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Open AccessArticle

Temperature Estimation for Photovoltaic Array Using an Adaptive Neuro Fuzzy Inference System

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Facultad de Ingeniería, Universidad Autónoma de Yucatán, Av. Industrias no Contaminantes, Apdo. Postal 150 Mérida, Yucatán, Mexico
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Posgrado en Energías Renovables, Facultad de Ingeniería, Universidad Autónoma de Yucatán, Av. Industrias no Contaminantes, Apdo. Postal 150 Mérida, Yucatán, Mexico
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Author to whom correspondence should be addressed.
Sustainability 2017, 9(8), 1399; https://doi.org/10.3390/su9081399
Received: 8 July 2017 / Revised: 30 July 2017 / Accepted: 4 August 2017 / Published: 15 August 2017
(This article belongs to the Special Issue Solar Photovoltaic Electricity)
Module temperature is an important parameter of photovoltaic energy systems since their performance is affected by its variation. Several cooling controllers require a precise estimation of module temperature to reduce excessive heating and power losses. In this work, an adaptive neuro fuzzy inference system technique is developed for temperature estimation of photovoltaic systems. For the learning process, experimental measurements comprising six environmental variables (temperature, irradiance, wind velocity, wind direction, relative humidity, and atmospheric pressure) and one operational variable (photovoltaic power output) were used as training parameters. The proposed predictive model comprises a zero-order Sugeno neuro fuzzy system with two generalized bell-shaped membership functions per input and 128 fuzzy rules. The model is validated with experimental information from an instrumented photovoltaic system with a fitness correlation parameter of R = 95%. The obtained results indicate that the proposed methodology provides a reliable tool for estimation of modules temperature based on environmental variables. The developed algorithm can be implemented as part of a cooling control system of photovoltaic modules to reduce the efficiency losses. View Full-Text
Keywords: solar energy; temperature photovoltaic cell; photovoltaic performance; sensitivity analysis; artificial intelligence modeling solar energy; temperature photovoltaic cell; photovoltaic performance; sensitivity analysis; artificial intelligence modeling
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MDPI and ACS Style

Bassam, A.; May Tzuc, O.; Escalante Soberanis, M.; Ricalde, L.J.; Cruz, B. Temperature Estimation for Photovoltaic Array Using an Adaptive Neuro Fuzzy Inference System. Sustainability 2017, 9, 1399.

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