A Novel Accuracy Adjustment Approach for Forecasted Quantity of Electricity Sales Based on Abnormal Influencing Factors Assessment

The forecasting of the monthly electricity sales is a fundamental work of the marketing department of State Grid Corporation of China, which has been implemented using regression and time-series analysis based on historical electricity sales. In this paper, we first study the correlation between electricity sales of all trades and related influencing factors, e.g., weather, economy, holidays and events, using the Pearson correlation coefficient, and further divide all trades into several super trades using the EM clustering algorithm based on the correlation. Then, a electricity sales adjustment model is created for each super trade using the SVM regression algorithm, which can determine the adjustment quantity of the electricity sales based on anomalies of the influencing factors. Extensive experimental study shows that the proposed adjustment approach can greatly improve the accuracy with respect to forecasting the next 12 months electricity sales of the power companies of State Grid Corporation of China (SGCC).