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Analysis of daily solar power prediction with data-driven approaches
Long H.1; Zhang Z.1; Su Y.2
Source PublicationApplied Energy

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.

KeywordArtificial Neural Network (Ann) Data Mining Solar Power Prediction Support Vector Machine (Svm) Time-series Model
URLView the original
Indexed BySCI
WOS Research AreaEnergy & Fuels ; Engineering
WOS SubjectEnergy & Fuels ; Engineering, Chemical
WOS IDWOS:000337651100004
The Source to ArticleScopus
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Cited Times [WOS]:40   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
Affiliation1.Department of Systems Engineering and Engineering Management, City University of Hong Kong, P6600, 6/F, Academic 1, Hong Kong
2.Department of Electromechanical Engineering, University of Macau, Macau
Recommended Citation
GB/T 7714
Long H.,Zhang Z.,Su Y.. Analysis of daily solar power prediction with data-driven approaches[J]. Applied Energy,2014,126:29.
APA Long H.,Zhang Z.,&Su Y..(2014).Analysis of daily solar power prediction with data-driven approaches.Applied Energy,126,29.
MLA Long H.,et al."Analysis of daily solar power prediction with data-driven approaches".Applied Energy 126(2014):29.
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