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Renewable generation forecast studies – Review and good practice guidance

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  • Croonenbroeck, Carsten
  • Stadtmann, Georg

Abstract

Propelled by the actual demand from the renewable energy industry, the progress of literature on quantitative forecasting models during the past years is extensive. Research provides a vast output of papers on wind speed, wind power, solar irradiance and solar power forecasting models, accompanied by models for energy load and price forecasting for short-term (e.g. for the intraday trading schemes available at many market places) to medium-term (e.g. for day-ahead trading) usage. While the models themselves are, mostly, rather sophisticated, the statistical evaluation of the results sometimes leaves headroom for improvement. Unfortunately, the latter may occasionally result in the rejection of papers.

Suggested Citation

  • Croonenbroeck, Carsten & Stadtmann, Georg, 2019. "Renewable generation forecast studies – Review and good practice guidance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 312-322.
  • Handle: RePEc:eee:rensus:v:108:y:2019:i:c:p:312-322
    DOI: 10.1016/j.rser.2019.03.029
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    4. Simian Pang & Zixuan Zheng & Fan Luo & Xianyong Xiao & Lanlan Xu, 2021. "Hybrid Forecasting Methodology for Wind Power-Photovoltaic-Concentrating Solar Power Generation Clustered Renewable Energy Systems," Sustainability, MDPI, vol. 13(12), pages 1-16, June.
    5. He, Yi & Guo, Su & Zhou, Jianxu & Ye, Jilei & Huang, Jing & Zheng, Kun & Du, Xinru, 2022. "Multi-objective planning-operation co-optimization of renewable energy system with hybrid energy storages," Renewable Energy, Elsevier, vol. 184(C), pages 776-790.
    6. Pereira, Diogo Santos & Marques, António Cardoso, 2020. "Could electricity demand contribute to diversifying the mix and mitigating CO2 emissions? A fresh daily analysis of the French electricity system," Energy Policy, Elsevier, vol. 142(C).
    7. Mashlakov, Aleksei & Kuronen, Toni & Lensu, Lasse & Kaarna, Arto & Honkapuro, Samuli, 2021. "Assessing the performance of deep learning models for multivariate probabilistic energy forecasting," Applied Energy, Elsevier, vol. 285(C).
    8. Francois Rozon & Craig McGregor & Michael Owen, 2023. "Long-Term Forecasting Framework for Renewable Energy Technologies’ Installed Capacity and Costs for 2050," Energies, MDPI, vol. 16(19), pages 1-20, September.
    9. Sun, Fei & Jin, Tongdan, 2022. "A hybrid approach to multi-step, short-term wind speed forecasting using correlated features," Renewable Energy, Elsevier, vol. 186(C), pages 742-754.

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    More about this item

    Keywords

    Forecasting; Electricity prices; Wind and solar; Point forecasts; Probabilistic forecasts; Sharpness; Reliability;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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