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WIREs Energy Environ.
Impact Factor: 3.297

The future of forecasting for renewable energy

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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
12‐month rolling average of the 48‐hr ahead root mean square error (RMSE) forecast skill for wind speed on the 850 hPa pressure level in the Northern Hemisphere. Blue lines: old/new ECMWF IFS. Black line: NCEP GFS
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Graphical representation of different business models in renewable energy forecasting
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Illustration of different types of errors: (a) Level Error, (b) Phase Error, (c) Spatial Error. (a) Forecast: Dashed line. Observed: Solid line. (b) Forecast: Dashed line. Observed: Solid line. (c) Forecast: Left plot. Observed: Right plot. Spatial difference highlighted as hatched area
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