Journal
APPLIED ENERGY
Volume 315, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2022.119015
Keywords
BIPV; PV module temperature; Module temperature estimation; Grammatical evolution; Differential evolution; Machine learning
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Funding
- Agencia Estatal de Investigacion (AEI-MICINN, Spain) [PID2019-104272RB-C55]
- Spanish MCIU/AEI/FEDER, UE [PGC2018-095322-B-C22]
- Agencia Estatal de Investigacion (FEDER funds)
- Comunidad de Madrid.. Fondos Estructurales de la Union Europea [P2018/TCS-4566]
- Fundacion Seneca -Region de Murcia (Spain) [21227/PD/19]
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This article presents an artificial intelligence-based approach for predicting the temperature of a photovoltaic module by considering both indoor and outdoor environmental factors. The results demonstrate the high accuracy of this method under different weather conditions.
Progress in development of building-integrated photovoltaic systems is still hindered by the complexity of the physics and materials properties of the photovoltaic (PV) modules and its effect on the thermal behavior of the building. This affects not only the energy generation, as its active function and linked to economic feasibility, but also the thermal insulation of the building as part of the structure's skin. Traditional modeling methods currently presents limitations, including the fact that they do not account for material thermal inertia and that the proposed semi-empirical coefficients do not define all types of technologies, mounting configuration, or climatic conditions. This article presents an artificial intelligence-based approach for predicting the temperature of a poly-crystalline silicon PV module based on local outdoor weather conditions (ambient temperature, solar irradiation, relative outdoor humidity and wind speed) and indoor comfort parameters (indoor temperature and indoor relative humidity) as inputs. A combination of two algorithms (Grammatical Evolution and Differential Evolution) guides to the creation of a customized expression based on the Sandia model. Different data-sets for a fully integrated PV system were tested to demonstrate its performance on three different types of days: sunny, cloudy and diffuse, showing relative errors of less than 4% in all cases and including night time. In comparison to Sandia model, this method reduces the error by up to 11% in conditions of variability of sky over short time intervals (cloudy days).
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