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- W2885207064 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 point-forecasted 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." @default.
- W2885207064 created "2018-08-22" @default.
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- W2885207064 date "2018-06-01" @default.
- W2885207064 modified "2023-09-23" @default.
- W2885207064 title "Probabilistic Short-term Wind Forecasting Based on Pinball Loss Optimization" @default.
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- W2885207064 doi "https://doi.org/10.1109/pmaps.2018.8440347" @default.
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