Electric power prosperity index construction method based on electric quantity and business expansion data

Abstract

The invention relates to an electric power prosperity index construction method based on electric quantity and business expansion data, and belongs to the technical field of data processing and analysis. The method is divided into two stages. In the first stage, a Pearson's correlation coefficient analysis method is applied to perform correlation analysis on power data and GDP, and through correlation coefficients, power consumption, business expansion households and business expansion are screened out to serve as key power parameters. And in the second stage, according to the thought of procurement manager index construction, a classification index and weight determination method is used, so that the construction of the electric power prosperity index is more reasonable and effective. And finally, obtaining an electric power prosperity index by using a calculation principle of a comprehensive index, namely a weighted synthesis index method, and evaluating economy. According to the method, the economic condition at the present stage can be evaluated more accurately through the characteristics of real-time performance, accuracy, comprehensiveness and the like of the electric power data.

Description

Technical Field

[0001] This invention relates to the field of data processing and analysis, specifically to a system for analyzing economic activity driven by electricity data for administrative, commercial, financial, management, and forecasting purposes. Specifically, a method for constructing an electricity prosperity index based on electricity consumption and industry expansion data is proposed. Background Art

[0002] In the context of global economic integration, electricity data, as a key indicator of economic activity, plays a crucial role in economic assessment and decision-making. Current economic assessment methods primarily rely on macroeconomic indicators such as GDP, unemployment rate, and inflation. While these indicators provide a snapshot of the macroeconomic landscape, their long update cycles make them difficult to accurately and timely meet the demands.

[0003] In recent years, with the development of power information technology, the collection and processing of power data has been significantly improved. Using power data to assess economic performance is becoming a new research approach. Power data, with its real-time, accurate, and comprehensive nature, can provide a more precise assessment of current economic conditions. Summary of the Invention

[0004] Current power data analysis methods fail to fully capture the underlying economic information. This paper proposes a method for constructing an Electricity Prosperity Index (EPI) based on electricity consumption and business expansion data. By deeply mining these data, an Electricity Prosperity Index (EPI) is constructed to assess the economy, supporting decision-making and economic trend analysis.

[0005] The method proposed in the present invention is mainly divided into two stages:

[0006] In the first stage, the Pearson correlation coefficient analysis method was used to conduct a correlation analysis on electricity data and GDP. Through the correlation coefficient, electricity consumption, number of business expansion households, and business expansion capacity were screened out as key electricity parameters.

[0007] In the second phase, to make the construction of the Power Prosperity Index more reasonable and effective, we used classification indices and determined weights based on the principles of the Purchasing Managers' Index (PMI). Finally, we applied the weighted composite index method, a comprehensive index calculation principle, to derive the Power Prosperity Index and evaluate the economy. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 This is a flow chart of a method for constructing an electricity prosperity index based on electricity quantity and business expansion data of the present invention. DETAILED DESCRIPTION

[0009] The specific implementation is as follows:

[0010] First, the Pearson correlation coefficient method is used to analyze the correlation between GDP and electricity data, and the electricity parameters are screened out through the correlation coefficient.

[0011]

[0012] Where: cov is the covariance; σ is the standard deviation; r is the correlation coefficient.

[0013] Secondly, the classification index is calculated using the diffusion index method. The classification index formula is:

[0014] DI = the percentage of the "growth" option × 1 + the percentage of the "remaining the same" option × 0.5

[0015] In the formula: DI is the classification index; "growth" means that the target data is greater than the previous month's data multiplied by (1+10%); "flat" means that the target data is between the previous month's data multiplied by (1-10%) and the previous month's data multiplied by (1+10%).

[0016] The next step is to determine the weights. Correlation analysis is performed on each pair of power parameters to determine the correlation coefficient, path coefficient, and the corresponding weights are calculated based on the relative contribution theory. The formula is:

[0017] |P GE |=|r GE -P GH ·r EH -P GC ·r EC |

[0018] |P GE |=|r GE -P GH ·r EH -P GC ·r HC |

[0019] |P GC |=|r GC -P GE ·r EC -P GH ·r HC |

[0020] |P GE |+|P GH |+|P GC |=W

[0021]

[0022] Where: G represents GDP; E represents electricity consumption; H represents the number of households for business expansion; C represents the capacity for business expansion; P GE , P GH , P GC are the path coefficients between GDP and electricity consumption, number of households expanded, and capacity expanded; r GE , r GH , r GC , r EH , r EC , r HC are the correlation coefficients between GDP and electricity consumption, GDP and the number of households with business expansion, GDP and business expansion capacity, electricity consumption and the number of households with business expansion, electricity consumption and business expansion capacity, and the number of households with business expansion and business expansion capacity, respectively; W, W E They represent the sum of the path coefficients and the weight of electricity consumption respectively.

[0023] Finally, the power prosperity index is constructed, and the current economic situation is judged by the power prosperity index. The formula of the power prosperity index is:

[0024] EPI=W E DI E +W H DI H +W C DI C

[0025] Where: W E , W H , W C The weights of GDP, electricity consumption, number of households expanding, and capacity expanding are calculated through path analysis and relative contribution; DI E , DI H , DI C They are the classification indexes of electricity consumption, number of business expansion households, and business expansion capacity.

[0026] When EPI>50%, from the perspective of electricity, the economic growth rate is in an upward state; when EPI=50%, from the perspective of electricity, the economic growth rate is in a flat state; when BPI<50%, from the perspective of electricity, the economic growth rate is in a slowing state.

[0027] In order to make the description easier to understand, the specific technical solutions of the present invention will be described in detail below with reference to specific examples:

[0028] First, the Pearson correlation coefficient method is used to perform correlation analysis, and the power parameters are screened out through the correlation coefficient.

[0029] Table 1 Correlation coefficient results GDP-Electricity Consumption GDP-number of business expansions GDP-Business Expansion Capacity Correlation coefficient 0.95 0.94 0.84 The experimental results show that GDP is strongly correlated with electricity consumption, the number of business expansion households and the business expansion capacity, so electricity consumption, the number of business expansion households and the business expansion capacity are selected as power parameters. Secondly, the classification index is determined using the diffusion index calculation method. Table 2 Classification index results month Electricity consumption Number of business expansions Business expansion capacity 2024.01 0.48 0.46 0.42 2024.02 0.71 0.32 0.41 2024.03 0.73 0.61 0.54 2024.04 0.19 0.87 0.75 2024.05 0.54 0.61 0.53 2024.06 0.48 0.47 0.53 The next step is to determine the weights, including the correlation coefficients and path coefficients between the power parameters and the corresponding weights calculated based on the relative contribution theory. Table 3 Correlation coefficient results between power parameters Table 4 Path coefficient results GDP-Electricity Consumption GDP-number of business expansions GDP-Business Expansion Capacity Path coefficient 1.342 1.246 1.341 Table 5 Results of power parameter weights Electricity consumption Number of business expansions Business expansion capacity Weight 0.34 0.32 0.34 Finally, construct the Power Prosperity Index and assess the economy Table 6 Electricity Prosperity Index Results month Power Prosperity Index 2024.01 0.45 2024.02 0.48 2024.03 0.63 2024.04 0.60 2024.05 0.56 2024.06 0.49 The Power Prosperity Index indicates that economic growth in the second quarter increased compared to the first quarter, consistent with changes in GDP. Compared to existing macroeconomic indicators, which have longer update cycles, the Power Prosperity Index provides a more accurate assessment of current economic conditions. The above embodiments are only preferred embodiments for fully illustrating the present invention, and the protection scope of the present invention is not limited thereto. Any equivalent substitution or exchange made by those skilled in the art based on the present invention is within the protection scope of the present invention.

Claims

1. A method for constructing a power prosperity index based on power consumption and business expansion data, characterized in that: The method includes the following operations: in the first stage, using the Pearson correlation coefficient analysis method to conduct a correlation analysis on electricity data and GDP, and through the correlation coefficient, screening out electricity consumption, the number of business expansion households, and the business expansion capacity as key electricity parameters; in the second stage, based on the concept of constructing the Purchasing Managers' Index (PMI), using the classification index and weight determination method to make the construction of the Electricity Prosperity Index (EPI) indicator more reasonable and effective; finally, using the calculation principle of the comprehensive index, namely the weighted composite index method, to obtain the Electricity Prosperity Index and evaluate the economy.

2. The method for constructing a power prosperity index based on power consumption and business expansion data according to claim 1, characterized in that: The method includes correlation analysis: using the Pearson correlation coefficient method to perform correlation analysis, and screening out power parameters through the correlation coefficient. The formula is as follows: Where: cov is the covariance; σ is the standard deviation; r is the correlation coefficient.

3. The method for constructing a power prosperity index based on power consumption and business expansion data according to claim 1, characterized in that: The method also includes calculating the classification index and determining the weight: first, the classification index is calculated using the diffusion index method, and the formula is as follows: DI = the percentage of the "growth" option × 1 + the percentage of the "remaining the same" option × 0.5 Where: DI is the classification index, "increase" means the target data is greater than the previous month's data multiplied by (1 + 10%); "remain unchanged" means the target data is between the previous month's data multiplied by (1 + 10%) and the previous month's data multiplied by (1 + 10%); The second step is to determine the weights. Correlation analysis is performed on each pair of power parameters to determine the correlation coefficient, path coefficient, and the corresponding weights are calculated based on the relative contribution theory. The formula is as follows: |P GE |=|r GE -P GH ·r EH -P GC ·r EC | |P GH |=|r GH -P GE ·r EH -P GC ·r HC | |P GC |=|r GC -P GE ·r EC -P GH ·r HC | |P GE |+|P GH |+|P GC |=W Where: G represents GDP; E represents electricity consumption; H represents the number of households for business expansion; C represents the capacity for business expansion; P GE , P GH , P GC are the path coefficients between GDP and electricity consumption, number of households expanded, and capacity expanded; r GE , r GH , r GC , r EH , r EC , r HC are the correlation coefficients between GDP and electricity consumption, GDP and the number of households with business expansion, GDP and business expansion capacity, electricity consumption and the number of households with business expansion, electricity consumption and business expansion capacity, and the number of households with business expansion and business expansion capacity, respectively; W, W E They represent the sum of the path coefficients and the weight of electricity consumption respectively.

4. The method for constructing a power prosperity index based on power consumption and business expansion data according to claim 1, characterized in that: The method also includes the construction of a power prosperity index; the formula is as follows: EPI=W E ·IN E +W H ·IN H +W C ·IN C Where: W E , W H , W C The weights of GDP, electricity consumption, number of households expanding, and capacity expanding are calculated through path analysis and relative contribution; DI E , DI H , DI C They are the classification indices of electricity consumption, number of households with business expansion, and business expansion capacity; When EPI>50%, from the perspective of electricity, the economic growth rate is in an upward state; When EPI = 50%, from the perspective of electricity, the economic growth rate is observed to be flat; When EPI is less than 50%, from the perspective of electricity, the economic growth rate is in a state of slowing down.

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