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Title: Probabilistic Short-Term Wind Forecasting Based on Pinball Loss Optimization

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.

Authors:
 [1];  [1];  [2];  [2];  [2]
  1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
  2. University of Texas at Dallas
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Wind and Water Technologies Office (EE-4W)
OSTI Identifier:
1476243
Report Number(s):
NREL/CP-5D00-72544
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at the 2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), 24-28 June 2018, Boise, Idaho
Country of Publication:
United States
Language:
English
Subject:
17 WIND ENERGY; probabilistic wind forecasting; optimization; surrogate model; machine learning; pinball loss

Citation Formats

Chartan, Erol Kevin, Hodge, Brian S, Sun, Mucun, Feng, Cong, and Zhang, Jie. Probabilistic Short-Term Wind Forecasting Based on Pinball Loss Optimization. United States: N. p., 2018. Web. doi:10.1109/PMAPS.2018.8440347.
Chartan, Erol Kevin, Hodge, Brian S, Sun, Mucun, Feng, Cong, & Zhang, Jie. Probabilistic Short-Term Wind Forecasting Based on Pinball Loss Optimization. United States. doi:10.1109/PMAPS.2018.8440347.
Chartan, Erol Kevin, Hodge, Brian S, Sun, Mucun, Feng, Cong, and Zhang, Jie. Mon . "Probabilistic Short-Term Wind Forecasting Based on Pinball Loss Optimization". United States. doi:10.1109/PMAPS.2018.8440347.
@article{osti_1476243,
title = {Probabilistic Short-Term Wind Forecasting Based on Pinball Loss Optimization},
author = {Chartan, Erol Kevin and Hodge, Brian S and Sun, Mucun and Feng, Cong and Zhang, Jie},
abstractNote = {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.},
doi = {10.1109/PMAPS.2018.8440347},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {8}
}

Conference:
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