Artificial neural network-based photovoltaic module temperature estimation for tropical climate of Malaysia and its impact on photovoltaic system energy yield
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 Ta, solar irradiation G,...
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my.utm.578952021-12-06T03:35:23Z http://eprints.utm.my/id/eprint/57895/ Artificial neural network-based photovoltaic module temperature estimation for tropical climate of Malaysia and its impact on photovoltaic system energy yield Almaktar, Mohamed Rahman, Hasimah Abdul Hassan, Mohammad Yusri Saeh, Ibrahim TK Electrical engineering. Electronics Nuclear 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 Ta, 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 Tm. 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. 2015 Article PeerReviewed Almaktar, Mohamed and Rahman, Hasimah Abdul and Hassan, Mohammad Yusri and Saeh, Ibrahim (2015) Artificial neural network-based photovoltaic module temperature estimation for tropical climate of Malaysia and its impact on photovoltaic system energy yield. Progress in Photovoltaics, 23 (3). pp. 302-318. ISSN 1062-7995 http://dx.doi.org/10.1002/pip.2424 DOI:10.1002/pip.2424 |
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TK Electrical engineering. Electronics Nuclear engineering Almaktar, Mohamed Rahman, Hasimah Abdul Hassan, Mohammad Yusri Saeh, Ibrahim Artificial neural network-based photovoltaic module temperature estimation for tropical climate of Malaysia and its impact on photovoltaic system energy yield |
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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 Ta, 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 Tm. 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. |
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Article |
author |
Almaktar, Mohamed Rahman, Hasimah Abdul Hassan, Mohammad Yusri Saeh, Ibrahim |
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Almaktar, Mohamed Rahman, Hasimah Abdul Hassan, Mohammad Yusri Saeh, Ibrahim |
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Almaktar, Mohamed |
title |
Artificial neural network-based photovoltaic module temperature estimation for tropical climate of Malaysia and its impact on photovoltaic system energy yield |
title_short |
Artificial neural network-based photovoltaic module temperature estimation for tropical climate of Malaysia and its impact on photovoltaic system energy yield |
title_full |
Artificial neural network-based photovoltaic module temperature estimation for tropical climate of Malaysia and its impact on photovoltaic system energy yield |
title_fullStr |
Artificial neural network-based photovoltaic module temperature estimation for tropical climate of Malaysia and its impact on photovoltaic system energy yield |
title_full_unstemmed |
Artificial neural network-based photovoltaic module temperature estimation for tropical climate of Malaysia and its impact on photovoltaic system energy yield |
title_sort |
artificial neural network-based photovoltaic module temperature estimation for tropical climate of malaysia and its impact on photovoltaic system energy yield |
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2015 |
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http://eprints.utm.my/id/eprint/57895/ http://dx.doi.org/10.1002/pip.2424 |
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1718926022757318656 |