The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
From probabilistic forecasts to statistical scenarios of short‐term wind power production
Abstract
Short‐term (up to 2–3 days ahead) probabilistic forecasts of wind power provide forecast users with highly valuable information on the uncertainty of expected wind generation. Whatever the type of these probabilistic forecasts, they are produced on a per horizon basis, and hence do not inform on the development of the forecast uncertainty through forecast series. However, this additional information may be paramount for a large class of time‐dependent and multistage decision‐making problems, e.g. optimal operation of combined wind‐storage systems or multiple‐market trading with different gate closures. This issue is addressed here by describing a method that permits the generation of statistical scenarios of short‐term wind generation that accounts for both the interdependence structure of prediction errors and the predictive distributions of wind power production. The method is based on the conversion of series of prediction errors to a multivariate Gaussian random variable, the interdependence structure of which can then be summarized by a unique covariance matrix. Such matrix is recursively estimated in order to accommodate long‐term variations in the prediction error characteristics. The quality and interest of the methodology are demonstrated with an application to the test case of a multi‐MW wind farm over a period of more than 2 years. Copyright © 2008 John Wiley & Sons, Ltd.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Correlation and Dependence in Risk Management: Properties and Pitfalls
Paul Embrechts, A., McNeil, A. J., & Straumann, D. (2002) · Cambridge University Press eBooks · doi:10.1017/cbo9780511615337.008 · Cited by 1,991
Probabilistic Forecasts, Calibration and Sharpness
Gneiting, T., Balabdaoui, F., & Raftery, A. E. (2007) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2007.00587.x · Cited by 1,783
A New Approach to Quantify Reserve Demand in Systems With Significant Installed Wind Capacity
Doherty, R., & O'Malley, M. (2005) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2005.846206 · Cited by 750
Trading Wind Generation From Short-Term Probabilistic Forecasts of Wind Power
Pinson, P., Chevallier, C., & Kariniotakis, G. N. (2007) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2007.901117 · Cited by 571
Self-citationRisk Management: Value at Risk and Beyond
Rheinländer, T. (2003) · Journal of the American Statistical Association · doi:10.1198/jasa.2003.s278 · Cited by 355
Non‐parametric probabilistic forecasts of wind power: required properties and evaluation
Pinson, P., Nielsen, H. A., Møller, J. K., Madsen, H., & Kariniotakis, G. N. (2007) · Wind Energy · doi:10.1002/we.230 · Cited by 300
Self-citationGneiting, T., Larson, K., Westrick, K., Genton, M. G., & Aldrich, E. (2006) · Journal of the American Statistical Association · doi:10.1198/016214506000000456 · Cited by 246
Probabilistic Short-term Wind Power Forecasting for the Optimal Management of Wind Generation
Juban, J., Siebert, N., & Kariniotakis, G. (2007) · doi:10.1109/pct.2007.4538398 · Cited by 159
A comparison of a few statistical models for making quantile wind power forecasts
Bremnes, J. B. (2005) · Wind Energy · doi:10.1002/we.182 · Cited by 106
Dynamic sizing of energy storage for hedging wind power forecast uncertainty
Pinson, P., Papaefthymiou, G., Klöckl, B., et al. (2009) · IEEE Power & Energy Society General Meeting (PESGM) · doi:10.1109/pes.2009.5275816 · Cited by 92
Self-citation
Cited by
Gneiting, T., & Katzfuss, M. (2014) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-062713-085831 · Cited by 888
Stochastic Energy Scheduling in Microgrids With Intermittent Renewable Energy Resources
Su, W., Wang, J., & Roh, J. (2014) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2013.2280645 · Cited by 734
Model-Free Renewable Scenario Generation Using Generative Adversarial Networks
Chen, Y., Wang, Y., Kirschen, D., & Zhang, B. (2018) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2018.2794541 · Cited by 674
Antonopoulos, I., Robu, V., Couraud, B., Kirli, D., Norbu, S., Kiprakis, A., Flynn, D., Elizondo-Gonzalez, S., & Wattam, S. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.109899 · Cited by 656
Energy Forecasting: A Review and Outlook
Hong, T., Pinson, P., Wang, Y., Weron, R., Yang, D., & Zareipour, H. (2020) · IEEE Open Access Journal of Power and Energy · doi:10.1109/oajpe.2020.3029979 · Cited by 621
Self-citationWind Energy: Forecasting Challenges for Its Operational Management
Pinson, P. (2013) · Statistical Science · doi:10.1214/13-sts445 · Cited by 377
Self-citationConditional Prediction Intervals of Wind Power Generation
Pinson, P., & Kariniotakis, G. (2010) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2010.2045774 · Cited by 329
Self-citationSetting the Operating Reserve Using Probabilistic Wind Power Forecasts
Matos, M. A., & Bessa, R. J. (2011) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2010.2065818 · Cited by 294
The future of forecasting for renewable energy
Sweeney, C., Bessa, R. J., Browell, J., & Pinson, P. (2020) · WIREs Energy and Environment · doi:10.1002/wene.365 · Cited by 292
Self-citationManaging Electric Vehicles in the Smart Grid Using Artificial Intelligence: A Survey
Rigas, E. S., Ramchurn, S. D., & Bassiliades, N. (2015) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2014.2376873 · Cited by 280
Artificial Intelligence Enabled Demand Response: Prospects and Challenges in Smart Grid Environment
Khan, M. A., Saleh, A. M., Waseem, M., & Sajjad, I. A. (2023) · IEEE Access · doi:10.1109/access.2022.3231444 · Cited by 179
Zhang, D., Jiang, S., Liu, J., Wang, L., Chen, Y., Xiao, Y., Jiao, S., Xie, Y., Zhang, Y., & Li, M. (2022) · Processes · doi:10.3390/pr10020330 · Cited by 25
Metadata
- Authors
- Pinson, P., Madsen, H., Nielsen, H. A., Papaefthymiou, G., & Klöckl, B.
- Venue
- Wind Energy
- Publisher
- Wiley
- Published
- 2008-09-22
- Volume
- 12
- Issue
- 1
- Pages
- 51-62
- ISSN
- 1095-4244 (ISSN-L)
- 1099-1824
- OpenAlex
- W2113810680
- Cited by
- 537
- References
- 11
- FWCI
- 10.16
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
