Accuracy of wind energy forecasts in Great Britain and prospects for improvement
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DOI: 10.1016/j.jup.2020.101111
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Cited by:
- Couto, António & Estanqueiro, Ana, 2022. "Enhancing wind power forecast accuracy using the weather research and forecasting numerical model-based features and artificial neuronal networks," Renewable Energy, Elsevier, vol. 201(P1), pages 1076-1085.
- Konrad Świrski & Piotr Błach, 2024. "Energy Storage Management Using Artificial Intelligence to Maximize Polish Energy Market Profits," Energies, MDPI, vol. 17(19), pages 1-17, September.
- Jastrzebska, Agnieszka & Morales Hernández, Alejandro & Nápoles, Gonzalo & Salgueiro, Yamisleydi & Vanhoof, Koen, 2022. "Measuring wind turbine health using fuzzy-concept-based drifting models," Renewable Energy, Elsevier, vol. 190(C), pages 730-740.
- Forbes, Kevin F., 2023. "Demand for grid-supplied electricity in the presence of distributed solar energy resources: Evidence from New York City," Utilities Policy, Elsevier, vol. 80(C).
- Philippe de Bekker & Sho Cremers & Sonam Norbu & David Flynn & Valentin Robu, 2023. "Improving the Efficiency of Renewable Energy Assets by Optimizing the Matching of Supply and Demand Using a Smart Battery Scheduling Algorithm," Energies, MDPI, vol. 16(5), pages 1-26, March.
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More about this item
Keywords
Wind energy forecasting; Forecast accuracy;JEL classification:
- Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
- Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
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