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Renewable Power Output Forecasting Using Least-Squares Support Vector Regression and Google Data

Author

Listed:
  • Kuen-Suan Chen

    (Department of Industrial Engineering and Management, National Chin-Yi University of Technology, Taichung 41170, Taiwan)

  • Kuo-Ping Lin

    (Institute of Innovation and Circular Economy, Asia University, Taichung 41354, Taiwan
    Department of Medical Research, China Medical University Hospital, China Medical University, Taichung 40402, Taiwan)

  • Jun-Xiang Yan

    (Department of Information Management, Lunghwa University of Science and Technology, Taoyuan 33306, Taiwan)

  • Wan-Lin Hsieh

    (Department of Industrial Engineering and Enterprise Information, Tunghai University, Taichung 40704, Taiwan)

Abstract

Sustainable and green technologies include renewable energy sources such as solar power, wind power, and hydroelectric power. Renewable power output forecasting is an essential contributor to energy technology and strategy analysis. This study attempts to develop a novel least-squares support vector regression with a Google (LSSVR-G) model to accurately forecast power output with renewable power, thermal power, and nuclear power outputs in Taiwan. This study integrates a Google application programming interface (API), least-squares support vector regression (LSSVR), and a genetic algorithm (GA) to develop a novel LSSVR-G model for accurately forecasting power output from various power outputs in Taiwan. Material price and the search volume via Google’s search engine for keywords, which is used for various power outputs and is collected by Google APIs, are used as input data. The forecasting model uses LSSVR. Furthermore, the LSSVR employs a GA to find the optimal parameters for the LSSVR. Real-world annual power output datasets collected from Taiwan were used to demonstrate the forecasting performance of the model. The empirical results reveal that the proposed LSSVR-G model is superior to all other considered models both in terms of accuracy and stability, and, thus, can be a useful tool for renewable power forecasting. Moreover, the accuracy forecasting thermal power and nuclear power could effectively assist in understanding the future trend of renewable power output in Taiwan. The accurately forecasting result could effectively provide basic information for renewable power, thermal power, and nuclear power planning and policy making in Taiwan.

Suggested Citation

  • Kuen-Suan Chen & Kuo-Ping Lin & Jun-Xiang Yan & Wan-Lin Hsieh, 2019. "Renewable Power Output Forecasting Using Least-Squares Support Vector Regression and Google Data," Sustainability, MDPI, vol. 11(11), pages 1-13, May.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:11:p:3009-:d:234964
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    References listed on IDEAS

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    Cited by:

    1. Qi Liu & Jie Zhao & Youguo Shao & Libin Wen & Jianxu Wu & Dichen Liu & Yuhui Ma, 2019. "Multi-Power Joint Peak-Shaving Optimization for Power System Considering Coordinated Dispatching of Nuclear Power and Wind Power," Sustainability, MDPI, vol. 11(17), pages 1-23, September.
    2. Guangyu Qin & Qingyou Yan & Jingyao Zhu & Chuanbo Xu & Daniel M. Kammen, 2021. "Day-Ahead Wind Power Forecasting Based on Wind Load Data Using Hybrid Optimization Algorithm," Sustainability, MDPI, vol. 13(3), pages 1-17, January.
    3. Paweł Piotrowski & Marcin Kopyt & Dariusz Baczyński & Sylwester Robak & Tomasz Gulczyński, 2021. "Hybrid and Ensemble Methods of Two Days Ahead Forecasts of Electric Energy Production in a Small Wind Turbine," Energies, MDPI, vol. 14(5), pages 1-25, February.

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