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Enhanced support vector regression based forecast engine to predict solar power output

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  • Shang, Chuanfu
  • Wei, Pengcheng

Abstract

The critical role of photovoltaic (PV) energy as renewable sources in network can make some problems in power grids operation. Due to high volatility of PV signal, the prediction and its evaluation in planning and operation is very difficult. For this purpose, an accurate prediction approach is developed in this paper to tackle the mentioned problem. The proposed approach is based on enhanced empirical model decomposition (EEMD), a new feature selection method and hybrid forecast engine. The proposed feature selection is formulated by different criteria to select the best candidate inputs of forecast engine. And finally the hybrid forecast engine composed of improved support vector regression (ISVR) plus optimization algorithm to fine tune the related free parameters. Effectiveness of proposed method is applied over real-world engineering test cases through comparison with various prediction models.

Suggested Citation

  • Shang, Chuanfu & Wei, Pengcheng, 2018. "Enhanced support vector regression based forecast engine to predict solar power output," Renewable Energy, Elsevier, vol. 127(C), pages 269-283.
    • Handle: RePEc:eee:renene:v:127:y:2018:i:c:p:269-283
    DOI: 10.1016/j.renene.2018.04.067
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    5. Antonio Bracale & Guido Carpinelli & Pasquale De Falco, 2019. "Developing and Comparing Different Strategies for Combining Probabilistic Photovoltaic Power Forecasts in an Ensemble Method," Energies, MDPI, vol. 12(6), pages 1-16, March.
    6. Gao, Mingming & Li, Jianjing & Hong, Feng & Long, Dongteng, 2019. "Day-ahead power forecasting in a large-scale photovoltaic plant based on weather classification using LSTM," Energy, Elsevier, vol. 187(C).
    7. Sultana, N. & Hossain, S.M. Zakir & Albalooshi, H.A. & Chrouf, S.M.B. & AlNajar, I.A. & Alhindi, K.R. & AlMofeez, K.A. & Razzak, S.A. & Hossain, M.M., 2021. "Soft computing modeling and multiresponse optimization for production of microalgal biomass and lipid as bioenergy feedstock," Renewable Energy, Elsevier, vol. 178(C), pages 1020-1033.
    8. Kushwaha, Vishal & Pindoriya, Naran M., 2019. "A SARIMA-RVFL hybrid model assisted by wavelet decomposition for very short-term solar PV power generation forecast," Renewable Energy, Elsevier, vol. 140(C), pages 124-139.
    9. Zaohui Kang & Jizhong Xue & Chun Sing Lai & Yu Wang & Haoliang Yuan & Fangyuan Xu, 2023. "Vision Transformer-Based Photovoltaic Prediction Model," Energies, MDPI, vol. 16(12), pages 1-14, June.
    10. Muhannad Alaraj & Ibrahim Alsaidan & Astitva Kumar & Mohammad Rizwan & Majid Jamil, 2023. "Advanced Intelligent Approach for Solar PV Power Forecasting Using Meteorological Parameters for Qassim Region, Saudi Arabia," Sustainability, MDPI, vol. 15(12), pages 1-16, June.
    11. Chao Fu & Guo-Quan Li & Kuo-Ping Lin & Hui-Juan Zhang, 2019. "Short-Term Wind Power Prediction Based on Improved Chicken Algorithm Optimization Support Vector Machine," Sustainability, MDPI, vol. 11(2), pages 1-15, January.

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