Prediction of daily global solar irradiation data using Bayesian neural network: A comparative study

Renewable Energy

Volumn 48, Issue , 2012, Pages 146-154

  Yacef, R ^a   Benghanem, M ^b   Mellit, A ^a  

^a UNIVERSITY OF JIJEL   (Algeria)

^b TAIBAH UNIVERSITY   (Saudi Arabia)

Author keywords

Bayesian neural network; Daily global solar irradiation; Empirical models; Neural network; Prediction

Indexed keywords

AIR TEMPERATURE; AUTOMATIC RELEVANCE DETERMINATION; BAYESIAN NEURAL NETWORKS; CLASSICAL NEURAL NETWORKS; COMPARATIVE STUDIES; EMPIRICAL MODEL; GLOBAL SOLAR IRRADIATION; INPUT PARAMETER; OPTIMUM INPUT; SAUDI ARABIA; SUNSHINE DURATION;

FORECASTING; SOLAR RADIATION;

NEURAL NETWORKS;

AIR TEMPERATURE; ARTIFICIAL NEURAL NETWORK; BAYESIAN ANALYSIS; CLIMATE PREDICTION; DATA SET; DATABASE; EMPIRICAL ANALYSIS; ENERGY RESOURCE; EXPERIMENTAL STUDY; IRRADIANCE; NUMERICAL MODEL; RELATIVE HUMIDITY; RENEWABLE RESOURCE; SOLAR RADIATION;

MADINAH; SAUDI ARABIA;

EID: 84861437871     PISSN: 09601481     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.renene.2012.04.036     Document Type: Article

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