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The future of forecasting for renewable energy

Author

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  • Conor Sweeney
  • Ricardo J. Bessa
  • Jethro Browell
  • Pierre Pinson

Abstract

Forecasting for wind and solar renewable energy is becoming more important as the amount of energy generated from these sources increases. Forecast skill is improving, but so too is the way forecasts are being used. In this paper, we present a brief overview of the state‐of‐the‐art of forecasting wind and solar energy. We describe approaches in statistical and physical modeling for time scales from minutes to days ahead, for both deterministic and probabilistic forecasting. Our focus changes then to consider the future of forecasting for renewable energy. We discuss recent advances which show potential for great improvement in forecast skill. Beyond the forecast itself, we consider new products which will be required to aid decision making subject to risk constraints. Future forecast products will need to include probabilistic information, but deliver it in a way tailored to the end user and their specific decision making problems. Businesses operating in this sector may see a change in business models as more people compete in this space, with different combinations of skills, data and modeling being required for different products. The transaction of data itself may change with the adoption of blockchain technology, which could allow providers and end users to interact in a trusted, yet decentralized way. Finally, we discuss new industry requirements and challenges for scenarios with high amounts of renewable energy. New forecasting products have the potential to model the impact of renewables on the power system, and aid dispatch tools in guaranteeing system security. This article is categorized under: Energy Infrastructure > Systems and Infrastructure Wind Power > Systems and Infrastructure Photovoltaics > Systems and Infrastructure

Suggested Citation

  • Conor Sweeney & Ricardo J. Bessa & Jethro Browell & Pierre Pinson, 2020. "The future of forecasting for renewable energy," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 9(2), March.
    • Handle: RePEc:bla:wireae:v:9:y:2020:i:2:n:e365
    DOI: 10.1002/wene.365
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    References listed on IDEAS

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    Cited by:

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    2. Han, Chanok & Vinel, Alexander, 2022. "Reducing forecasting error by optimally pooling wind energy generation sources through portfolio optimization," Energy, Elsevier, vol. 239(PB).
    3. Yang, Dazhi & Kleissl, Jan, 2023. "Summarizing ensemble NWP forecasts for grid operators: Consistency, elicitability, and economic value," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1640-1654.
    4. Yang, Dazhi & Wang, Wenting & Gueymard, Christian A. & Hong, Tao & Kleissl, Jan & Huang, Jing & Perez, Marc J. & Perez, Richard & Bright, Jamie M. & Xia, Xiang’ao & van der Meer, Dennis & Peters, Ian , 2022. "A review of solar forecasting, its dependence on atmospheric sciences and implications for grid integration: Towards carbon neutrality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    5. González-Sopeña, J.M. & Pakrashi, V. & Ghosh, B., 2021. "An overview of performance evaluation metrics for short-term statistical wind power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    6. Mayer, Martin János & Yang, Dazhi, 2022. "Probabilistic photovoltaic power forecasting using a calibrated ensemble of model chains," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    7. Nasery, Praanjal & Aziz Ezzat, Ahmed, 2023. "Yaw-adjusted wind power curve modeling: A local regression approach," Renewable Energy, Elsevier, vol. 202(C), pages 1368-1376.
    8. Li, Yang & Wang, Ruinong & Li, Yuanzheng & Zhang, Meng & Long, Chao, 2023. "Wind power forecasting considering data privacy protection: A federated deep reinforcement learning approach," Applied Energy, Elsevier, vol. 329(C).
    9. 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).
    10. Cramer, Eike & Witthaut, Dirk & Mitsos, Alexander & Dahmen, Manuel, 2023. "Multivariate probabilistic forecasting of intraday electricity prices using normalizing flows," Applied Energy, Elsevier, vol. 346(C).
    11. Yang, Dazhi & Yang, Guoming & Liu, Bai, 2023. "Combining quantiles of calibrated solar forecasts from ensemble numerical weather prediction," Renewable Energy, Elsevier, vol. 215(C).

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