A two-step short-term probabilistic wind forecasting methodology based on predictive distribution optimization. (15th March 2019)

• Record Type: Journal Article
• Authors: Sun, Mucun; Feng, Cong; Chartan, Erol Kevin; Hodge, Bri-Mathias; Zhang, Jie
• Is Part Of: Applied energy. Volume 238(2019)
• Page Start: 1497
• Page End: 1505
• Publication Date: 2019-03-15
• Subjects: Probabilistic wind forecasting -- Optimization -- Surrogate model -- Machine learning -- Pinball loss; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2019.01.182
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: A pinball loss optimization based probabilistic forecasting method is developed. The best shape of a predictive distribution is explored and optimized. The proposed method reduces pinball loss by up to 35% compared to baselines. Abstract: With increasing wind penetrations into electric power systems, probabilistic wind forecasting becomes more critical to power system operations because of its capability of quantifying wind uncertainties. In this paper, a two-step probabilistic wind forecasting approach based on pinball loss optimization is developed. First, a multimodel machine learning-based ensemble deterministic forecasting framework is adopted to generate deterministic forecasts. The deterministic forecast is assumed to be the mean value of the predictive distribution at each forecasting time stamp. Then, the optimal unknown parameter (i.e., standard deviation) of the predictive distribution is estimated by a support vector regression surrogate model based on the deterministic forecasts. Finally, probabilistic forecasts are generated from the predictive distribution. Numerical results of case studies at eight locations show that the developed two-step probabilistic forecasting methodology has improved the pinball loss metric score by up to 35% compared to a baseline quantile regression forecasting model.