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Short Term Wind Power Prediction Based on Data Regression and Enhanced Support Vector Machine

Author

Listed:
  • Chia-Sheng Tu

    (College of Intelligence Robot, Fuzhou Polytechnic, Fuzhou 350108, China)

  • Chih-Ming Hong

    (Department of Electronic Communication Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 811213, Taiwan)

  • Hsi-Shan Huang

    (College of Intelligence Robot, Fuzhou Polytechnic, Fuzhou 350108, China)

  • Chiung-Hsing Chen

    (Department of Electronic Communication Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 811213, Taiwan)

Abstract

This paper presents a short-term wind power forecasting model for the next day based on historical marine weather and corresponding wind power output data. Due the large amount of historical marine weather and wind power data, we divided the data into clusters using the data regression (DR) algorithm to get meaningful training data, so as to reduce the number of modeling data and improve the efficiency of computing. The regression model was constructed based on the principle of the least squares support vector machine (LSSVM). We carried out wind speed forecasting for one hour and one day and used the correlation between marine wind speed and the corresponding wind power regression model to realize an indirect wind power forecasting model. Proper parameter settings for LSSVM are important to ensure its efficiency and accuracy. In this paper, we used an enhanced bee swarm optimization (EBSO) to perform the parameter optimization for LSSVM, which not only improved the forecast model availability, but also improved the forecasting accuracy.

Suggested Citation

  • Chia-Sheng Tu & Chih-Ming Hong & Hsi-Shan Huang & Chiung-Hsing Chen, 2020. "Short Term Wind Power Prediction Based on Data Regression and Enhanced Support Vector Machine," Energies, MDPI, vol. 13(23), pages 1-18, November.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:23:p:6319-:d:453766
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    References listed on IDEAS

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    2. David Barbosa de Alencar & Carolina De Mattos Affonso & Roberto Célio Limão de Oliveira & Jorge Laureano Moya Rodríguez & Jandecy Cabral Leite & José Carlos Reston Filho, 2017. "Different Models for Forecasting Wind Power Generation: Case Study," Energies, MDPI, vol. 10(12), pages 1-27, November.
    3. Cadenas, E. & Jaramillo, O.A. & Rivera, W., 2010. "Analysis and forecasting of wind velocity in chetumal, quintana roo, using the single exponential smoothing method," Renewable Energy, Elsevier, vol. 35(5), pages 925-930.
    4. Qian Zhang & Kin Keung Lai & Dongxiao Niu & Qiang Wang & Xuebin Zhang, 2012. "A Fuzzy Group Forecasting Model Based on Least Squares Support Vector Machine (LS-SVM) for Short-Term Wind Power," Energies, MDPI, vol. 5(9), pages 1-18, September.
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    Cited by:

    1. Jingtao Huang & Gang Niu & Haiping Guan & Shuzhong Song, 2023. "Ultra-Short-Term Wind Power Prediction Based on LSTM with Loss Shrinkage Adam," Energies, MDPI, vol. 16(9), pages 1-13, April.
    2. Karthick Kanagarathinam & S. K. Aruna & S. Ravivarman & Mejdl Safran & Sultan Alfarhood & Waleed Alrajhi, 2023. "Enhancing Sustainable Urban Energy Management through Short-Term Wind Power Forecasting Using LSTM Neural Network," Sustainability, MDPI, vol. 15(18), pages 1-18, September.
    3. Wen-Chang Tsai & Chih-Ming Hong & Chia-Sheng Tu & Whei-Min Lin & Chiung-Hsing Chen, 2023. "A Review of Modern Wind Power Generation Forecasting Technologies," Sustainability, MDPI, vol. 15(14), pages 1-40, July.

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