期刊
SOLAR ENERGY
卷 150, 期 -, 页码 423-436出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.solener.2017.04.066
关键词
Solar power forecasting; Multi-site forecasting; Spatio-temporal forecasting; Regression trees; Gradient boosting; Machine learning
The challenges to optimally utilize weather dependent renewable energy sources call for powerful tools for forecasting. This paper presents a non-parametric machine learning approach used for multi-site prediction of solar power generation on a forecast horizon of one to six hours. Historical power generation and relevant meteorological variables related to 42 individual PV rooftop installations are used to train a gradient boosted regression tree (GBRT) model. When compared to single-site linear autoregressive and variations of GBRT models the multi-site model shows competitive results in terms of root mean squared error on all forecast horizons. The predictive performance and the simplicity of the model setup make the boosted tree model a simple and attractive compliment to conventional forecasting techniques. (C) 2017 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