IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v4y2011i1p173-184d11061.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/4/1/173/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/4/1/173/
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:4:y:2011:i:1:p:173-184:d:11061. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.