期刊
ENERGY
卷 219, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2020.119647
关键词
Solar photovoltaic power; Very short-term forecasting; Feature selection; Neural networks; Support vector regression; Random forests
This paper introduces a univariate data-driven method to improve the accuracy of very short-term electrical solar power forecasting by defining new features to efficiently tackle the nonlinear characteristics of electrical solar power and using instance-based variable selection to identify the best relevant features, thus significantly enhancing the performance of very short-term solar power forecasting.
Improving the accuracy of solar power forecasting has become crucial for dealing with the negative effects of the integration of continually increasing solar power into power systems. This is a more challenging task when historical solar radiation data has not been recorded and no specific sky imaging equipment is available. This paper proposes a univariate data-driven method to improve the accuracy of very short-term electrical solar power forecasting. This approach includes defining new features that efficiently tackle the nonlinear characteristics of electrical solar power. An instance-based variable selection is also used to identify the best relevant features. Three state-of-the-art learning algorithms (i.e. neural networks, support vector regression, and random forest) have been used and compared as prediction algorithms of the proposed method. The effectiveness of the proposed approach is evaluated in a 15-min ahead prediction trial using a real solar power dataset and against three evaluation measures. The results show the proposed method significantly enhances the performance of very short-term solar power forecasting. (C) 2020 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据