2013
DOI: 10.1016/j.enconman.2012.08.003
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Artificial Neural Network based control for PV/T panel to track optimum thermal and electrical power

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Cited by 72 publications
(27 citation statements)
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“…As the electricity efficiency is about 5-19%, most solar energy is converted into thermal energy and results in high temperature [1,2]. Accurate prediction of the high temperature is necessary for evaluating PV performances, such as the PV efficiency [3,4], stress distribution [5] and life cycle assessment [6,7]. In this case, thermal models are indispensable to predict temperature characteristics and values of PV panels.…”
Section: Introductionmentioning
confidence: 99%
“…As the electricity efficiency is about 5-19%, most solar energy is converted into thermal energy and results in high temperature [1,2]. Accurate prediction of the high temperature is necessary for evaluating PV performances, such as the PV efficiency [3,4], stress distribution [5] and life cycle assessment [6,7]. In this case, thermal models are indispensable to predict temperature characteristics and values of PV panels.…”
Section: Introductionmentioning
confidence: 99%
“…The two similar neural based MPPT techniques were presented in [23,24]. The performance of a PV module/panel is significantly affected by the environmental conditions such as dust.…”
Section: Introductionmentioning
confidence: 99%
“…3 Many researchers have worked on improving the PV/T system performance. Ammar et al 4 designed a network controller to optimize the electrical and thermal efficiency of PV/T system for different operation conditions. Spectral filtering has been successfully employed to increase the amount of sunlight that the conventional concentrated solar PV/T system can utilize.…”
Section: Introductionmentioning
confidence: 99%