CN110008981B - Electric-water gas energy-consumption alternative guidable evaluation method based on vector fuzzy matrix model - Google Patents

Abstract

The invention discloses an electric water gas energy alternative guidable evaluation method based on a vector fuzzy matrix model, and belongs to the technical field of electric power. The existing evaluation method cannot fully mine the correlation between the user energy consumption; the analysis effect is poor, and an evaluation scheme for analyzing whether a user can be guided to use electric power to replace fuel gas is lacked, so that the user cannot be guided in a targeted manner, and the guiding success rate is low. According to the invention, the energy consumption condition of each user is analyzed by collecting data of electricity, water and gas and using a big data parallel computing technology, daily electricity consumption, water consumption and gas consumption of the user for one year are utilized, an electric-water-gas fusion data vector is constructed, a fuzzy matrix conversion and evaluation formula is utilized to calculate an evaluation index, and a guidability index of the user group for replacing energy sources is provided for a power supply department, so that the guiding success rate can be effectively improved, and the purposes of improving the working efficiency and reducing the energy source waste are achieved.

Description

A bootable evaluation method for electricity, water and gas energy substitution based on vector fuzzy matrix model

technical field

The invention relates to a bootable evaluation method based on a vector fuzzy matrix model for the replacement of electricity, water and gas, and belongs to the technical field of electric power.

Background technique

With the continuous advancement of remote data collection technology for smart electricity meters, smart water meters, and gas meters for residential users, many places now have multi-meter data joint collection of "electricity, water, gas, and heat" to provide energy consumption for households. The analysis provides the basic conditions. The existing energy consumption analysis methods cannot fully explore the correlation between energy consumption of users; To guide customers in a targeted manner, the success rate of guidance is low.

Contents of the invention

Aiming at the defects of the prior art, the object of the present invention is to provide a method for power supply department by collecting electricity, water and gas data, constructing electricity-water-gas fusion data vector, and using fuzzy matrix conversion and evaluation formula to calculate evaluation index. The bootable index of user groups for energy substitution, and the bootable evaluation method of electricity, water and gas energy substitution based on vector fuzzy matrix model, which can effectively improve the success rate of guidance.

To achieve the above object, the technical solution of the present invention is:

The bootable evaluation method of electricity, water, gas and energy substitution based on the vector fuzzy matrix model includes the following steps:

Step 1. Calculate the daily energy consumption according to the collected water, electricity and gas indicators;

Step 2. Process the user's energy consumption and remove individual abnormal energy consumption data caused by equipment;

Step 3. The three-dimensional energy consumption data composed of electricity consumption, water consumption, and gas consumption is used as the user's daily energy consumption data; and according to the clustering cycle, construct a data vector, and the sequence of splicing requires each user to be consistent;

Step 4. Calculate the correlation cof ₁ between the electricity consumption and water consumption of the user to be evaluated, and the correlation cof ₂ between the electricity consumption and the gas consumption;

Step 5, using the correlation to calculate the weight distribution set;

Step 6. Calculate the fuzzy matrix of user power consumption according to the definition, and each item in the matrix corresponds to the degree of membership of different power consumption types;

Step 7, the method similar to step 6 calculates the user's water consumption fuzzy matrix;

Step 8. Calculate the interval of the peak value of the user's daily 24 o'clock power consumption curve and calculate the membership degree of the user's power consumption type fuzzy matrix corresponding to different power consumption characteristic types;

Step 9, performing fuzzy transformation on the obtained fuzzy set;

Step 10, calculating the grade parameter evaluation result.

The present invention collects electricity, water, and gas data, and combines the multi-table fusion energy consumption behavior guidance analysis method based on the vector fuzzy matrix model designed according to the field operation experience for many years, and uses the big data parallel computing technology to analyze the energy consumption of each user. Analysis, using the user's daily electricity consumption, water consumption, and gas consumption for a year, by constructing the electricity-water-gas fusion data vector, using the fuzzy matrix conversion and evaluation formula to calculate the evaluation index, and providing the power supply department with the energy alternatives of the user group The guideability index can effectively improve the success rate of guidance, achieve the purpose of improving work efficiency and reducing energy waste.

As a preferred technical measure, the step 5: weight distribution set A=[a ₁ , a ₂ , a ₃ , a ₄ ]; satisfy a ₄ =1.3a ₁ , a ₂ =cof ₁ *a ₁ , a ₃ =cof ₂ *a ₁ , and a ₁ +a ₂ +a ₃ +a ₄ =1, a ₁ , a ₂ , a ₃ , a ₄ correspond to different types of weight coefficients; weight a ₁ , a ₂ , a ₃ , a ₄ What is measured is the contribution weight of each factor to the score; where a ₁ represents the weight of electricity consumption, a ₂ represents the weight of water consumption, a ₃ represents the weight of gas consumption, and a ₄ represents the weight of electricity consumption type.

As a preferred technical measure, the step 6: the user power consumption fuzzy matrix R ₁ =[r ₁₁ , r ₁₂ , r ₁₃ , r ₁₄ ]; r ₁₁ , r ₁₂ , r ₁₃ , r ₁₄ respectively correspond to different power consumption type of membership.

As an optimal technical measure, high power consumption is defined as above 400kWh per month, general power consumption is 200-400kWh per month, low power consumption is 100-200kWh per month, and extremely low power consumption is below 100kWh per month;

As a preferred technical measure, the step 7: user water consumption fuzzy matrix R _z = [r ₂₁ , r ₂₂ , r ₂₃ , r ₂₄ ], r ₂₁ , r ₂₂ , r ₂₃ , r ₂₄ correspond to different types of water consumption Membership.

As a preferred technical measure, the step 8: if it is in the peak period, then define the user’s electricity consumption type on that day as the peak consumption type, and the same peak period, flat section, and valley section respectively correspond to a small amount of atypical peak Electricity type.

As an optimal technical measure, the fuzzy matrix of user electricity consumption type R ₃ =[r ₃₁ , r ₃₂ , r ₃₃ , r ₃₄ ], where r ₃₁ , r ₃₂ , r ₃₃ , and r ₃₄ respectively correspond to the membership degrees of different electricity consumption characteristic types;

As a preferred technical measure, said step 9: performing fuzzy transformation on the obtained fuzzy set "*" stands for fuzzy operator.

As an optimal technical measure, the fuzzy operators used are as follows:

M(∧,∨) operator

M(·,∨) operator

operator

operator

The a _j corresponds to the jth element of the left vector of the operator.

As a preferred technical measure, the step 10: Calculation of grade parameter evaluation results: B×F=p; p is the last guiding potential parameter of the user; the magnitudes of quantified grades at all levels are: F={4,3,2,1 } ^T , the value in brackets is the quantification of the user guidance score, which is divided into four levels, the highest level is 4, and the lowest level is 1. If a user has a higher guiding potential, the higher the weight of its high level will be, and the higher the final score will be.

Compared with the prior art, the present invention has the following beneficial effects:

The present invention collects electricity, water, and gas data, and combines the multi-table fusion energy consumption behavior guidance analysis method based on the vector fuzzy matrix model designed according to the field operation experience for many years, and uses the big data parallel computing technology to analyze the energy consumption of each user. Analysis, using the user's daily electricity consumption, water consumption, and gas consumption for a year, by constructing the electricity-water-gas fusion data vector, using the fuzzy matrix conversion and evaluation formula to calculate the evaluation index, and providing the power supply department with the energy alternatives of the user group The guideability index can effectively improve the success rate of guidance, achieve the purpose of improving work efficiency and reducing energy waste.

Description of drawings

Fig. 1 is a flowchart of steps of the present invention.

Detailed ways

In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

On the contrary, the invention covers any alternatives, modifications, equivalent methods and schemes within the spirit and scope of the invention as defined by the claims. Further, in order to make the public have a better understanding of the present invention, some specific details are described in detail in the detailed description of the present invention below. The present invention can be fully understood by those skilled in the art without the description of these detailed parts.

As shown in Figure 1, the bootable evaluation method based on the vector fuzzy matrix model for electricity, water, gas and energy substitution refers to the historical experience of long-term operation of the collection system, and specifically includes the following steps:

Step 1: Calculate the daily energy consumption based on the collected water, electricity and gas indicators.

Step 2: Process the user's energy consumption and remove individual abnormal energy consumption data caused by equipment.

Step 3: Take the three-dimensional energy consumption data composed of electricity consumption, water consumption and gas consumption as the user's daily energy consumption data. And according to the clustering period, the data vector is constructed, and the order of splicing requires each user to be consistent.

Step 4: Calculate the correlation cof ₁ between electricity consumption and water consumption of the user to be evaluated, and the correlation cof ₂ between electricity consumption and gas consumption.

Said step 5: use correlation to calculate weight distribution set A=[a ₁ , a ₂ , a ₃ , a ₄ ]; satisfy a ₄ =1.3a ₁ , a ₂ =cof ₁ *a ₁ , a ₃ =cof ₂ *a ₁ , and a ₁ +a ₂ +a ₃ +a ₄ =1, a ₁ , a ₂ , a ₃ , a ₄ correspond to different types of weight coefficients; weights a ₁ , a ₂ , a ₃ , a ₄ measure where a ₁ represents the weight of electricity consumption, a ₂ represents the weight of water consumption, a ₃ represents the weight of gas consumption, and a ₄ represents the weight of electricity consumption type.

Step 6: Calculate the fuzzy matrix R ₁ =[r ₁₁ , r ₁₂ , r ₁₃ , r ₁₄ ] according to the definition. r ₁₁ , r ₁₂ , r ₁₃ , and r ₁₄ respectively correspond to the membership degrees of different power consumption types. Define high power consumption as above 400kWh per month, general power consumption as 200-400kWh per month, low power consumption as 100-200kWh per month, and extremely low power consumption below 100kWh per month.

Step 7: Calculate the user water consumption fuzzy matrix R ₂ =[r ₂₁ , r ₂₂ , r ₂₃ , r ₂₄ ] similarly to step 6.

Step 8: Calculate the interval of the peak value of the user's power consumption curve at 24 o'clock every day. If it is in the peak period, define the type of power consumption of the user on that day as the peak power consumption type. The same peak period, flat section, and valley section are respectively Corresponding to a small number of atypical peak power consumption types. Calculate the fuzzy matrix R ₃ =[r ₃₁ , r ₃₂ , r ₃₃ , r ₃₄ ] of user electricity consumption type, where r ₃₁ , r ₃₂ , r ₃₃ , and r ₃₄ respectively correspond to the membership degrees of different electricity consumption characteristic types.

Step 9: Perform fuzzy transformation on the obtained fuzzy set "*" represents the fuzzy operator, and the fuzzy operator used is as follows:

M(∧,∨) operator

M(·,∨) operator

operator

operator

The a _j corresponds to the jth element of the left vector of the operator.

Step 10: Calculate the grade parameter evaluation result, B×F=p. p is the last guiding potential parameter of the user. In this paper, the magnitude of each level of quantification is: F={4, 3, 2, 1} ^T , the value in brackets is the quantification of user guidance rating, which is divided into four levels, the highest level is 4, and the lowest level is 1 . If a user has a higher guiding potential, the higher the weight of its high level will be, and the higher the final score will be.

Table 1 Evaluation index system of residential user electricity strategy guidance potential

Table 2 Division of Peak and Valley Power Consumption in Regions

The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present invention should be included in the protection of the present invention. within range.

Claims (5)

1. The electric water gas energy alternative guidable evaluation method based on the vector fuzzy matrix model is characterized by comprising the following steps of:

step 1, calculating daily energy according to collected water, electricity and gas indication;

step 2, processing energy for users, and removing abnormal energy data caused by equipment;

step 3, forming three-dimensional energy consumption data by using the electricity consumption, the water consumption and the air consumption as daily energy consumption data of the user; constructing a data vector according to the clustering period, wherein the splicing sequence requires each user to be consistent;

step 4, calculating the correlation cof between the electricity consumption and the water consumption of the user to be evaluated ₁ Correlation cof between electricity consumption and gas consumption ₂ ；

And 5, calculating a weight distribution set by using the correlation, wherein the expression is as follows:

A＝[a ₁ ,a ₂ ,a ₃ ,a ₄ ]；

satisfy a ₄ ＝1.3a ₁ ,a ₂ ＝cof ₁ *a ₁ ，a ₃ ＝cof ₂ *a ₁ And a ₁ +a ₂ +a ₃ +a ₄ ＝1，a ₁ ,a ₂ ,a ₃ ,a ₄ Corresponding to different types of weight coefficients;

weight a ₁ ,a ₂ ,a ₃ ,a ₄ The contribution weight of each factor to the score is measured; wherein a is ₁ A represents the weight of the electricity consumption, a ₂ Weight representing water consumption, a ₃ Weight representing gas consumption, a ₄ A weight representing a type of electricity usage;

step 6, calculating a fuzzy matrix R of the user power consumption according to definition ₁ ＝[r ₁₁ ,r ₁₂ ,r ₁₃ ,r ₁₄ ]；r ₁₁ 、r ₁₂ 、r ₁₃ 、r ₁₄ Membership degrees corresponding to different electricity consumption types respectively;

defining high electricity consumption to be more than 400kWh per month, and generally 200-400kWh per month, low electricity consumption to be 100-200kWh per month, and extremely low electricity consumption to be less than 100kWh per month;

step 7, calculating a fuzzy matrix of the water consumption and the air consumption of the user by a method similar to the step 6;

step 8, counting the intervals of the peak value of the 24-point power consumption curve of the user every day, and calculating membership degrees of fuzzy matrixes of the power consumption types of the user corresponding to different power consumption characteristic types respectively;

and 9, performing fuzzy transformation on the obtained fuzzy matrix, wherein the calculation formula is as follows:

". Times" represents blurring operator;

step 10, calculating a grade parameter evaluation result b×f=p;

p is the guidance potential parameter of the last user; the quantized level magnitudes at each level are: f= {4,3,2,1} ^T The values in brackets are the quantification of the user guidance scores, divided into four classes, with the highest class being 4 and the lowest class being 1.

2. The method for evaluating the electric water gas energy alternative guidability based on a vector blur matrix model as recited in claim 1, wherein said step 7: fuzzy matrix R of user water consumption ₂ ＝[r ₂₁ ,r ₃₂ ,r ₂₃ ,r ₂₄ ]，r ₂₁ 、r ₂₂ 、r ₂₃ 、r ₂₄ Respectively correspond to membership degrees of different water consumption types.

3. The method for evaluating the electric water gas energy alternative guidability based on the vector blur matrix model as claimed in claim 2, wherein said step 8: if the peak period is in the peak period, defining the electricity type of the user on the day as the peak electricity type, wherein the same peak period, flat section and valley section respectively correspond to a part of atypical peak electricity type.

4. The method for the guidable evaluation of electric water gas energy consumption based on a vector blur matrix model of claim 3, wherein the user uses an electric type blur matrix R ₄ ＝[r ₄₁ ,r ₄₂ ,r ₄₃ ,r ₄₄ ]，r ₄₁ 、r ₄₂ 、r ₄₃ 、r ₄₄ Membership degrees corresponding to different electricity utilization characteristic types respectively;

5. the method for the guidable evaluation of the electric water gas energy based on the vector blur matrix model according to claim 1, wherein the blur operators are utilized as follows:

m ([ lambda ], V-shaped) operator

M (.V., V.) operator

Operator

Operator

The a is that _j Corresponding to the j-th element of the operator left vector.