The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Conditional Prediction Intervals of Wind Power Generation
Open accessAbstract
A generic method for the providing of prediction intervals of wind power generation is described. Prediction intervals complement the more common wind power point forecasts, by giving a range of potential outcomes for a given probability, their so-called nominal coverage rate. Ideally they inform of the situation-specific uncertainty of point forecasts. In order to avoid a restrictive assumption on the shape of forecast error distributions, focus is given to an empirical and nonparametric approach named adapted resampling. This approach employs a fuzzy inference model that permits to integrate expertise on the characteristics of prediction errors for providing conditional interval forecasts. By simultaneously generating prediction intervals with various nominal coverage rates, one obtains full predictive distributions of wind generation. Adapted resampling is applied here to the case of an onshore Danish wind farm, for which three point forecasting methods are considered as input. The probabilistic forecasts generated are evaluated based on their reliability and sharpness, while compared to forecasts based on quantile regression and the climatology benchmark. The operational application of adapted resampling to the case of a large number of wind farms in Europe and Australia among others is finally discussed.
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Cites
An Introduction to the Bootstrap
Efron, B., & Tibshirani, R. J. (1994) · doi:10.1201/9780429246593 · Cited by 40,500
Adaptive Fuzzy Systems and Control: Design and Stability Analysis
Wang, L.-X. (1994) · Medical Entomology and Zoology · Cited by 3,535
The Analysis of Time Series: An Introduction
Dickey, D. A., & Chatfield, C. (1991) · Technometrics · doi:10.2307/1268794 · Cited by 3,525
The Analysis of Time Series: An Introduction with R
Chatfield, C., & Xing, H. (2019) · Chapman & Hall/CRC eBooks · doi:10.1201/9781351259446 · Cited by 1,803
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
Adaptive fuzzy systems and control
Pedrycz, W. (1994) · Control Engineering Practice · doi:10.1016/0967-0661(94)91882-1 · Cited by 883
Combining Probability Distributions From Experts in Risk Analysis
Clemen, R. T., & Winkler, R. L. (1999) · Risk Analysis · doi:10.1111/j.1539-6924.1999.tb00399.x · Cited by 816
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
A review on the young history of the wind power short-term prediction
Costa, A., Crespo, A., Navarro, J., Lizcano, G., Madsen, H., & Feitosa, E. (2008) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2007.01.015 · Cited by 638
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
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Cited by
Robust Energy Management for Microgrids With High-Penetration Renewables
Zhang, Y., Gatsis, N., & Giannakis, G. B. (2013) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2013.2255135 · Cited by 798
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
Wind Energy: Forecasting Challenges for Its Operational Management
Pinson, P. (2013) · Statistical Science · doi:10.1214/13-sts445 · Cited by 377
Self-citationZhang, C., Zhao, Y., Fan, C., Li, T., Zhang, X., & Li, J. (2020) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2020.115261 · Cited by 79
Metadata
- Authors
- Pinson, P., & Kariniotakis, G.
- Venue
- IEEE Transactions on Power Systems
- Published
- 2010-11
- Volume
- 25
- Issue
- 4
- Pages
- 1845-1856
- ISSN
- 0885-8950 (ISSN-L)
- 1558-0679
- OpenAlex
- W2156604062
- Cited by
- 329
- References
- 24
- FWCI
- 10.96
- 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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