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Short-Term Load Forecasting for Electric Power Systems Using the PSO-SVR and FCM Clustering Techniques

Author

Listed:
  • Pan Duan

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing, 400030, China)

  • Kaigui Xie

    (School of Automation Engineering, Chongqing University, 400030, China)

  • Tingting Guo

    (School of Automation Engineering, Chongqing University, 400030, China)

  • Xiaogang Huang

    (Chongqing Tongnan Electric Power Company, Chongqing, 402660, China)

Abstract

This paper presents a new combined method for the short-term load forecasting of electric power systems based on the Fuzzy c-means (FCM) clustering, particle swarm optimization (PSO) and support vector regression (SVR) techniques. The training samples used in this method are of the same data type as the learning samples in the forecasting process and selected by a fuzzy clustering technique according to the degree of similarity of the input samples considering the periodic characteristics of the load. PSO is applied to optimize the model parameters. The complicated nonlinear relationships between the factors influencing the load and the load forecasting can be regressed using the SVR. The practical load data from a city in Chongqing was used to illustrate the proposed method, and the results indicate that the proposed method can obtain higher accuracy compared with the traditional method, and is effective for forecasting the short-term load of power systems.

Suggested Citation

  • Pan Duan & Kaigui Xie & Tingting Guo & Xiaogang Huang, 2011. "Short-Term Load Forecasting for Electric Power Systems Using the PSO-SVR and FCM Clustering Techniques," Energies, MDPI, vol. 4(1), pages 1-12, January.
    • Handle: RePEc:gam:jeners:v:4:y:2011:i:1:p:173-184:d:11061
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    Citations

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    Cited by:

    1. Yi Liang & Dongxiao Niu & Minquan Ye & Wei-Chiang Hong, 2016. "Short-Term Load Forecasting Based on Wavelet Transform and Least Squares Support Vector Machine Optimized by Improved Cuckoo Search," Energies, MDPI, vol. 9(10), pages 1-17, October.
    2. Ebrahim Farjah & Mosayeb Bornapour & Taher Niknam & Bahman Bahmanifirouzi, 2012. "Placement of Combined Heat, Power and Hydrogen Production Fuel Cell Power Plants in a Distribution Network," Energies, MDPI, vol. 5(3), pages 1-25, March.
    3. Csereklyei, Zsuzsanna & Thurner, Paul W. & Langer, Johannes & Küchenhoff, Helmut, 2017. "Energy paths in the European Union: A model-based clustering approach," Energy Economics, Elsevier, vol. 65(C), pages 442-457.
    4. Cao, Guohua & Wu, Lijuan, 2016. "Support vector regression with fruit fly optimization algorithm for seasonal electricity consumption forecasting," Energy, Elsevier, vol. 115(P1), pages 734-745.
    5. Félix Iglesias & Wolfgang Kastner, 2013. "Analysis of Similarity Measures in Times Series Clustering for the Discovery of Building Energy Patterns," Energies, MDPI, vol. 6(2), pages 1-19, January.
    6. Liu, Xin & Zhang, Zijun & Song, Zhe, 2020. "A comparative study of the data-driven day-ahead hourly provincial load forecasting methods: From classical data mining to deep learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 119(C).
    7. Zeyu Wang & Ravi S. Srinivasan, 2015. "Classification of Household Appliance Operation Cycles: A Case-Study Approach," Energies, MDPI, vol. 8(9), pages 1-15, September.

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