Analysis of daily solar power prediction with data-driven approaches

• We develop daily solar power prediction models with data-driven approaches.
• We introduce a parameter selection procedure for reducing dimensions of prediction models.
• We report a comparative analysis of data mining algorithms in daily solar power prediction.
• Data mining algorithms can perform better than persistent methods.
• None of data mining algorithms can dominate others in all prediction scenarios.

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.