The future of forecasting for renewable energy

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

Research output: Contribution to journalArticlepeer-review

164 Citations (Scopus)
147 Downloads (Pure)

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 modelling 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 modelling 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 decentralised 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.
Original languageEnglish
Article numbere365
JournalWIREs: Energy and Environment
Volume9
Issue number2
Early online date9 Sept 2019
DOIs
Publication statusE-pub ahead of print - 9 Sept 2019

Keywords

  • renewable energy
  • forecasting
  • decision making support systems
  • probabilistic information
  • blockchain

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