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

Qiangqiang Cheng, Yiqi Yan, Shichao Liu, Chunsheng Yang, Hicham Chaoui and Mohamad Alzayed
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Qiangqiang Cheng: The Key Laboratory of Nondestructive Testing, Nanchang Hangkong University, Nanchang 330063, China
Yiqi Yan: The Key Laboratory of Nondestructive Testing, Nanchang Hangkong University, Nanchang 330063, China
Shichao Liu: Department of Electronics, Carleton University, Ottawa, ON K1S 5B6, Canada
Chunsheng Yang: The National Research Council, Ottawa, ON K1L 5M4, Canada
Hicham Chaoui: Department of Electronics, Carleton University, Ottawa, ON K1S 5B6, Canada
Mohamad Alzayed: Department of Electronics, Carleton University, Ottawa, ON K1S 5B6, Canada

Energies, 2020, vol. 13, issue 24, 1-15

Abstract: 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.

Keywords: electricity load prediction; day-ahead scheduling; particle filter; microgrid energy management (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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