Abstract
Probabilistic wind power forecasts that quantify the uncertainty in wind output have the potential to aid in the economic grid integration of wind power at large penetration levels. In this paper, a novel probabilistic wind forecasting approach based on pinball loss optimization is proposed, in conjunction with a multi-model machine learning based ensemble deterministic forecasting framework. By assuming the pointforecasted value as the mean at each point, one unknown parameter (i.e., standard deviation) of a predictive distribution at each forecasting point is determined by minimizing the pinball loss. A surrogate model is developed to represent the unknown distribution parameter as a function of deterministic forecasts. This surrogate model can be used together with deterministic forecasts to predict the unknown distribution parameter and thereby generate probabilistic forecasts. Numerical results of case studies show that the proposed method has improved the pinball loss by up to 35% compared to a baseline quantile regression forecasting model.
Original language | American English |
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Number of pages | 8 |
State | Published - 2018 |
Event | 2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) - Boise, Idaho Duration: 24 Jun 2018 → 28 Jun 2018 |
Conference
Conference | 2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) |
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City | Boise, Idaho |
Period | 24/06/18 → 28/06/18 |
Bibliographical note
See NREL/CP-5D00-72544 for paper as published in IEEE proceedingsNREL Publication Number
- NREL/CP-5D00-71247
Keywords
- machine learning
- optimization
- pinball loss
- probabilistic wind forecasting
- surrogate model