In this work, four artificial intelligence (AI) techniques, based on Artificial Neural Networks, Support Vector Machine (SVM), and Regression Tree Ensembles, were used to estimate the operating temperature of photovoltaic (PV) modules (TPV). The models' input parameters correspond to experimental measurements of environmental (solar radiation, ambient temperature, relative humidity, wind speed, and wind direction) and operational (power output and tracking system) variables. Several AI models were trained and statistically compared with the measured data using a computational methodology that determines the performance and accuracy of the AI technique. Finally, a global sensitivity analysis was conducted to identify the ability of each technique to reflect the physical coherence of the phenomenon that is under study. It is reported that the four techniques can provide an estimate having a precision of about 93%. On the other hand, the sensitivity analysis demonstrates that all the models cannot correctly interpret the physical interaction of the input parameters with respect to TPV, where the SVM is reported to be the most appropriate. The results indicate that the proposed methodology is a viable alternative for the estimation of TPV by AI techniques. This methodology can be implemented as an alternative tool in the development of smart PV module cooling systems to improve its performance and to reduce its operating costs.
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Research Article|
May 16 2018
Estimation of the operating temperature of photovoltaic modules using artificial intelligence techniques and global sensitivity analysis: A comparative approach
O. May Tzuc
;
O. May Tzuc
a)
1
Facultad de Ingeniería, Universidad Autónoma de Yucatán, Av. Industrias No Contaminantes
, Apo. Postal 150 Mérida, Yucatán, México
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A. Bassam
;
A. Bassam
a)
1
Facultad de Ingeniería, Universidad Autónoma de Yucatán, Av. Industrias No Contaminantes
, Apo. Postal 150 Mérida, Yucatán, México
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P. E. Mendez-Monroy
;
P. E. Mendez-Monroy
2
IIMAS-Mérida Universidad Nacional Autónoma de México, Parque Científico y Tecnológico de Yucatán km. 55 Carretera Sierra Papacal-Chuburná
, CP 97320 Sierra Papacal, Yucatán, México
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I. Sanchez Dominguez
I. Sanchez Dominguez
2
IIMAS-Mérida Universidad Nacional Autónoma de México, Parque Científico y Tecnológico de Yucatán km. 55 Carretera Sierra Papacal-Chuburná
, CP 97320 Sierra Papacal, Yucatán, México
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J. Renewable Sustainable Energy 10, 033503 (2018)
Article history
Received:
November 27 2017
Accepted:
April 26 2018
Citation
O. May Tzuc, A. Bassam, P. E. Mendez-Monroy, I. Sanchez Dominguez; Estimation of the operating temperature of photovoltaic modules using artificial intelligence techniques and global sensitivity analysis: A comparative approach. J. Renewable Sustainable Energy 1 May 2018; 10 (3): 033503. https://doi.org/10.1063/1.5017520
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