Development of a Predictive Model for a Photovoltaic Module’s Surface Temperature

Dong Eun Jung, Chanuk Lee, Kee Han Kim and Sung Lok Do
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Dong Eun Jung: Department of Building and Plant Engineering, Hanbat National University, Daejeon 34158, Korea
Chanuk Lee: Department of Building and Plant Engineering, Hanbat National University, Daejeon 34158, Korea
Kee Han Kim: Department of Architectural Engineering, Ulsan University, Ulsan 44610, Korea
Sung Lok Do: Department of Building and Plant Engineering, Hanbat National University, Daejeon 34158, Korea

Energies, 2020, vol. 13, issue 15, 1-18

Abstract: PV (photovoltaic) systems are receiving the spotlight in Korea due to the Renewable Energy 3020 Implementation Plan (RE3020), which has the goal of reaching 20% for the proportion of renewable energy generation by 2030. Accordingly, the actual performance evaluation of PV systems to achieve the RE3020 has become more important. PV efficiency is mainly determined by various weather conditions (e.g., solar radiation) that affect the power generation of PV systems. However, the efficiency is also affected by changes in module surface temperature. In particular, the efficiency decreases when the module surface temperature rises. That is, the actual PV efficiency falls short of the rated efficiency. The estimation of module surface temperature is critical for evaluating the actual performance of PV systems. Many studies have been conducted to calculate the surface temperature. However, most of the previous studies focused on calculations of current surface temperatures using current environment data, which means that the previous studies have limitations related to timestep. That is, there is a lack of predictive models that calculate the future surface temperatures by using the current measured data. Therefore, this study developed a predictive model using an ANN (artificial neural network) algorithm to determine the surface temperature of PV modules for a future period of time. Then, this study evaluated the actual performance (i.e., power generation) with the predicted surface temperatures.

Keywords: photovoltaic system; efficiency; power generation; module surface temperature; predictive model; artificial neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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