Long-term load combination forecasting method considering the periodicity and trend of data

¹ Economic and Technical Research Institute of State Grid Jilin Electric Power Company, Lvyuan District, Changchun 130062, China
² State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (North China Electric Power University), Changping District, Beijing 102206, China ;

^* Corresponding author: fumengdi97@163.com

Abstract

In order to solve the problems of insufficient accuracy of long-term power load forecasting and poor applicability of the model, this paper considers the coupling of a number of macro indicators, such as regional economic development and social development indicators, with the time series data of regional power load. BP neural network and Autoregressive integrated moving average model (ARIMA) are used to integrate and improve the forecasting model, so as to improve the trend forecasting ability of annual load forecasting model. The non parametric function method is used to forecast the periodic load data in the monthly load data, the annual load forecast is combined with the monthly load forecast to improve the overall forecasting accuracy of the model. Finally, through the comparison of grey prediction and other models and the verification of MAPE error analysis method, the prediction accuracy of the model method considering the combination of data periodicity and trend is significantly improved, which is suitable for the long-term prediction of regional power load.