Efficient electricity sales forecasting based on curve decomposition and factor regression

Electricity sales affected by economic development, seasonal changes, holiday, temperature and other factors, is difficult to predict accurately. In this paper, based on X13 seasonal adjustment and factor regression, a forecasting method is proposed, which takes development trend, seasonal variation and random variation of electricity sales into consideration. Firstly, the historical electricity sales data is preprocessed to improve the data quality, and the X13 seasonal adjustment method is adopted to decompose the electricity sales data into three subsequences: trend, season and random items. Then combining the influential factors of each subsequence with the characteristics of the curves, each item is predicted and reconstructed to get the forecasting results respectively, where the trend item is predicted by several algorithms. Finally, analytic hierarchy process (AHP)-based comprehensive evaluation model is adopted to get the final forecasting results. The validation shows that the monthly average error of the whole industry is 1.11% from January to June 2016.