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Electric Load Forecasting Based on a Least Squares Support Vector Machine with Fuzzy Time Series and Global Harmony Search Algorithm

Author

Listed:
  • Yan Hong Chen

    (School of Information, Zhejiang University of Finance & Economics, Hangzhou 310018, China)

  • Wei-Chiang Hong

    (School of Economics & Management, Nanjing Tech University, Nanjing 211800, China
    Department of Information Management, Oriental Institute of Technology, 58 Sec. 2, Sichuan Road, Panchiao, Taipei 220, Taiwan)

  • Wen Shen

    (School of Information, Zhejiang University of Finance & Economics, Hangzhou 310018, China)

  • Ning Ning Huang

    (School of Information, Zhejiang University of Finance & Economics, Hangzhou 310018, China)

Abstract

This paper proposes a new electric load forecasting model by hybridizing the fuzzy time series (FTS) and global harmony search algorithm (GHSA) with least squares support vector machines (LSSVM), namely GHSA-FTS-LSSVM model. Firstly, the fuzzy c-means clustering (FCS) algorithm is used to calculate the clustering center of each cluster. Secondly, the LSSVM is applied to model the resultant series, which is optimized by GHSA. Finally, a real-world example is adopted to test the performance of the proposed model. In this investigation, the proposed model is verified using experimental datasets from the Guangdong Province Industrial Development Database, and results are compared against autoregressive integrated moving average (ARIMA) model and other algorithms hybridized with LSSVM including genetic algorithm (GA), particle swarm optimization (PSO), harmony search, and so on. The forecasting results indicate that the proposed GHSA-FTS-LSSVM model effectively generates more accurate predictive results.

Suggested Citation

  • Yan Hong Chen & Wei-Chiang Hong & Wen Shen & Ning Ning Huang, 2016. "Electric Load Forecasting Based on a Least Squares Support Vector Machine with Fuzzy Time Series and Global Harmony Search Algorithm," Energies, MDPI, vol. 9(2), pages 1-13, January.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:2:p:70-:d:62863
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    References listed on IDEAS

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    Cited by:

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