Investigating the performance of support vector machine and artificial neural networks in predicting solar radiation on a tilted surface: Saudi Arabia case study

Energy Conversion and Management

Volumn 105, Issue , 2015, Pages 442-452

  Ramli, Makbul A M ^a   Twaha, Ssennoga ^b   Al Turki, Yusuf A ^a  

^a KING ABDULAZIZ UNIVERSITY   (Saudi Arabia)

^b UNIVERSITY OF NOTTINGHAM   (United Kingdom)

Author keywords

Artificial neural networks; Prediction; Solar radiation; Super vector machine

Indexed keywords

ERRORS; FORECASTING; MEAN SQUARE ERROR; NEURAL NETWORKS; PHOTOVOLTAIC CELLS; RADIATION; SOLAR RADIATION;

COEFFICIENT OF CORRELATION; GLOBAL SOLAR RADIATION DATA; HORIZONTAL SURFACES; ROOT MEAN SQUARE ERRORS; SOLAR RADIATION DATA; STATISTICAL MEASURES; SUPER VECTOR MACHINE; TRAINING AND TESTING;

SUPPORT VECTOR MACHINES;

EID: 84939246688     PISSN: 01968904     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.enconman.2015.07.083     Document Type: Article

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