Adaptive image features for intra-hour solar forecasts

作     者：Pedro, Hugo T. C. Coimbra, Carlos F. M. Lauret, Philippe 

作者机构：Univ Calif San Diego Ctr Energy Res Dept Mech & Aerosp Engn La Jolla CA 92093 USA Univ La Reunion 15 Ave Rene Cassin F-97715 St Denis 9 Reunion France 

出 版 物：《JOURNAL OF RENEWABLE AND SUSTAINABLE ENERGY》 (可再生与可持续能源杂志)

年 卷 期：2019年第11卷第3期

页      面：036101-036101页

核心收录：

学科分类：0820[工学-石油与天然气工程] 080703[工学-动力机械及工程] 08[工学] 0807[工学-动力工程及工程热物理] 

主　　题：Atmospheric radiation Algorithms and data structure Machine learning Solar energy Computational methods Solar irradiance Prediction theory Optimization algorithms Descriptive statistics Sensors 

摘      要：We introduce a simple and novel technique to extract dynamic features from sky images in order to increase the accuracy of intrahour forecasts for both Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI) values. The proposed methodology is based on a block-matching algorithm that correctly identifies the bulk motion of clouds relative to the position of the Sun in the sky. Adaptive rectangular- and wedge-shaped Regions Of Interest are used to select the image pixels for the new features. The results show an average increase of 6.8% (6.7%) in forecast skill for GHI (DNI) across all horizons tested as measured against a model with global (nonadaptive) image features. Relative to clear-sky persistence, the new model achieves skills ranging from 20% to 30% (22%-35%) for GHI (DNI), among the highest ever reported for these time horizons. An analysis based on Mutual Information and Pearson correlation coefficients between the image features and the training data reveals overall improvements in all metrics. The proposed adaptive method also improves the predictability of the ramp magnitude and direction.