Analysis of daily solar power prediction with data-driven approaches

Huan Long, Zijun Zhang and Yan Su

Applied Energy, 2014, vol. 126, issue C, 29-37

Abstract: 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.

Keywords: Solar power prediction; Time-series model; Data mining; Artificial Neural Network (ANN); Support Vector Machine (SVM) (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (29)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:126:y:2014:i:c:p:29-37

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DOI: 10.1016/j.apenergy.2014.03.084

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