Random forest ensemble of support vector regression models for solar power forecasting
Random forest ensemble of support vector regression models for solar power forecasting
复制标题
用于太阳能预测的支持向量回归模型的随机森林集成
DOI：
10.1109/isgt.2017.8086027
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
B. Chowdhury
中科院分区：
文献类型：
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作者：
Mohamed Abuella;B. Chowdhury
为了减轻可变可再生资源的不确定性，部署了两种现成的机器学习工具来预测太阳能光伏系统的太阳能输出。支持向量机生成预测，随机森林充当集成学习方法来组合预测。风能和太阳能预测中常见的集合技术是混合多个来源的气象数据。但在这项研究中，来自多个模型的当前和过去的太阳能预测以及相关的气象数据被纳入随机森林中，以结合并提高日前太阳能预测的准确性。组合模型的性能在全年进行评估，并与其他组合技术进行比较。
To mitigate the uncertainty of variable renewable resources, two off-the-shelf machine learning tools are deployed to forecast the solar power output of a solar photovoltaic system. The support vector machines generate the forecasts and the random forest acts as an ensemble learning method to combine the forecasts. The common ensemble technique in wind and solar power forecasting is the blending of meteorological data from several sources. In this study though, the present and the past solar power forecasts from several models, as well as the associated meteorological data, are incorporated into the random forest to combine and improve the accuracy of the day-ahead solar power forecasts. The performance of the combined model is evaluated over the entire year and compared with other combining techniques.