economy industry classification table, and outputting the users as sample users. Namely, selecting representative industry users in the industry as sample users with large power consumption, high economic relevance and high position importance, wherein the specifically selected ranking can be set according to specific conditions; regional enterprise influence data can be marked on enterprises with the most prominent contribution and the greatest influence in the region or marked on national civilian enterprises; or marking the Baiqiang enterprises in the area listed in the previous year, and the like.
In a specific embodiment, all users in area a are ranked according to the total electricity consumption in 2019, wherein the difference between the 1 st user and the 173 th user is more than a hundred times, and is more than a thousand times as much as the difference between the 201 st user and the 1 st user. Therefore, users except 200 power consumption ordering are removed as a sample space, and then 100 industrial users are manually selected as samples according to a given rule; the selected samples relate to the industry and their code numbers are compared as shown in the following table:
step S2, obtaining the periodic component of the historical daily electric quantity of the sample user, and calculating the daily electric quantity adjustment value of the sample user according to the periodic component of the historical daily electric quantity; it can be understood that the historical daily electric quantity of the sample user in the previous natural year is subjected to STL decomposition to obtain a periodic component of the historical daily electric quantity of the sample user, and the actual daily electric quantity of the user 2020 is adjusted accordingly to obtain an adjusted daily electric quantity D' (t). The curves of the actual daily electricity amount and the adjusted daily electricity amount before and after adjustment by one user are shown in fig. 3.
In a specific embodiment, the historical daily electric quantity of the sample user is decomposed into a periodic component and a remainder component according to the following formula:
Dh(t)＝Td+R(t)
wherein, TdRepresents the cycle component value on day d of the week, d ═ 1,2, …, 7;Dh(t) represents a user's historical daily electricity quantity; r (t) represents a remainder component; t denotes a user number.
Calculating a daily electricity quantity adjustment value of the sample user according to the following formula:
wherein D' (t) represents a daily electricity amount adjustment value; d (t) represents the daily electricity actual value; d denotes day d of the week.
Step S3, calculating importance weight, contribution rate weight, unit energy consumption value weight and business expansion potential weight of the sample user at the industry level according to the daily electric quantity adjustment value of the sample user; it can be understood that indexes such as the importance, the contribution rate, the unit energy consumption output value and the business expansion potential of an individual level of an industry level are respectively constructed, and the weight value of the indexes is calculated.
In a specific embodiment, calculating the importance weight of the sample user at the industry level specifically includes:
obtaining the grade results of the status of a plurality of judgment expert judgment sample users related to the industry; the ranking results include slightly important, generally important, more important, very important; carrying out quantization processing on the grade result to obtain a fuzzy value corresponding to the grade result; integrating fuzzy numbers corresponding to all expert evaluation grade results in an equal-weight linear weighting mode to obtain an important degree judgment fuzzy number of the industry; judging the fuzzy number according to the important degree of the industry, and determining the gravity center size of the fuzzy number: and carrying out normalization processing on the gravity center of the fuzzy number to obtain the importance weight. It can be understood that, based on the fuzzy expert evaluation method, the importance weight is calculated, first, the relative importance of the status of the samples related to the industry is judged by inviting k experts according to five levels of slightly important, generally important, more important, very important, etc., and the expert judgment opinions are quantified by a trapezoidal fuzzy number M ═ l, M, n, r, where l, M, n, r are the lower bound value, the upper bound value, the lower bound value and the upper bound value of the fuzzy number respectively, and the values are shown in the following table:
then, integrating fuzzy numbers corresponding to all expert evaluation results in an equal-weight linear weighting mode to obtain an importance degree judgment fuzzy number M of the ith industryiComprises the following steps:
in the formula: miqRepresenting fuzzy numbers corresponding to the evaluation results of the qth expert on the ith industry;
next, the center of gravity c (M) of the trapezoidal blur number is calculatedi)：
Finally, the gravity center size c (M)i) The importance weight s can be obtained by normalizationi：
In the formula: n represents the total number of industries involved with the sample;
b) calculating contribution rate weight, and defining industry contribution rate rCIncreasing the ratio of the value increment to the total value increment of the area production for each industry, namely:
in the formula: delta EiThe increment of the added value of the regional industry of the ith industry is represented; delta GDP represents the increment of the total production value of a region
Specifically, calculating contribution rate weight and defining industry contribution rate rCThe ratio of the value increment to the total value increment of regional production is increased for each industry. Calculate sample user is based on the following formulaContribution ratio weight at industry level:
wherein r isCiRepresenting a contribution ratio weight; delta EiThe increment of the added value of the regional industry of the ith industry is represented; Δ GDP represents the regional production total value increment.
And more specifically, calculating the unit energy consumption and output value weight of the sample user at the industry level according to the following formula:
where ρ isiRepresenting a unit energy consumption output value weight; eiA regional industry added value representing the ith industry; diRepresenting the sum of the sample electric quantity of the ith industry; n represents the total number of industries involved with the sample.
More specifically, the business expansion potential weight is calculated, and the annual business expansion proportion k is providedpAnd volume saturation SS,pTwo indexes are used for judging the business expansion potential of the user p. And calculating the business expansion potential weight of the sample user at the industry level according to the following formula:
wherein e ispRepresenting a business expansion potential weight; k is a radical ofpRepresenting the annual business expansion ratio; sS,pRepresents the capacity saturation; sp (y)Representing the capacity of a user p y years ago from the current year；ΩiA set of users for the ith industry; etapThe capacity utilization ratio of the user p is obtained by adopting a grading and grading mode shown in the following table:
in this embodiment, the data such as the industry added value in area a, the annual sample relating to the industry added value increment and the total electric quantity are shown in the following table:
the annual area production total value increment is shown in the following table:
according to the 5-bit expert opinion result, calculating the gravity center of the fuzzy number to obtain an industry importance degree weight, an industry contribution rate weight and an energy consumption output relation weight as shown in the following table
And moreover, calculating the business expansion potential weight of 100 business users according to the capacity expansion condition of the business users in the last 3 years.
And step S4, determining the component index of the daily electric quantity adjustment value of the industry user in the base period according to the importance weight, the contribution rate weight, the unit energy consumption production value weight and the industry expansion potential weight of the sample user. It can be understood that the daily electricity quantity adjustment values are subjected to weighted summation, and the component index of the daily electricity quantity adjusted by the base period construction industry user is selected.
In a specific embodiment, the comprehensive weight value of the user p in the ith industry is calculated according to the following formula:
ωp＝(αsi+βrCi+θρi)·ep(p∈Ωi)
in the formula: omegapRepresents the integrated weight value of user p; siRepresenting an importance weight; r isCiRepresenting a contribution ratio weight; rhoiRepresenting a unit energy consumption output value weight; e.g. of the typepRepresenting a business expansion potential weight; alpha, beta and theta are combined coefficients, and are taken
Adjusting the daily electric quantity adjustment value of each user in the sample space to a basic value, weighting and summing the daily electric quantity adjustment values by utilizing the comprehensive weight value, and selecting a certain day t in the year0Taking the daily weighted sum as a reference value, and carrying out weighted summation on the daily electric quantity adjustment value according to the following formula to obtain a composition index in the base period:
wherein, t0Representing a base period, and selecting a certain day of a year; the base value D' (t) of the daily power adjustment value of the user.
In this embodiment, the above weights are integrated, and the base period is 1 month and 1 day in 2020, and the component index of the daily adjustment electric quantity of the sample industry user in 2020 is calculated, and a curve is plotted as shown in fig. 4.
As can be seen from fig. 4, the index value in 2020 is mostly over 1200, and the whole takes the shape of "r". Throughout the 2020 exponential change, it is worth noting the 2-3 month exponential dip, several distinct abrupt rises and falls, and the 10 month past exponential turbulence. Specifically, the method comprises the following steps: the affected index of epidemic situation falls to 700 in month 2 until the index recovers to the level of month 1 in the middle of 3 months, which indicates that the large enterprise basically realizes the reworking and production recovery. On one hand, large enterprises have obvious load drop in four legal festivals including the New year, the spring festival, the labor festival and the national day, wherein the drop amplitude is as follows: spring festival, national celebration, New year's day, and in this respect, the index also reflects the importance of holidays on the side. On the other hand, three abnormal fluctuations exist around 6-month-10 days, 8-month-20 days and 12-month-15 days, the load data on the day in the system is found to be wrong, so that the index is abnormally fluctuated, and the abnormality can be found and followed up on the second day of actual operation. After 10 months, the temperature change amplitude of Shenzhen city is increased, the temperature difference between adjacent days reaches +/-5 ℃, and the temperature fluctuates up and down at the upper limit of 24 ℃ in the human body comfortable temperature range, so that the load change caused by the air conditioner load difference is presumed. In the above analysis, the index plays a major role in: on one hand, the method can reflect the load change situation in time and reflect the production and operation behaviors of enterprises, such as post-construction and re-production; on the other hand, the utility model has the humanistic value of showing the degree of importance for holidays.
Fig. 2 is a schematic diagram illustrating an embodiment of a composition index evaluation system based on a user daily electricity adjustment value according to the present invention. In this embodiment, the system is used to implement the ingredient index evaluation method based on the user daily electricity quantity adjustment value, and includes:
the sample selection module is used for acquiring industry user data and selecting sample users according to a preset screening rule;
the daily electric quantity adjustment value calculation module is used for acquiring the periodic component of the historical daily electric quantity of the sample user and calculating the daily electric quantity adjustment value of the sample user according to the periodic component of the historical daily electric quantity;
the weight calculation module is used for calculating the importance weight, contribution rate weight, unit energy consumption value weight and business expansion potential weight of the sample user at the industry level according to the daily electric quantity adjustment value of the sample user;
and the component index calculation module is used for determining the component index of the daily electric quantity adjustment value of the industry user in the base period according to the importance weight, the contribution rate weight, the unit energy consumption production value weight and the industry expansion potential weight of the sample user.
For the specific implementation process, reference may be made to the specific process of the component index evaluation method based on the user daily electricity quantity adjustment value, which is not described herein again.
In summary, the embodiment of the invention has the following beneficial effects:
according to the component index evaluation method and system based on the user daily electric quantity adjustment value, weights such as importance, contribution rate, unit energy consumption production value and business expansion potential are given from an industry level and an individual level based on the daily electric quantity adjustment value of an industry user, and the component index of the industry user adjustment daily electric quantity is selected and constructed at a base period, so that a data basis is provided for analyzing the business state of an enterprise and future economic trend. Displaying the electric quantity load development condition of an industry user in real time, wherein when the index is greater than 1000, the load is higher than the initial year, and the load lifting condition can be compared by comparing the index values of two days; when the index reflects the load fluctuation condition, the index has more accurate and visual visibility in distinguishing the fluctuation types and analyzing the production behavior change of the user.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present invention, and it is therefore to be understood that the invention is not limited by the scope of the appended claims.
Claims (10)
1. A component index evaluation method based on a user daily electric quantity adjustment value is characterized by comprising the following steps:
step S1, acquiring industry user data, and selecting sample users according to a preset screening rule;
step S2, obtaining the periodic component of the historical daily electric quantity of the sample user, and calculating the daily electric quantity adjustment value of the sample user according to the periodic component of the historical daily electric quantity;
step S3, calculating importance weight, contribution rate weight, unit energy consumption value weight and business expansion potential weight of the sample user at the industry level according to the daily electric quantity adjustment value of the sample user;
and step S4, determining the component index of the daily electric quantity adjustment value of the industry user in the base period according to the importance weight, the contribution rate weight, the unit energy consumption production value weight and the industry expansion potential weight of the sample user.
2. The method according to claim 1, wherein in step S1, the preset filtering rule specifically includes:
selecting users meeting preset user standards to form a sampling space, wherein the preset user standards comprise users with electricity consumption duration longer than a preset electricity consumption duration threshold, users without abnormal fluctuation of historical electricity consumption and users without long-time no-load records;
sorting the users in a descending order according to the total power consumption of the users in the sample space in the previous year to obtain a power consumption sorting result;
and acquiring enterprise influence data and a national economy industry classification table in the region, selecting users with top electricity utilization ranks in all industries according to relative proportions according to electricity utilization sequencing results, the enterprise influence data and the national economy industry classification table, and outputting the users as sample users.
3. The method according to claim 2, wherein in step S2, the obtaining the periodic component of the historical daily power of the sample user specifically includes:
and decomposing the historical daily electric quantity of the sample user into a periodic component and a remainder component according to the following formula:
Dh(t)＝Td+R(t)
wherein, TdRepresents the cycle component value on day d of the week, d ═ 1,2, …, 7; dh(t) represents a user's historical daily electricity quantity; r (t) represents a remainder component; t denotes a user number.
4. The method of claim 3, wherein in step S2, the daily charge adjustment value of the sample user is calculated according to the following formula:
wherein D' (t) represents a daily electricity amount adjustment value; d (t) represents the daily electricity actual value; d denotes day d of the week.
5. The method according to claim 4, wherein in step S3, the calculating the importance weight of the sample user at the industry level specifically comprises:
obtaining the grade results of the status of a plurality of judgment expert judgment sample users related to the industry; the ranking results include slightly important, generally important, more important, very important;
carrying out quantization processing on the grade result to obtain a fuzzy value corresponding to the grade result;
integrating fuzzy numbers corresponding to all expert evaluation grade results in an equal-weight linear weighting mode to obtain an important degree judgment fuzzy number of the industry;
judging the fuzzy number according to the important degree of the industry, and determining the gravity center size of the fuzzy number:
and carrying out normalization processing on the gravity center of the fuzzy number to obtain the importance weight.
6. The method of claim 5, wherein in step S3, the contribution rate weight of the sample user at the industry level is calculated according to the following formula:
wherein r isCiRepresenting a contribution ratio weight; delta EiThe increment of the added value of the regional industry of the ith industry is represented; Δ GDP represents the regional production total value increment.
7. The method of claim 6, wherein in step S3, the specific energy consumption value weight of the sample user at the industry level is calculated according to the following formula:
where ρ isiRepresenting a unit energy consumption output value weight; eiA regional industry added value representing the ith industry; diRepresenting the sum of the sample electric quantity of the ith industry; n represents the total number of industries involved with the sample.
8. The method of claim 7, wherein in step S3, the industry expansion potential weight of the sample user at the industry level is calculated according to the following formula:
wherein e ispRepresenting a business expansion potential weight; k is a radical ofpRepresenting the annual business expansion ratio; sS,pRepresents the capacity saturation; sp (y)Representing the capacity of the user p y years ago from the current year; etapCapacity usage proportion for user p; omegaiIs the set of users of the ith industry.
9. The method of claim 1, wherein the step S4 includes:
calculating the comprehensive weight value of the user p in the ith industry according to the following formula:
ωp＝(αsi+βrCi+θρi)·ep(p∈Ωi)
in the formula: omegapRepresents the integrated weight value of user p; siRepresenting an importance weight; r isCiRepresenting a contribution ratio weight; rhoiRepresenting a unit energy consumption output value weight; e.g. of the typepRepresenting a business expansion potential weight; alpha is alphaBeta and theta are combined coefficients, and are taken
Adjusting the daily electric quantity adjustment value of each user in the sample space to a basic value, and carrying out weighted summation on the daily electric quantity adjustment value according to the following formula to obtain a composition index in a basic period:
wherein, t0Representing a base period, and selecting a certain day of a year; the base value D' (t) of the daily power adjustment value of the user.
10. A composition index evaluation system based on a user daily electricity adjustment value, for implementing the method according to any one of claims 1 to 9, comprising:
the sample selection module is used for acquiring industry user data and selecting sample users according to a preset screening rule;
the daily electric quantity adjustment value calculation module is used for acquiring the periodic component of the historical daily electric quantity of the sample user and calculating the daily electric quantity adjustment value of the sample user according to the periodic component of the historical daily electric quantity;
the weight calculation module is used for calculating the importance weight, contribution rate weight, unit energy consumption value weight and business expansion potential weight of the sample user at the industry level according to the daily electric quantity adjustment value of the sample user;
and the component index calculation module is used for determining the component index of the daily electric quantity adjustment value of the industry user in the base period according to the importance weight, the contribution rate weight, the unit energy consumption production value weight and the industry expansion potential weight of the sample user.
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