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

Author

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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)

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.

Suggested Citation

  • Qiangqiang Cheng & Yiqi Yan & Shichao Liu & Chunsheng Yang & Hicham Chaoui & Mohamad Alzayed, 2020. "Particle Filter-Based Electricity Load Prediction for Grid-Connected Microgrid Day-Ahead Scheduling," Energies, MDPI, vol. 13(24), pages 1-15, December.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:24:p:6489-:d:458887
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    References listed on IDEAS

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