The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Non‐parametric probabilistic forecasts of wind power: required properties and evaluation
Abstract
Predictions of wind power production for horizons up to 48–72 h ahead comprise a highly valuable input to the methods for the daily management or trading of wind generation. Today, users of wind power predictions are not only provided with point predictions, which are estimates of the conditional expectation of the wind generation for each look‐ahead time, but also with uncertainty estimates given by probabilistic forecasts. In order to avoid assumptions on the shape of predictive distributions, these probabilistic predictions are produced from non‐parametric methods, and then take the form of a single or a set of quantile forecasts. The required and desirable properties of such probabilistic forecasts are defined and a framework for their evaluation is proposed. This framework is applied for evaluating the quality of two statistical methods producing full predictive distributions from point predictions of wind power. These distributions are defined by a number of quantile forecasts with nominal proportions spanning the unit interval. The relevance and interest of the introduced evaluation framework are discussed. Copyright © 2007 John Wiley & Sons, Ltd.
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Cites
Koenker, R., & Bassett, G. (1978) · Econometrica · doi:10.2307/1913643 · Cited by 13,316
Strictly Proper Scoring Rules, Prediction, and Estimation
Gneiting, T., & Raftery, A. E. (2007) · Journal of the American Statistical Association · doi:10.1198/016214506000001437 · Cited by 5,793
Christoffersen, P. F. (1998) · International Economic Review · doi:10.2307/2527341 · Cited by 2,669
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
Decomposition of the Continuous Ranked Probability Score for Ensemble Prediction Systems
Hersbach, H. (2000) · Weather and Forecasting · doi:10.1175/1520-0434(2000)015<0559:dotcrp>2.0.co;2 · Cited by 1,531
Chatfield, C. (2000) · Chapman & Hall/CRC eBooks · doi:10.1201/9781420036206 · Cited by 1,040
Resampling Methods for Dependent Data
Lahiri, S. N. (2003) · Springer Series in Statistics · doi:10.1007/978-1-4757-3803-2 · Cited by 1,015
Introduction to Probability and Statistics for Engineers and Scientists
Reynolds, M. R. (1988) · Technometrics · doi:10.1080/00401706.1988.10488450 · Cited by 989
Interpretation of Rank Histograms for Verifying Ensemble Forecasts
Hamill, T. M. (2001) · Monthly Weather Review · doi:10.1175/1520-0493(2001)129<0550:iorhfv>2.0.co;2 · Cited by 892
What Is a Good Forecast? An Essay on the Nature of Goodness in Weather Forecasting
Murphy, A. H. (1993) · Weather and Forecasting · doi:10.1175/1520-0434(1993)008<0281:wiagfa>2.0.co;2 · Cited by 833
Cited by
Review on probabilistic forecasting of wind power generation
Zhang, Y., Wang, J., & Wang, X. (2014) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2014.01.033 · Cited by 646
Recent advances in electricity price forecasting: A review of probabilistic forecasting
Nowotarski, J., & Weron, R. (2018) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.05.234 · Cited by 552
From probabilistic forecasts to statistical scenarios of short‐term wind power production
Pinson, P., Madsen, H., Nielsen, H. A., Papaefthymiou, G., & Klöckl, B. (2009) · Wind Energy · doi:10.1002/we.284 · Cited by 537
Self-citationReview on probabilistic forecasting of photovoltaic power production and electricity consumption
van der Meer, D. W., Widén, J., & Munkhammar, J. (2018) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.05.212 · Cited by 481
Zhang, J., Yan, J., Infield, D., Liu, Y., & Lien, F.-S. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.03.044 · Cited by 396
Metadata
- Authors
- Pinson, P., Nielsen, H. A., Møller, J. K., Madsen, H., & Kariniotakis, G. N.
- Venue
- Wind Energy
- Publisher
- Wiley
- Published
- 2007-05-21
- Volume
- 10
- Issue
- 6
- Pages
- 497-516
- ISSN
- 1095-4244 (ISSN-L)
- 1099-1824
- OpenAlex
- W2124640139
- Cited by
- 300
- References
- 27
- FWCI
- 7.88
- Type
- journal article
- Language
- en
- Open access
- closed
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