Distributed electricity load forecasting model mining based on hybrid gene expression programming and cloud computing

Abstract

Load forecasting is an important part of power grid management. Accurate and timely load forecasting is of great significance to formulate economical and reasonable power allocation plan, improve safety and economy of power grid operation and improve power quality. In this paper, in order to find electricity load forecasting model, we propose an electricity load forecasting function mining algorithm based on artificial fish swarm and gene expression programming (ELFFM-AFSGEP). On the basis, distributed load forecast model mining based on hybrid gene expression programming and cloud computing (DLFMM-HGEPCloud) is proposed to solve the problem of massive electricity load forecasting. In order to better solve global electricity load forecasting model, error minimization crossover is introduced into DLFMM-HGEPCloud. The performance of the proposed algorithm in this paper is evaluated with a real-world dataset, and compared with GEP and some published algorithms by using the same dataset. Experimental results show that our proposed algorithm has an advantage in average time-consumption, average number of convergence, forecasted accuracy and excellent parallel performance in speedup and scaleup.