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Artificial neural network‐based photovoltaic module temperature estimation for tropical climate of Malaysia and its impact on photovoltaic system energy yield
Author(s) -
Almaktar Mohamed,
Abdul Rahman Hasimah,
Hassan Mohammad Yusri,
Saeh Ibrahim
Publication year - 2015
Publication title -
progress in photovoltaics: research and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.286
H-Index - 131
eISSN - 1099-159X
pISSN - 1062-7995
DOI - 10.1002/pip.2424
Subject(s) - photovoltaic system , wind speed , artificial neural network , meteorology , relative humidity , environmental science , sunshine duration , mean squared error , correlation coefficient , computer science , statistics , engineering , mathematics , machine learning , geography , electrical engineering
This article presents an artificial neural network (ANN)‐based approach for predicting photovoltaic (PV) module temperature using meteorological variables. The proposed approach utilizes actual hourly records of various meteorological parameters, such as ambient temperature T a , solar irradiation G , relative humidity RH , and wind speed Ws as input variables. The hourly meteorological data were collected over 9 months in the year 2009 from a 92‐kWp installed PV system in Selangor, Malaysia. The data were divided into two sets: training data, which are a set of 1849 (April–October) hourly data, and 578 (November–December) hourly records of working as test data. Four ANN models have been developed by using different combination of meteorological parameters as inputs, and, for each model, the output is the PV module temperature T m . It was found that the model using all parameters, including RH and Ws as inputs, gave the most accurate results with correlation coefficient ( r ) 95.9%, and 0.41, 0.1, and 4.5% for MBE , RMSE , and MPE , respectively. To show the superiority and applicability of the developed ANN model, results from the proposed ANN model have been compared with the conventional model adopted by Malaysia Energy Center and another mathematical model based on regression. With the model's simplicity, the proposed approach can be used as an effective tool for predicting the PV module temperature, for any type of PV systems, in remote or rural locations with no direct measurement equipments. The developed model also will be very useful in studying PV system performance and estimating its energy output. Copyright © 2013 John Wiley & Sons, Ltd.