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• W2017108778 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." @default.
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• W2017108778 date "2014-08-01" @default.
• W2017108778 modified "2023-10-03" @default.
• W2017108778 title "Analysis of daily solar power prediction with data-driven approaches" @default.
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• W2017108778 doi "https://doi.org/10.1016/j.apenergy.2014.03.084" @default.
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