Very Short-Term Nonparametric Probabilistic Forecasting of Renewable Energy Generation-With Application to Solar Energy

被引:239
作者
Golestaneh, Faranak [1 ]
Pinson, Pierre [2 ]
Gooi, H. B. [1 ]
机构
[1] Nanyang Technol Univ, Dept Elect & Elect Engn, Singapore, Singapore
[2] Tech Univ Denmark, Dept Elect Engn, Ctr Elect Power & Energy, Lyngby, Denmark
基金
新加坡国家研究基金会;
关键词
Extreme machine learning; forecasting; quantile regression; solar power; uncertainty quantification; EXTREME LEARNING-MACHINE; PREDICTION INTERVALS; POWER;
D O I
10.1109/TPWRS.2015.2502423
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
080906 [电磁信息功能材料与结构]; 082806 [农业信息与电气工程];
摘要
Due to the inherent uncertainty involved in renewable energy forecasting, uncertainty quantification is a key input to maintain acceptable levels of reliability and profitability in power system operation. A proposal is formulated and evaluated here for the case of solar power generation, when only power and meteorological measurements are available, without sky-imaging and information about cloud passages. Our empirical investigation reveals that the distribution of forecast errors do not follow any of the common parametric densities. This therefore motivates the proposal of a nonparametric approach to generate very short-term predictive densities, i.e., for lead times between a few minutes to one hour ahead, with fast frequency updates. We rely on an Extreme Learning Machine (ELM) as a fast regression model, trained in varied ways to obtain both point and quantile forecasts of solar power generation. Four probabilistic methods are implemented as benchmarks. Rival approaches are evaluated based on a number of test cases for two solar power generation sites in different climatic regions, allowing us to show that our approach results in generation of skilful and reliable probabilistic forecasts in a computationally efficient manner.
引用
收藏
页码:3850 / 3863
页数:14
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