Particle Filter-Based Electricity Load Prediction for Grid-Connected Microgrid Day-Ahead Scheduling

This paper proposes a particle filter (PF)-based electricity load prediction method to improve the accuracy of the microgrid day-ahead scheduling. While most of the existing prediction methods assume electricity loads follow normal distributions, we consider it is a nonlinear and non-Gaussian process which is closer to the reality. To handle the nonlinear and non-Gaussian characteristics of electricity load profile, the PF-based method is implemented to improve the prediction accuracy. These load predictions are used to provide the microgrid day-ahead scheduling. The impact of load prediction error on the scheduling decision is analyzed based on actual data. Comparison results on a distribution system show that the estimation precision of electricity load based on the PF method is the highest among several conventional intelligent methods such as the Elman neural network (ENN) and support vector machine (SVM). Furthermore, the impact of the different parameter settings are analyzed for the proposed PF based load prediction. The management efficiency of microgrid is significantly improved by using the PF method.