Annual Contribution Electricity Forecasting Model Based on Logistic Regression Analysis

Abstract

Abstract Due to the complex characteristics of the annual contribution time series, it is difficult to achieve the ideal prediction effect by a single prediction. Therefore, the annual contribution electricity prediction model based on Logistic regression analysis is studied. The statistical method of time series analysis combined with the fuzzy correlation feature analysis method is used to obtain the high-voltage power transmission data of business expansion and the power consumption data after power transmission. We use the fuzzy clustering theory to complete the customer segmentation and accurately locate the same type of user groups. On this basis, we preprocess the characteristic data of the annual contribution electricity forecast and build the annual contribution electricity forecast model based on Logistic regression analysis to realize the annual contribution electricity forecast. The experimental results show that the proposed method has a good prediction effect of annual contribution power, and can effectively shorten the prediction time of annual contribution power.