CN118115004A - Method for evaluating power utilization level based on power big data - Google Patents
Method for evaluating power utilization level based on power big data Download PDFInfo
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Abstract
The invention provides a method for evaluating power consumption level based on power big data, which comprises the following steps of S1, acquiring an initial evaluation value, acquiring the power big data in a period of time of a corresponding area, and calculating a power consumption data index; and S2, performing step S2. Calculating the variable weights of all indexes, and carrying out variable weights on the basic weights of all indexes by combining the economic development of different areas and the types of the areas; and S3, evaluating the power utilization level by adopting a variable weight approximation ideal solution sequencing method. By the method, reasonable evaluation of the electricity utilization level can be realized.
Description
Technical Field
The invention relates to an evaluation method in the power industry, in particular to a method for evaluating the power consumption level by utilizing power big data.
Background
Along with the development of social economy and the improvement of the living standard of people, the electric power demand is continuously increased. How to accurately and effectively evaluate the power consumption level and realize the balance of power supply and demand has become an important subject in the power industry. The traditional power consumption level evaluation method is mainly based on historical power consumption data and manual experience, and has the problems of low efficiency and insufficient accuracy when processing big data. In addition, the traditional evaluation method aims at the factors such as development conditions, region types and the like in different regions, and the evaluation result is unreasonable. Therefore, the development of the power consumption level assessment method based on the power big data is helpful for solving the problems in the prior art.
Disclosure of Invention
The invention aims to solve the problems in the prior art and provides a method for evaluating the power utilization level based on big power data.
In order to achieve the above object, the present invention provides a method for performing power consumption level assessment based on power big data, the method comprising the steps of:
step S1: acquiring an initial evaluation value, acquiring power big data in a period of time of a corresponding area, and calculating a power utilization data index;
Step S2: calculating the variable weights of all indexes, and carrying out variable weights on the basic weights of all indexes by combining the economic development of different areas and the types of the areas;
step S3: and (5) evaluating the power utilization level by adopting a variable weight approximation ideal solution sequencing method.
Preferably, the number of the areas to be evaluated is M, large electric power data in a period of time of the corresponding areas are collected, and 9 indexes including accumulated electric power consumption I1, electric wave dynamic conditions I2, electric power change regularity I3, maximum electric power load I4, minimum electric power load I5, electric power quality I6, average fault time I7, average fault repair time I8 and line loss ratio I9 are respectively calculated; then get the initial evaluation value data
Wherein I ij represents an index value of the I-th region index Ij to be evaluated.
Preferably, assuming that the initial weights of the indices I1-I9 are (w 1,w2,…,w9), the weight change is performed as follows,
Step S2.1: firstly, constructing a variable weight vector, combining with dynamic factors for each region, wherein the type R 1 of the power consumption region, the development guide R 2, the population number R 3, the population density R 4, the average population GDP R 5 and the economic growth rate R 6 of the power consumption region, and the normalized value R of the influence factors is as follows for M regions
Then the weight vector S (X i)＝(xi1,xi2,…,xi9), where X ij＝gj(ri1,ri2,ri3,ri4,ri5,ri6), j=1, 2, …,9;
The function g j () represents the influence relation of influence factors on index weights, and is excitation type or penalty type variable weight according to the action type;
Step 2.2 calculating the variable weight of each index
Where W i′＝(w′i1,w′i2,…,w′i9) is the variable weight of each evaluation index of the i-th region, i=1, 2, …, M.
Preferably, in step S3.1, a normalized decision matrix is obtained by vector normalization, and a normalized evaluation matrix z= (Z ij)m×n) is calculated from the initial evaluation value data= (I ij)M×9
Preferably, in step S3.2, a weighted canonical matrix y= (Y ij)M×9, the variable weights of the various indexes are W i′＝(w′i1,w′i2,…,w′i9) is formed, and then Y ij in the weighted canonical matrix is calculated as follows
yij＝w′ij·zij,i＝1,2,…,M；j＝1,2,…,9。
Preferably, in step S3.3, the ideal solution y + and the negative ideal solution y - are determined, and the j-th attribute value of the ideal solution y + is set asThe j-th attribute value of the negative ideal solution y - is/>Then
Wherein i=1, 2, …, M; j=1, 2, …,9.
Preferably, in step S3.4, the distances from each scheme to the ideal solution and the negative ideal solution are calculated, and the distances from the weighted standard value y i＝(yi1,yi2,…,yi9) of each index evaluation value of the power consumption level of the region i) to the ideal solution and the negative ideal solution are
Where i=1, 2, …, M.
Preferably, in step S3.5, a comprehensive evaluation index P i of the power consumption level of each region is calculated,
In another aspect, the present invention provides a storage medium, where the storage medium stores a method program for performing power consumption level estimation on big power data, where the method program for performing power consumption level estimation on big power data, when executed by a processor, implements a calculation flow of a method for performing power consumption level estimation on big power data.
In another aspect, the invention provides a power consumption level evaluation system based on power big data, which is characterized in that the system is used for executing a method for evaluating the power consumption level of the power big data.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention fully utilizes the power big data information and provides indexes such as power consumption, power consumption regularity, fault interval time and the like, and the power consumption condition of each relevant area is measured perfectly.
2. In order to improve the reasonability of the power utilization level evaluation of each area, a weight determination mode based on a weight-changing algorithm is provided, and the power utilization level can be reasonably evaluated for areas with different types and different development conditions.
3. In the evaluation process, a TOPSIS algorithm is adopted, and a reasonable result can be obtained by utilizing the distance relation between the ideal value and the negative ideal value according to the characteristics of evaluation data of different areas.
Drawings
Fig. 1 is a flow chart of a method for evaluating power consumption level based on power big data according to a first embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the attached drawing figures:
For a better understanding of the present invention, embodiments of the present invention are explained in detail below with reference to the drawings.
The embodiment of the invention discloses a method for evaluating the power utilization level based on power big data, wherein a schematic diagram of the method is shown in fig. 1, and the method specifically comprises the following steps:
In order to effectively evaluate the electricity consumption level of each area, nine evaluation indexes are provided for evaluation, wherein the evaluation indexes are respectively as follows: the power consumption I1, the power consumption situation I2, the power consumption change regularity I3, the maximum power consumption load I4, the minimum power consumption load I5, the power consumption quality I6, the average fault time I7, the average fault repair time I8 and the line loss ratio I9 are integrated.
The meaning and calculation mode of each index are as follows:
the accumulated electricity consumption I1 is the total electricity consumption of the area to be evaluated for a period of time, and represents the total electricity consumption level. The index can be directly obtained from electricity consumption big data.
The electricity consumption wave dynamic condition I2 is the electricity consumption fluctuation condition of the region to be evaluated in a period of time, and is mainly the change condition of the electricity consumption power. Mainly from the maximum rate of change of the electric power.
Setting the electric power in a period of time to acquire data according to time as P (T 1)、P(T2)、…、P(TN); with electric wave movement
The electricity consumption change regularity I3 is the regularity of electricity consumption change in a period of time of the region to be evaluated, and represents the rule change condition of electricity consumption along with corresponding factors. The regular change of the electricity consumption power along with time can be used for representing, the change period is set to be T s, and the electricity consumption change condition in 2 periods is acquired.
The maximum electricity load I4 is the maximum electricity load in a period of time in the area to be evaluated. The index can be directly obtained from electricity consumption big data.
The minimum electric load I5 is the minimum electric load in a period of time in the area to be evaluated. The index can be directly obtained from electricity consumption big data.
The electrical quality I6 is mainly represented by a weighted combination of voltage and frequency stability and harmonic voltage distortion.
I6＝a1V+a2F+a3U
Wherein V is voltage stability, F is frequency stability, U is harmonic voltage stability, and the voltage stability can be obtained by analyzing electric power big data. a1, a2, a3 are weights of the respective classifications, a 1+a2+a3 =1.
The average fault time I7 is expressed as the average time of power faults in the area to be evaluated. Let the time length of the evaluation region selection be T f-T0, where T 0 is the data acquisition start time and T f is the termination time. The number of faults occurring in the period is Q, then the index
The average fault repair time I8 is represented by the average power fault repair time of the region to be evaluated and can be obtained by statistical calculation of power big data.
Line loss ratio I9, the ratio of line loss to the total power usage at the input of the area. The index can be obtained by analyzing and calculating the power big data.
In particular, the invention provides a power consumption level evaluation method based on a variable weight TOPSIS (approach to ideal solution ordering method).
Step S1: an initial evaluation value is acquired. Setting M areas to be evaluated, collecting power big data in a period of time of the corresponding areas, and respectively calculating: the accumulated electricity consumption I1, the electricity consumption situation I2, the electricity consumption change regularity I3, the maximum electricity consumption load I4, the minimum electricity consumption load I5, the electricity consumption quality I6, the average fault time I7, the average fault repair time I8 and the line loss ratio I9 are 9 indexes. The initial evaluation value data can be obtained:
Wherein I ij represents an index value of the I-th region index Ij to be evaluated.
Step S2: and calculating the variable weight of each index. Because the economic development, the region type and the like of each region are different, the weight change is required to be carried out on the basic weight of each index in order to more reasonably reflect the actual power consumption level of each region. Assuming that the initial weights of the indices I1 to I9 are (w 1,w2,…,w9), the weight change is performed as follows.
Step S2.1 first constructing a variable weight vector
Consider the area-wise electrical influence factor: the electricity utilization region type R 1, the development direction R 2, the population number R 3, the population density R 4, the average population GDP R 5 and the economic growth rate R 6, and the normalization value R of the influence factors is as follows for M regions
Then the weight vector S (X i)＝(xi1,xi2,…,xi9)
Where x ij＝gj(ri1,ri2,ri3,ri4,ri5,ri6), j=1, 2, …,9
The function g j () represents the influence relation of influence factors on index weights, and can be divided into excitation type and penalty type weights according to the action type. Those skilled in the art can determine the specific form of the function based on the evaluation emphasis and the application object.
Step S2.2 calculating the variable weight of each index
Where W i′＝(w′i1,w′i2,…,w′i9) is the variable weight of each evaluation index of the i-th region, i=1, 2, …, M.
Step S3 adopts a variable weight TOPSIS (approach to ideal solution ordering) to evaluate.
After the final variable weight is obtained, the power consumption level of the target is estimated by adopting a TOPSIS method, and the specific algorithm flow is as follows:
Step S3.1, obtaining a normalized decision matrix by using a vector normalization method.
From the initial evaluation value data= (I ij)M×9, calculate normalized evaluation matrix z= (Z ij)m×n, then
Step S3.2 constitutes a weighted canonical matrix y= (Y ij)M×9.
The variable weights of the various indicators are W i′＝(w′i1,w′i2,…,w′i9), then y ij in the weighted canonical matrix is calculated as follows:
yij＝w′ij·zij,i＝1,2,…,M；j＝1,2,…,9
Step S3.3 determines an ideal solution y + and a negative ideal solution y -.
Let the j-th attribute value of the ideal solution y + beThe j-th attribute value of the negative ideal solution y - is/>Then
Wherein i=1, 2, …, M; j=1, 2, …,9.
Step S3.4 calculates the distance from each solution to the ideal solution and the negative ideal solution.
The distance from the weighted standard value y i＝(yi1,yi2,…,yi9) of each index evaluation value of the electricity consumption level of the region i) to the ideal solution and the negative ideal solution is
Where i=1, 2, …, M.
Step S3.5 calculates a comprehensive evaluation index P i of the electricity consumption level of each region.
The method provided by the invention can fully utilize the power big data information and can perfectly measure the power consumption condition of each relevant area.
In the description of the present invention, it should be noted that, unless explicitly specified and limited otherwise, the terms "connected," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
In the description of the present invention, unless otherwise indicated, the terms "upper," "lower," "left," "right," "inner," "outer," and the like are used for convenience in describing the present invention and simplifying the description based on the orientation or positional relationship shown in the drawings, and do not denote or imply that the devices or elements in question must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention.
Finally, it should be noted that the above-mentioned technical solution is only one embodiment of the present invention, and various modifications and variations can be easily made by those skilled in the art based on the application methods and principles disclosed in the present invention, and are not limited to the methods described in the above-mentioned specific embodiments of the present invention, therefore, the foregoing description is only preferred, and not meant to be limiting.
Claims (10)
1. A method for power usage level assessment based on power big data, the method comprising the steps of:
step S1: acquiring an initial evaluation value, acquiring power big data in a period of time of a corresponding area, and calculating a power utilization data index;
Step S2: calculating the variable weights of all indexes, and carrying out variable weights on the basic weights of all indexes by combining the economic development of different areas and the types of the areas;
step S3: and (5) evaluating the power utilization level by adopting a variable weight approximation ideal solution sequencing method.
2. The method according to claim 1, wherein said step S1 specifically comprises:
Setting M areas to be evaluated, collecting power big data of the corresponding areas within a period of time, and respectively calculating 9 indexes of accumulated power consumption I1, power consumption situation I2, power consumption change regularity I3, maximum power consumption load I4, minimum power consumption load I5, power consumption quality I6, average fault time I7, average fault repair time I8 and line loss ratio I9; then get the initial evaluation value data
Wherein I ij represents an index value of the I-th region index Ij to be evaluated.
3. The method according to claim 2, wherein said step S2 comprises:
Assuming that the initial weights of the indexes I1-I9 are (w 1,w2,…,w9), the weight change is performed as follows,
Step S2.1: firstly, constructing a variable weight vector, combining with dynamic factors for each region, wherein the type R 1 of the power consumption region, the development guide R 2, the population number R 3, the population density R 4, the average population GDPr 5 and the economic growth rate R 6 of the power consumption region, and the normalized value R of the influence factors is as follows for M regions
Then the weight vector S (X i)＝(xi1,xi2,…,xi9), where X ij＝gj(ri1,ri2,ri3,ri4,ri5,ri6), j=1, 2, …,9;
The function g j () represents the influence relation of influence factors on index weights, and is excitation type or penalty type variable weight according to the action type;
Step 2.2 calculating the variable weight of each index
Where W i′＝(w′i1,w′i2,…,w′i9) is the variable weight of each evaluation index of the i-th region, i=1, 2, …, M.
4. A method according to claim 3, characterized in that said step S3 comprises in particular:
Step S3.1, obtaining a normalized decision matrix by using a vector normalization method, and calculating a normalized evaluation matrix Z= (Z ij)m×n) from the initial evaluation value data= (I ij)M×9, then
5. The method according to claim 4, wherein the step S3 specifically includes:
step S3.2, forming a weighted canonical matrix Y= (Y ij)M×9, variable weights of each index are W i′＝(w′i1,w′i2,…,w′i9), then Y ij in the weighted canonical matrix is calculated as follows
yij＝w′ij·zij,i＝1,2,…,M；j＝1,2,…,9。
6. The method according to claim 5, wherein the step S3 specifically includes:
Step S3.3, determining ideal solution y + and negative ideal solution y -, and setting the j-th attribute value of ideal solution y + as The j-th attribute value of the negative ideal solution y - is/>Then
Wherein i=1, 2, …, M; j=1, 2, …,9.
7. The method according to claim 6, wherein the step S3 specifically includes:
Step S3.4, calculating the distance from each scheme to the ideal solution and the negative ideal solution, wherein the distance from the weighted standard value y i＝(yi1,yi2,…,yi9) of each index evaluation value of the power consumption level of the area i) to the ideal solution and the negative ideal solution is
Where i=1, 2, …, M.
8. The method according to claim 7, wherein the step S3 specifically includes:
Step S3.5, calculating a comprehensive evaluation index P i of the power consumption level of each area,
9. A storage medium, wherein a method program for performing power consumption level estimation of power consumption big data is stored on the storage medium, and when the method program for performing power consumption level estimation of power consumption big data is executed by a processor, the calculation flow of the method for performing power consumption level estimation of power consumption big data according to any one of claims 1 to 8 is implemented.
10. A power usage level assessment system based on power big data, characterized in that the system is adapted to perform a method of power usage level assessment of power big data according to any of claims 1-8.
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