An electricity load forecasting method based on association rule analysis in smart grid

Abstract

The short-term load forecasting of electric system is one of pivotal aspects for safety and economy operation of the power system. Accurate short-term load forecasting is advantageous to enhance the secure and economic effect of power system and ameliorate the supply quality. Thus, it is important to find an effective method to improve the short-term forecast precision effectively. In this paper, association rule analysis is proposed to analyze the relevance of power load and its influencing factors, in order to improve the accuracy of load forecasting. There are lots of uncertain factors in smart grid power system affect the accuracy of the load forecasting directly. Meanwhile some less important factors can be reduced by attribute reduction. In this paper, a new method (IPLSAR) is proposed to forecast short-term load which is based on association rule analysis (AR) and least squares support vector machine (LSSVM) optimized by improved particle swarm optimization (IPSO). The Apriori-4A® algorithm is proposed to extract association rules. And attribute reduction is carried out based on these extracted association rules. Experimental results indicate that the proposed forecast method (IPLSAR) has higher accuracy and less running time.