The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.
The future of forecasting for renewable energy
Open accessAbstract
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
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Dwork, C. (2006) · Lecture Notes in Computer Science · doi:10.1007/11787006_1 · Cited by 5,291
Blockchains and smart contracts for the internet of things
Christidis, K., & Devetsikiotis, M. (2016) · IEEE Access · doi:10.1109/access.2016.2566339 · Cited by 4,552
The quiet revolution of numerical weather prediction
Bauer, P., Thorpe, A., & Brunet, G. (2015) · Nature · doi:10.1038/nature14956 · Cited by 2,729
Advances in Neural Information Processing Systems 27 (NIPS 2014)
Zhong, M., Goddard, N., & Sutton, C. A. (2014) · Cited by 2,440
Maier, H. R., & Dandy, G. C. (2000) · Environmental Modelling & Software · doi:10.1016/s1364-8152(99)00007-9 · Cited by 2,377
Review of photovoltaic power forecasting
Antonanzas, J., Osorio, N., Escobar, R., Urraca, R., Martinez-de-Pison, F. J., & Antonanzas-Torres, F. (2016) · Solar Energy · doi:10.1016/j.solener.2016.06.069 · Cited by 1,343
Wind Turbines Emulating Inertia and Supporting Primary Frequency Control
Morren, J., de Haan, S. W. H., Kling, W. L., & Ferreira, J. A. (2006) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2005.861956 · Cited by 1,263
Current status and future advances for wind speed and power forecasting
Jung, J., & Broadwater, R. (2014) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2013.12.054 · Cited by 710
Toward Numerical Modeling in the “Terra Incognita”
Wyngaard, J. C. (2004) · Journal of the Atmospheric Sciences · doi:10.1175/1520-0469(2004)061<1816:tnmitt>2.0.co;2 · Cited by 695
Computing arbitrary functions of encrypted data
Gentry, C. (2010) · Communications of the ACM · doi:10.1145/1666420.1666444 · Cited by 650
Cited by
A review and taxonomy of wind and solar energy forecasting methods based on deep learning
Alkhayat, G., & Mehmood, R. (2021) · Energy and AI · doi:10.1016/j.egyai.2021.100060 · Cited by 312
Li, Y., Wang, R., Li, Y., Zhang, M., & Long, C. (2023) · Applied Energy · doi:10.1016/j.apenergy.2022.120291 · Cited by 259
Metadata
- Authors
- Sweeney, C., Bessa, R. J., Browell, J., & Pinson, P.
- Venue
- WIREs Energy and Environment
- Publisher
- Wiley
- Published
- 2019-09-09
- Volume
- 9
- Issue
- 2
- ISSN
- 2041-8396 (ISSN-L)
- 2041-840X
- OpenAlex
- W2972425174
- Cited by
- 292
- References
- 60
- FWCI
- 8.02
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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