Abstract
Under the background of the slowdown in macroeconomic growth and the gradual liberalization of the power system reforming market, the competition pressure of power grid companies in the electricity sales market has intensified, and the growth of power sales is not optimistic. It is necessary to conduct research and analysis of electricity sales. This paper conducts the analysis with the following steps: first, determines the leading, coincident, lagging economic indicators based on multi-factor correlation analysis, then synthesizes early warning index, forecasts electricity sales, finally, achieves early warning of external risks to improve the company's management quality of the electricity sales.
Highlights
In March 2015, "Several Opinions of the Central Committee of the Communist Party of China and the State Council on Further Deepening the Reform of the Power System" (Zhongfa [2015] No 9 Document) was issued, which opened the prelude to the reform of the power system
In order to actively respond to the impact of power system reform and external macroeconomic changes caused to the company's electricity sales market, it is necessary to establish a complete external risk early warning analysis system based on external risk indicators, on the one hand to achieve early warning of power sales risks
It shows that since January 2007, the average growth rate of electricity sales has been stable, and many places are in the 4-5% growth rate range. 4.4 Electricity sales warning result According to the results of the above-mentioned early warning index and macroeconomic forecast, the forecast results of the early warning index on electricity sales will show a volatility trend in the sales of electricity in late 2017
Summary
According to different attributes of different factors, the correlation analysis is mainly divided into classification, sequencing and distance correlation analysis. The coefficient is between 0 and 1, and a larger value indicates a higher degree of correlation. 2) Grey correlation coefficient: known as the gray correlation degree, it is based on the analysis of the proximity of the curve shape of each factor, and provides some suggestions for decision makers [2]. It proposes the concept of gray correlation analysis for each subsystem, and intends to seek numerical relationships among subsystems (or factors) in the system through certain methods. C. Find the gray correlation coefficient between the reference series and the comparison series ξ(Xi). The sequence and distance method are used for correlation analysis
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