Experimental and numerical analysis of solar cell temperature transients

Bodnár, István; Matusz-Kalász, Dávid; Koós, Dániel

doi:10.1556/606.2020.00260

Cited by 2 publications

(3 citation statements)

References 33 publications

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“…According to the procedure of Duffie and Beckman [22], the laboratory of the "Hybrid Optimization of Multiple Energy Resources" (HOMER) uses a value of 0.9 for τα in equation (2). Furthermore, the PV module continuously works at its maximum power point efficiency, so the efficiency of the cell is always equal to the maximum power point efficiency as follows [5,17,22]:…”

Section: Methodsmentioning

confidence: 99%

“…The PV module efficiency at maximum power depends mainly on the cell temperature and can be written [5,17,22]: Standard geometrical equations are used to calculate angles of incidence (α fixed ) for fixed systems given surface tilt β, solar azimuth γ sun , azimuth γ, and angle of the solar zenith θ sun , as detailed in the following equation [23,24].…”

Section: Methodsmentioning

confidence: 99%

“…Many research studies use a cooling process to increase solar cells' lifetime and energy efficiency [10,[17][18][19]. As a result of the cooling, solar cells produced more power [4,5,10,[17][18][19]. However, the cooling system needs the energy to function.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The effects of climate change on photovoltaic solar production in hot regions

Al-Baghdadi¹,

Ridha²,

AL-Khayyat³

2022

Diagnostyka

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The work of solar cells and their production of electrical energy have been affected by climate change, especially in hot regions which became significantly hotter and still receive relatively high levels of solar radiation throughout the year. Higher ambient temperature and solar radiation result in higher PV cell temperature and, therefore, the reduction in PV module power output and efficiency. This study investigates how a PV module performs throughout the year in a hot region by considering the variations in cell temperature resulting from changes in ambient temperature and solar radiation every day. The tilt angles and two-axis tracking have also been examined. Results indicate that the two-axis solar tracking system is critical to use in hot regions for obtaining higher output power. Therefore, part of this power can be used to cool solar panels using various methods to keep their efficiency high, such as operating air fans or operating pumps to cool them with coolant.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

The effects of climate change on photovoltaic solar production in hot regions

Al-Baghdadi¹,

Ridha²,

AL-Khayyat³

2022

Diagnostyka

View full text Add to dashboard Cite

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An Empirical Approach to Solar Photovoltaic Cell Temperature Prediction

Chiteka,

Enweremadu

2024

Environmental and Climate Technologies

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Solar cell temperature is critical in the determination of solar energy generated by a solar photovoltaic power plant. High temperatures are associated with a reduction in the energy generated and hence prediction of photovoltaic cell temperature is essential in temperature mitigation and solar energy forecasting, especially in commercial power plants. The present study focused on the development of a hybrid machine learning based predictive model for solar photovoltaic cell temperature prediction in solar photovoltaic arrays. A physical experimental set up was developed to measure solar cell temperature under different weather and other related parameters. Satellite data were also collated for these parameters and were used to compliment experimental data used in this study. Satellite data used in the study were statistically transformed to mimic experimentally measured data. Feature selection and dimensionality reduction were performed to reduce the input variables and maintain relevant data in the modelling process. A solar cell temperature predictive model based on selected weather parameters was developed using a machine learning approach (Random Forests), and parameters used were selected from the statistical analysis. The prediction accuracy of the developed model was analysed using the coefficient of determination (R2) and the Mean Absolute Percentage Error (MAPE). The results indicated a higher model performance compared to generic models used in cell temperature prediction. The prediction MAPE for the developed model was 0.08 % while an R2 value of 0.99 was obtained which was indicative of a good model. The developed model was also comparable to other contemporary models developed to predict solar photovoltaic cell temperature. Simulations were also done to determine the annual energy generated with the incorporation of the solar cell temperature prediction model. The results revealed an average of 25.52 % daily energy difference between a simulation which considered solar cell temperature and that which ignored solar cell temperature.

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Experimental and numerical analysis of solar cell temperature transients

Cited by 2 publications

References 33 publications

The effects of climate change on photovoltaic solar production in hot regions

The effects of climate change on photovoltaic solar production in hot regions

An Empirical Approach to Solar Photovoltaic Cell Temperature Prediction

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