Journal
APPLIED ENERGY
Volume 126, Issue -, Pages 29-37Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2014.03.084
Keywords
Solar power prediction; Time-series model; Data mining; Artificial Neural Network (ANN); Support Vector Machine (SVM)
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Funding
- Early Career Scheme Project Grant of Hong Kong Research Grant Council [CityU-138313]
- Science and Technology Development Fund of Macau [FDCT/115/2012/A]
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Daily solar power prediction using data-driven approaches is studied. Four famous data-driven approaches, the Artificial Neural Network (ANN), the Support Vector Machine (SVM), the k-nearest neighbor (kNN), and the multivariate linear regression (MLR), are applied to develop the prediction models. The persistent model is considered as a baseline for evaluating the effectiveness of data-driven approaches. A procedure of selecting input parameters for solar power prediction models is addressed. Two modeling scenarios, including and excluding meteorological parameters as inputs, are assessed in the model development. A comparative analysis of the data-driven algorithms is conducted. The capability of data-driven models in multi-step ahead prediction is examined. The computational results indicate that none of the algorithms can outperform others in all considered prediction scenarios. (C) 2014 Elsevier Ltd. All rights reserved.
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