CN115730853A - Consumption prospect index calculation method and system based on electric power big data - Google Patents
Consumption prospect index calculation method and system based on electric power big data Download PDFInfo
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Abstract
The invention discloses a consumption landscape index calculation method based on electric power big data, which comprises the steps of obtaining electric power system data information of an area to be evaluated; establishing a power consumption index system of each industry in the area to be evaluated; calculating the power consumption index of each industry in the area to be evaluated; and calculating to obtain the consumption prosperity index of the area to be evaluated. The invention also discloses a system for realizing the power big data-based consumption prospect index calculation method. The invention gives full play to the practical application value of the power data; the method adopts a weighting mode combining an analytic hierarchy process and an entropy weight method and combines a subjective weighting method and an objective weighting method, so that the reliability of the weighting method is improved; therefore, the invention has high reliability, good accuracy and objective science.
Description
Technical Field
The invention belongs to the field of electric power big data, and particularly relates to a consumption prospect index calculation method and system based on electric power big data.
Background
With the development of economic technology and the improvement of living standard of people, electric energy becomes essential secondary energy in production and life of people, and brings endless convenience to production and life of people. Therefore, ensuring stable and reliable supply of electric energy is one of the most important tasks of the power system.
The big data of the power industry can reflect social consumption trend to a certain extent, and the social consumption trend influences the formulation of power generation plans, construction plans and the like of the power industry to a certain extent. Therefore, the social consumption prospect index is calculated through big data of the power industry, and safe and stable operation of the power system can be better promoted.
At present, although the consumption prospect index of the power market can be calculated by the existing power big data-based consumption prospect index calculation method, the method does not consider the power utilization difference of different industries and does not process data carefully. Therefore, the index set established according to the electricity utilization data is too general, and the practical application value needs to be improved. The drawbacks of such methods are particularly: the consumption conditions of various industries cannot be reflected, and the significance of guiding scientific power utilization of various industries is not realized; a comprehensive power consumption index set cannot be screened out, and the power consumption index calculation result is greatly different from the real consumption condition.
Disclosure of Invention
The invention aims to provide a consumption prospect index calculation method based on electric power big data, which is high in reliability, good in accuracy and objective and scientific.
The invention also aims to provide a system for realizing the consumption prosperity index calculation method based on the electric power big data.
The invention provides a consumption prospect index calculation method based on electric power big data, which comprises the following steps:
s1, acquiring data information of a power system of an area to be evaluated;
s2, establishing a power consumption index system of each industry in the area to be evaluated according to the data information obtained in the step S1;
s3, calculating the power consumption index of each industry in the area to be evaluated according to the power consumption index system of each industry constructed in the step S2;
and S4, calculating to obtain the consumption prosperity index of the area to be evaluated according to the electric power consumption index of each industry obtained by calculation in the step S3.
Step S2, establishing an electric power consumption index system of each industry in the area to be evaluated according to the data information obtained in the step S1, specifically, establishing an electricity consumption index set of electricity sales, a user number electricity consumption index set, an average electricity price electricity consumption index set and an industry expansion capacity electricity consumption index set according to the data information obtained in the step S1, and calculating objective weights corresponding to the index sets by adopting an entropy weight method.
The method comprises the following steps of establishing a power consumption index system of each industry in the area to be evaluated according to the data information obtained in the step S1, and specifically comprises the following steps:
constructing a power consumption index set of electricity sales, a user number power consumption index set, an average electricity price power consumption index set and an industry expansion capacity power consumption index set; all the index sets have m sample data;
establishing a decision matrix X, wherein the element X in the matrix ij The ith sample data of the jth index;
standardizing the established decision matrix X by adopting the following formula to obtain a standardized decision matrix Y:
for the forward indicator, normalization is performed using the following equation:
in the formula y ij For element X in the normalized decision matrix X ij ；min(x ij ) Is an element X in a decision matrix X ij Minimum value of (d); max (x) ij ) Is an element X in a decision matrix X ij Maximum value of (d); the forward index is defined as an index which is better when the numerical value is larger, and specifically comprises an electricity consumption index set of electricity sales, a user number electricity consumption index set and an industry expansion capacity electricity consumption index set;
for the fitness index, normalization was performed using the following equation:
in the formula y ij For element X in the normalized decision matrix X ij ；z ij Is an element x ij Is normalized by a positive transformation of
E j The average value of j column elements in the decision matrix X is obtained; the moderate index is defined as an index with a numerical value closer to a set value and a better value, and specifically comprises an average electricity price power consumption index set;
and according to the established standardized decision matrix Y, calculating the specific gravity value of each sample in the corresponding index by adopting the following formula:
in the formula, p ij The proportion of the ith sample in the corresponding index under the jth index is calculated;
according to the specific gravity value of each sample in the corresponding index, calculating the entropy value of each index by adopting the following formula:
in the formula, e j Entropy value of j index;
according to the entropy value of each index, the weighted value of each index is calculated by adopting the following formula:
in the formula
The weight value of the jth index; n is the number of indexes.
Step S3, calculating the power consumption index of each industry in the area to be evaluated according to the power consumption index system of each industry constructed in step S2, specifically including the following steps:
summarizing electricity selling quantity data, user number data, average electricity price data and business expansion capacity data in a base period and a report period;
and according to the summarized data and the power consumption index system of each industry constructed in the step S2, calculating the power consumption index of each industry by adopting the following formula:
in the formula ECI j Is the power consumption index of the jth industry; s 1 Selling electricity for the reporting period; s 0 Selling electricity for the base period;
is the weight of sold electricity quantity; u. of 1 The number of users in the report period; u. of 0 The number of the users in the base period;
the weight of the user number; v. of 1 Is the average electricity price in the report period; v. of 0 Is the average electricity price of the base period;
is the average electricity price weight; c. C 1 Capacity is expanded for reporting period business; c. C 0 Capacity is expanded for the base-term business;
installing capacity for business expansion;
step S4, calculating the consumption landscape index of the area to be evaluated according to the power consumption index of each industry calculated in step S3, and specifically including the following steps:
asking the experts to compare every two electric power consumption indexes of all industries obtained in the step S3 to obtain the relative importance between every two electric power consumption indexes: comparing the power consumption indexes of N industries pairwise to obtain a comparison matrix R N×N ：
In the formula r ij Comparing the power consumption index of the ith industry with the power consumption index of the jth industry;
for the obtained contrast matrix R N×N And (3) carrying out consistency check:
calculating a check coefficient CR of
CI is a consistency index and
λ R is a contrast matrix R N×N The maximum characteristic root of (1), N is contrastMatrix R N×N The order of (a); RI is a random coefficient, and the value rule is as follows:
if N =1, RI =0; if N =2, RI =0; RI =0.58 if N = 3; if N =4 then RI =0.90; if N =5 then RI =1.12; if N =6 then RI =1.24; if N =7, RI =1.32; if N =8 then RI =1.41; if N =9 then RI =1.45; if N =10, RI =1.49;
the check coefficient CR is determined: if CR is less than the set threshold, then the contrast matrix R is determined N×N The consistency check of (1) is passed; otherwise, identify the contrast matrix R N×N The consistency check of (1) fails;
contrast matrix R N×N After the consistency check is passed, a contrast matrix R is calculated N×N W is W = { W 1 ,w 2 ,...,w n And normalizing the characteristic vector W to obtain a normalized characteristic vector W' = { W = 1 ',w' 2 ,...,w’ n }；
Calculating to obtain the single-level weight vector of the current level
Is composed of
W 'of' j Is the jth element in the normalized feature vector W';
finally, the consumption prosperity index ECI of the area to be evaluated is obtained through calculation
The invention also discloses a system for realizing the consumption prospect index calculation method based on the electric power big data, which specifically comprises a data acquisition module, an electric power consumption index system construction module, an electric power consumption index calculation module and a consumption prospect index calculation module; the data acquisition module, the power consumption index system construction module, the power consumption index calculation module and the consumption landscape index calculation module are sequentially connected in series; the data acquisition module is used for acquiring the data information of the power system of the area to be evaluated and uploading the data to the power consumption index system construction module; the power consumption index system construction module is used for establishing power consumption index systems of all industries in the area to be evaluated according to the acquired data and uploading the data to the power consumption index calculation module; the power consumption index calculation module is used for calculating power consumption indexes of various industries in the area to be evaluated according to the acquired data and uploading the data to the consumption scene index calculation module; and the consumption prosperity index calculation module is used for calculating to obtain the consumption prosperity index of the area to be evaluated according to the acquired data.
According to the consumption landscape index calculation method and system based on the electric power big data, the obtained electric power data are classified and analyzed in different industries, a comprehensive electric power consumption index set is normalized on the basis, and the actual application value of the electric power data is fully exerted; in the selection of the weighting method, the invention adopts a weighting mode combining an analytic hierarchy process and an entropy weight process, and the entropy weight process is used for objectively weighting each electric power consumption index under the industry electric power consumption index, and calculating to obtain the industry electric power consumption index; subjectively weighting each sub-industry power consumption index under the total industry power consumption index by using an analytic hierarchy process, and calculating to obtain the total industry power consumption index; finally, the invention also adopts a weighting mode combining a subjective weighting method and an objective weighting method, thereby improving the reliability of the weighting method; therefore, the invention has the advantages of high reliability, good accuracy and objective science.
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FIG. 1 is a schematic process flow diagram of the process of the present invention.
FIG. 2 is a functional block diagram of the system of the present invention.
Detailed Description
FIG. 1 is a schematic flow chart of the method of the present invention: the invention provides a consumption prospect index calculation method based on electric power big data, which comprises the following steps:
s1, acquiring data information of a power system of an area to be evaluated;
s2, establishing a power consumption index system of each industry in the area to be evaluated according to the data information obtained in the step S1; specifically, according to the data information obtained in the step S1, an electricity consumption index set of electricity sales amount, an electricity consumption index set of user number, an average electricity price electricity consumption index set and an electricity consumption index set of business expansion capacity are constructed, and an entropy weight method is adopted to calculate objective weights corresponding to the index sets;
the electricity selling quantity refers to the electricity sold to users (including wholesale households) by an electric power enterprise and the electricity supplied to non-electric power production, basic construction, overhaul, non-production departments and the like of the enterprise; the number of users refers to the total number of users using electricity in the limited evaluation area; the average electricity price is the average price of RMB which should be paid when one kilowatt of electricity is consumed in the daily life of people in the limited evaluation area; the business expansion capacity is the application capacity when a client applies for power utilization to a power supply enterprise;
in specific implementation, the method comprises the following steps:
constructing a power consumption index set of electricity sales, a user number power consumption index set, an average electricity price power consumption index set and an industry expansion capacity power consumption index set; all the index sets have m sample data;
establishing a decision matrix X, wherein the element X in the matrix ij The ith sample data of the jth index;
standardizing the established decision matrix X by adopting the following formula to obtain a standardized decision matrix Y:
for the forward indicator, normalization is performed using the following equation:
in the formula y ij For element X in the normalized decision matrix X ij ；min(x ij ) Is an element X in a decision matrix X ij Minimum value of (d); max (x) ij ) Is an element X in the decision matrix X ij Maximum value of (d); the forward index is defined as an index with a larger numerical value and better numerical value, and specifically comprises a power consumption index set of electricity selling quantity and user quantity electric powerA consumption index set and a business expansion capacity electric power consumption index set;
for the fitness index, normalization was performed using the following equation:
in the formula y ij For element X in the normalized decision matrix X ij ；z ij Is an element x ij Is normalized by a positive transformation of
E j The average value of the jth column element in the decision matrix X is obtained; the moderate index is defined as an index with a numerical value closer to a set value and a better value, and specifically comprises an average electricity price power consumption index set;
and according to the established standardized decision matrix Y, calculating the specific gravity value of each sample in the corresponding index by adopting the following formula:
in the formula, p ij The proportion of the ith sample in the corresponding index under the jth index is calculated;
according to the specific gravity value of each sample in the corresponding index, calculating to obtain the entropy value of each index by adopting the following formula:
in the formula, e j Entropy value of j index;
according to the entropy value of each index, the weighted value of each index is calculated by adopting the following formula:
in the formula
The weight value of the jth index; n is the number of indexes;
s3, calculating the power consumption index of each industry in the area to be evaluated according to the power consumption index system of each industry constructed in the step S2; the method specifically comprises the following steps:
summarizing electricity selling quantity data, user number data, average electricity price data and business expansion capacity data in a base period and a report period; in specific implementation, the basic period is kept unchanged within five years, and the last year planned in the last five years is taken as the basic period, for example, the basic period from 2016 to 2020 is 2015;
and according to the summarized data and the power consumption index system of each industry constructed in the step S2, calculating the power consumption index of each industry by adopting the following formula:
in the formula ECI j Is the power consumption index of the jth industry; s 1 Selling electricity for the reporting period; s 0 Selling electricity for the base period;
is the weight of sold electricity quantity; u. of 1 The number of users in the report period; u. of 0 The number of the users in the base period;
the weight of the user number; v. of 1 Is the average electricity price in the report period; v. of 0 Is the average electricity price of the base period;
is the average electricity price weight; c. C 1 Capacity is expanded for reporting period business; c. C 0 Loading capacity for the basal period business expansion;
to businessExpanding the installation capacity;
s4, calculating to obtain a consumption landscape index of the area to be evaluated according to the power consumption index of each industry calculated in the step S3; the method specifically comprises the following steps:
asking the experts to compare every two electric power consumption indexes of all industries obtained in the step S3 to obtain the relative importance between every two electric power consumption indexes: comparing the power consumption indexes of N industries pairwise to obtain a comparison matrix R N×N ：
In the formula r ij Comparing the power consumption index of the ith industry with the power consumption index of the jth industry;
for the obtained contrast matrix R N×N And (3) carrying out consistency check:
calculating the checking coefficient CR of
CI is a consistency index and
λ R is a contrast matrix R N×N N is the contrast matrix R N×N The order of (a); RI is a random coefficient, and the value rule is as follows:
if N =1, RI =0; if N =2, RI =0; RI =0.58 if N = 3; if N =4 then RI =0.90; if N =5 then RI =1.12; if N =6 then RI =1.24; if N =7, RI =1.32; if N =8 then RI =1.41; if N =9 then RI =1.45; if N =10, RI =1.49;
the check coefficient CR is determined: if CR is less than the set threshold, then the contrast matrix R is determined N×N The consistency check of (1) is passed; otherwise, identify the contrast matrix R N×N ToSex check fails;
contrast matrix R N×N After the consistency check of (2) is passed, a contrast matrix R is calculated N×N The eigenvector W corresponding to the maximum eigenvalue of (a) is W = { W = { (W) 1 ,w 2 ,...,w n And normalizing the characteristic vector W to obtain a normalized characteristic vector W' = { W } 1 ',w' 2 ,...,w’ n }；
Calculating to obtain the single-level weight vector of the current level
Is composed of
W 'of' j Is the jth element in the normalized feature vector W';
finally, the consumption prosperity index ECI of the area to be evaluated is obtained through calculation
FIG. 2 is a schematic diagram of functional modules of the system of the present invention: the system for realizing the consumption prospect index calculation method based on the electric power big data specifically comprises a data acquisition module, an electric power consumption index system construction module, an electric power consumption index calculation module and a consumption prospect index calculation module; the data acquisition module, the power consumption index system construction module, the power consumption index calculation module and the consumption landscape index calculation module are sequentially connected in series; the data acquisition module is used for acquiring the data information of the power system of the area to be evaluated and uploading the data to the power consumption index system construction module; the power consumption index system construction module is used for establishing power consumption index systems of all industries in the area to be evaluated according to the acquired data and uploading the data to the power consumption index calculation module; the power consumption index calculation module is used for calculating power consumption indexes of various industries in the area to be evaluated according to the acquired data and uploading the data to the consumption scene index calculation module; and the consumption prosperity index calculation module is used for calculating to obtain the consumption prosperity index of the area to be evaluated according to the acquired data.
Claims (6)
1. A consumption prospect index calculation method based on electric power big data comprises the following steps:
s1, acquiring data information of a power system of an area to be evaluated;
s2, establishing a power consumption index system of each industry in the area to be evaluated according to the data information obtained in the step S1;
s3, calculating the power consumption index of each industry in the area to be evaluated according to the power consumption index system of each industry constructed in the step S2;
and S4, calculating to obtain the consumption prospect indexes of the areas to be evaluated according to the electric power consumption indexes of all industries obtained by calculation in the step S3.
2. The consumption landscape index calculation method based on the electric power big data according to claim 1, characterized in that the step S2 establishes an electric power consumption index system of each industry in the area to be evaluated according to the data information obtained in the step S1, specifically, establishes an electric power consumption index set of the electricity sold quantity, an electric power consumption index set of the user number, an average electricity price electric power consumption index set and an electric power consumption index set of the business expansion installation capacity according to the data information obtained in the step S1, and calculates objective weights corresponding to the index sets by using an entropy weight method.
3. The consumption landscape index calculation method based on the big power data as claimed in claim 2, wherein the power consumption index system of each industry in the area to be evaluated is established according to the data information obtained in step S1, and specifically comprises the following steps:
constructing an electricity consumption index set of electricity sales, an electricity consumption index set of user quantity, an average electricity price electricity consumption index set and an industry expansion installation capacity electricity consumption index set; all the index sets have m sample data;
establishing a decision matrix X, wherein the element X in the matrix ij The ith sample data of the jth index;
standardizing the established decision matrix X by adopting the following formula to obtain a standardized decision matrix Y:
for the forward indicator, normalization is performed using the following equation:
in the formula y ij For element X in the normalized decision matrix X ij ；min(x ij ) Is an element X in a decision matrix X ij Minimum value of (d); max (x) ij ) Is an element X in a decision matrix X ij The maximum value of (a); the forward index is defined as an index which is better when the numerical value is larger, and specifically comprises an electricity consumption index set of electricity sales, a user number electricity consumption index set and an industry expansion capacity electricity consumption index set;
for the fitness index, normalization was performed using the following equation:
in the formula y ij For element X in the normalized decision matrix X ij ；z ij Is an element x ij Is normalized by a positive transformation of
E j The average value of j column elements in the decision matrix X is obtained; the moderate index is defined as an index with a numerical value closer to a set value and a better value, and specifically comprises an average electricity price power consumption index set;
and according to the established standardized decision matrix Y, calculating the specific gravity value of each sample in the corresponding index by adopting the following formula:
in the formula, p ij Corresponding to the ith sample under the jth indexThe specific gravity of the index;
according to the specific gravity value of each sample in the corresponding index, calculating the entropy value of each index by adopting the following formula:
in the formula, e j Entropy value of j index;
according to the entropy value of each index, the weighted value of each index is calculated by adopting the following formula:
in the formula
The weight value of the jth index; n is the number of indexes.
4. The consumption landscape index calculation method based on the big power data as claimed in claim 3, wherein the step S3 of calculating the power consumption index of each industry in the area to be evaluated according to the power consumption index system of each industry constructed in the step S2 specifically comprises the following steps:
summarizing electricity selling quantity data, user number data, average electricity price data and business expansion loading capacity data in a base period and a report period;
and according to the summarized data and the power consumption index systems of the industries constructed in the step S2, calculating to obtain the power consumption indexes of the industries by adopting the following formula:
in the formula ECI j Is the power consumption index of the jth industry; s 1 Selling electricity for the reporting period; s 0 Selling electricity for the base period;
is the weight of sold electricity quantity; u. of 1 The number of users in the report period; u. of 0 The number of the users in the base period;
the weight of the user number; v. of 1 Is the average electricity price in the report period; v. of 0 Is the average electricity price of the base period;
is the average electricity price weight; c. C 1 Capacity is expanded for reporting period business; c. C 0 Capacity is expanded for the base-term business;
installing capacity for business expansion;
5. the consumption landscape index calculation method based on the big power data as claimed in claim 4, wherein the step S4 calculates the consumption landscape index of the area to be evaluated according to the power consumption index of each industry calculated in the step S3, and specifically comprises the following steps:
asking the experts to compare every two electric power consumption indexes of all industries obtained in the step S3 to obtain the relative importance between every two electric power consumption indexes: comparing the power consumption indexes of N industries pairwise to obtain a comparison matrix R N×N ：
In the formula r ij For electricity consumption index of ith industry relative to electricity consumption index of jth industryComparing the force consumption indexes;
for the obtained contrast matrix R N×N And (3) carrying out consistency check:
calculating the checking coefficient CR of
CI is a consistency index and
λ R is a contrast matrix R N×N N is the contrast matrix R N×N The order of (a); RI is a random coefficient, and the value rule is as follows:
if N =1, RI =0; RI =0 if N = 2; RI =0.58 if N = 3; if N =4 then RI =0.90; if N =5 then RI =1.12; if N =6 then RI =1.24; if N =7, RI =1.32; if N =8 then RI =1.41; if N =9 then RI =1.45; if N =10, RI =1.49;
the check coefficient CR is determined: if CR is less than the set threshold, then the contrast matrix R is determined N×N The consistency check of (1) is passed; otherwise, identify the contrast matrix R N×N The consistency check of (1) fails;
contrast matrix R N×N After the consistency check of (2) is passed, a contrast matrix R is calculated N×N The eigenvector W corresponding to the maximum eigenvalue of (a) is W = { W = { (W) 1 ,w 2 ,...,w n And normalizing the characteristic vector W to obtain a normalized characteristic vector W' = { W } 1 ',w' 2 ,...,w’ n }；
Calculating to obtain the single-level weight vector of the current level
Is composed of
W 'of' j Is the jth element in the normalized feature vector W';
finally, the consumption prosperity index ECI of the area to be evaluated is obtained through calculation
6. A system for realizing the consumption prospect index calculation method based on the electric power big data as claimed in one of claims 1 to 5 is characterized by comprising a data acquisition module, an electric power consumption index system construction module, an electric power consumption index calculation module and a consumption prospect index calculation module; the data acquisition module, the power consumption index system construction module, the power consumption index calculation module and the consumption landscape index calculation module are sequentially connected in series; the data acquisition module is used for acquiring the data information of the power system of the area to be evaluated and uploading the data to the power consumption index system construction module; the power consumption index system building module is used for building power consumption index systems of all industries in the area to be evaluated according to the obtained data and uploading the data to the power consumption index calculation module; the power consumption index calculation module is used for calculating power consumption indexes of various industries in the area to be evaluated according to the acquired data and uploading the data to the consumption scene index calculation module; and the consumption prosperity index calculation module is used for calculating to obtain the consumption prosperity index of the area to be evaluated according to the acquired data.
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