Performance evaluation of photovoltaic systems on Kuwaiti schools’ rooftop

Al-Otaibi, Ahmad; Al-Qattan, Ayman; Fairouz, Fatma; Al-Mulla, A.

doi:10.1016/j.enconman.2015.02.039

Cited by 121 publications

(61 citation statements)

References 20 publications

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“…During the other two days we observed that the solar irradiation has an irregular shape and the wind speed is greater in comparison with the clear day, which explains the small difference between the ambient temperature and the temperatures of the modules. This means that the modules cool down fairly quickly with the help of wind speed, these results are coherent with the results reported in Al-Otaibi et al [16].…”

Section: Methodssupporting

confidence: 82%

Performance Analysis and Comparison of Different Photovoltaic Modules Technologies under Different Climatic Conditions in Casablanca

Karami¹,

Rafi²,

Haibaoui³

et al. 2017

J Fundam Renewable Energy Appl

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Section: Methodssupporting

confidence: 82%

Performance Analysis and Comparison of Different Photovoltaic Modules Technologies under Different Climatic Conditions in Casablanca

Karami¹,

Rafi²,

Haibaoui³

et al. 2017

J Fundam Renewable Energy Appl

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“…The results revealed that the performance ratio varies between 79% and 86.5% for the period of March to October 2012. Another relevant investigation is that of Al-Otaibi et al [24]. They analyzed a twelve-month-long performance of the 80.05 kWp and 21.6 kWp CIGS grid-connected PV systems in Kuwait; the findings indicated that the performance ratio was maintained between 74% and 85% while the annual average daily final yield of the PV systems was 4.5 kW h/kWp day.…”

Section: Introductionmentioning

confidence: 97%

Comparative performance investigation of mono- and poly-crystalline silicon photovoltaic modules for use in grid-connected photovoltaic systems in dry climates

2015

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“…Several research groups from variable countries (Turkey, 6 Spain, 7 United Arab Emirates (UAE), 8 Oman, 9 and Kuwait 10,11 ) conducted an economic, technical evaluation of a PV system connected to the network. The researchers presented an analytical presentation of weather data for the area where the system has been constructed.…”

Section: Introductionmentioning

confidence: 99%

Experimental and deep learning artificial neural network approach for evaluating grid-connected photovoltaic systems

et al. 2019

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Summary This article evaluates a 1.4‐kW building integrated grid‐connected photovoltaic plant. The PV plant was installed in the Faculty of Engineering solar energy lab, Sohar University, Oman, and the system data have been collected for a year from July 2017 to June 2018. The grid‐connected system was evaluated in terms of power, energy, specific yield, capacity factor, and cost of energy, and payback period. The measured diffuse and global solar irradiations are 3289 and 6182 Wh/m2, respectively. Four predictive models (TLRN, FRNN‐1, FRNN‐2, and FRNN‐3) using deep learning approach based on RNN and TLRN were proposed to predict the PV current performance through data input of temperature (T) and solar irradiance (G). The experiment results found that the highest energy production, array, reference, and final yields are 245.8 kWh, 3.43 to 5.65 kWh/kWp‐day, 4.61 to 7.33 kWh/kWp‐day, and 3.24 to 4.82 kWh/kWp‐day, respectively. Meanwhile, CF, CoE, and PBP were found to be 21.7%, 0.045 USD/kWh and 11.17 years, respectively. The highest performance for prediction models were found for FRNN‐2 and FRNN‐3 due to they exhibit lower MSE which means being tightly fitted to experiments.

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Performance evaluation of photovoltaic systems on Kuwaiti schools’ rooftop

Cited by 121 publications

References 20 publications

Performance Analysis and Comparison of Different Photovoltaic Modules Technologies under Different Climatic Conditions in Casablanca

Performance Analysis and Comparison of Different Photovoltaic Modules Technologies under Different Climatic Conditions in Casablanca

Comparative performance investigation of mono- and poly-crystalline silicon photovoltaic modules for use in grid-connected photovoltaic systems in dry climates

Experimental and deep learning artificial neural network approach for evaluating grid-connected photovoltaic systems

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