@inproceedings{67cc53ede4d34616b0fd1ebeee45d56f,
title = "Short-term global horizontal irradiance forecasting based on sky imaging and pattern recognition",
abstract = "Accurate short-term forecasting is crucial for solar integration in the power grid. In this paper, a classification forecasting framework based on pattern recognition is developed for 1-hour-ahead global horizontal irradiance (GHI) forecasting. Three sets of models in the forecasting framework are trained by the data partitioned from the preprocessing analysis. The first two sets of models forecast GHI for the first four daylight hours of each day. Then the GHI values in the remaining hours are forecasted by an optimal machine learning model determined based on a weather pattern classification model in the third model set. The weather pattern is determined by a support vector machine (SVM) classifier. The developed framework is validated by the GHI and sky imaging data from the National Renewable Energy Laboratory (NREL). Results show that the developed short-term forecasting framework outperforms the persistence benchmark by 16\% in terms of the normalized mean absolute error and 25\% in terms of the normalized root mean square error.",
keywords = "Classification, Pattern recognition, Sky imaging, Solar forecasting, Support vector machine",
author = "Cong Feng and Mingjian Cui and Meredith Lee and Jie Zhang and Hodge, \{Bri Mathias\} and Siyuan Lu and Hamann, \{Hendrik F.\}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE Power and Energy Society General Meeting, PESGM 2017 ; Conference date: 16-07-2017 Through 20-07-2017",
year = "2018",
month = jan,
day = "29",
doi = "10.1109/PESGM.2017.8274480",
language = "English",
series = "IEEE Power and Energy Society General Meeting",
publisher = "IEEE Computer Society",
pages = "1--5",
booktitle = "2017 IEEE Power and Energy Society General Meeting, PESGM 2017",
}