Analysis of daily solar power prediction with data-driven approaches

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

105 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)29-37
Journal / PublicationApplied Energy
Volume126
Online published23 Apr 2014
Publication statusPublished - 1 Aug 2014

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. © 2014 Elsevier Ltd.

Research Area(s)

  • Artificial neural network (ANN), Data mining, Solar power prediction, Support vector machine (SVM), Time-series model