Multi-distribution ensemble probabilistic wind power forecasting. (April 2020)

• Record Type: Journal Article
• Authors: Sun, Mucun; Feng, Cong; Zhang, Jie
• Is Part Of: Renewable energy. Volume 148(2020)
• Page Start: 135
• Page End: 149
• Publication Date: 2020-04
• Subjects: Probabilistic wind power forecasting -- Optimization -- Surrogate model -- Pinball loss -- Ensemble forecasting; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2019.11.145
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: Ensemble methods have shown to be able to improve the performance of deterministic wind forecasting. In this paper, an improved multi-distribution ensemble (MDE) probabilistic wind power forecasting framework is developed to explore the advantages of different predictive distributions. Both competitive and cooperative strategies are applied to the developed MDE framework to generate 1–6 h ahead and day-ahead probabilistic wind power forecasts. Three probabilistic forecasting models based on Gaussian, gamma, and laplace predictive distributions are adopted to form the ensemble model. The parameters of the ensemble model (i.e., weights and standard deviations) are optimized by minimizing the pinball loss at the training stage. A set of surrogate models are built to quantify the relationship between the unknown optimal parameters and deterministic forecasts, which can be used for online forecasting. The effectiveness of the proposed MDE framework is validated by using the Wind Integration National Dataset (WIND) Toolkit. Numerical results of case studies at seven locations show that the developed MDE probabilistic forecasting methodology has improved the pinball loss metric score by up to 20.5% compared to the individual-distribution models and benchmark ensemble models. Highlights: Develop an ensemble probabilistic wind power forecasting framework. Explore both competitive and cooperative ensemble strategies. Explore probabilistic forecasting accuracies at different forecasting horizons. Reduce pinball loss by up to 20.5% compared to benchmark models.