Hourly and Day Ahead Power Prediction of Building Integrated Semitransparent Photovoltaic System
The building integrated semitransparent photovoltaic (BISTPV) system is an emerging technology which replaces the conventional building material envelopes and roof. The performance prediction of the BISTPV system places a vital role in the reduction of the energy consumption in the building. In this...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | International Journal of Photoenergy |
| Online Access: | http://dx.doi.org/10.1155/2021/7894849 |
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| author | S. Kaliappan R. Saravanakumar Alagar Karthick P. Marish Kumar V. Venkatesh V. Mohanavel S. Rajkumar |
| author_facet | S. Kaliappan R. Saravanakumar Alagar Karthick P. Marish Kumar V. Venkatesh V. Mohanavel S. Rajkumar |
| author_sort | S. Kaliappan |
| collection | DOAJ |
| description | The building integrated semitransparent photovoltaic (BISTPV) system is an emerging technology which replaces the conventional building material envelopes and roof. The performance prediction of the BISTPV system places a vital role in the reduction of the energy consumption in the building. In this work, the artificial neural network (ANN) is used to predict the performance of this system by optimizing the important parameter of the feature selection. The Elman neural network (EN) algorithm, feed forward neural network (FN), and generalized regression neural network model (GRN) are investigated in this study. The performance metrics of the errors are analysed such as the root mean square error (RMSE), mean absolute percentage error (MAPE), and mean square root (MSE). According to the findings, the model behaves consistently at the specified time and place in the experiment. Forecasters utilizing neural network models will have better accuracy if they use techniques like EN, FFN, and GRN having the RMSE of 0.25, 0.37, and 0.45, respectively. |
| format | Article |
| id | doaj-art-bc17d517f6524ec884a5ea94434efa66 |
| institution | OA Journals |
| issn | 1687-529X |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Photoenergy |
| spelling | doaj-art-bc17d517f6524ec884a5ea94434efa662025-08-20T02:18:43ZengWileyInternational Journal of Photoenergy1687-529X2021-01-01202110.1155/2021/7894849Hourly and Day Ahead Power Prediction of Building Integrated Semitransparent Photovoltaic SystemS. Kaliappan0R. Saravanakumar1Alagar Karthick2P. Marish Kumar3V. Venkatesh4V. Mohanavel5S. Rajkumar6Department of Electrical and Electronics EngineeringDepartment of Wireless CommunicationRenewable Energy LabDepartment of Electrical and Electronics EngineeringDepartment of Electrical and Electronics EngineeringCentre for Materials Engineering and Regenerative MedicineDepartment of Mechanical EngineeringThe building integrated semitransparent photovoltaic (BISTPV) system is an emerging technology which replaces the conventional building material envelopes and roof. The performance prediction of the BISTPV system places a vital role in the reduction of the energy consumption in the building. In this work, the artificial neural network (ANN) is used to predict the performance of this system by optimizing the important parameter of the feature selection. The Elman neural network (EN) algorithm, feed forward neural network (FN), and generalized regression neural network model (GRN) are investigated in this study. The performance metrics of the errors are analysed such as the root mean square error (RMSE), mean absolute percentage error (MAPE), and mean square root (MSE). According to the findings, the model behaves consistently at the specified time and place in the experiment. Forecasters utilizing neural network models will have better accuracy if they use techniques like EN, FFN, and GRN having the RMSE of 0.25, 0.37, and 0.45, respectively.http://dx.doi.org/10.1155/2021/7894849 |
| spellingShingle | S. Kaliappan R. Saravanakumar Alagar Karthick P. Marish Kumar V. Venkatesh V. Mohanavel S. Rajkumar Hourly and Day Ahead Power Prediction of Building Integrated Semitransparent Photovoltaic System International Journal of Photoenergy |
| title | Hourly and Day Ahead Power Prediction of Building Integrated Semitransparent Photovoltaic System |
| title_full | Hourly and Day Ahead Power Prediction of Building Integrated Semitransparent Photovoltaic System |
| title_fullStr | Hourly and Day Ahead Power Prediction of Building Integrated Semitransparent Photovoltaic System |
| title_full_unstemmed | Hourly and Day Ahead Power Prediction of Building Integrated Semitransparent Photovoltaic System |
| title_short | Hourly and Day Ahead Power Prediction of Building Integrated Semitransparent Photovoltaic System |
| title_sort | hourly and day ahead power prediction of building integrated semitransparent photovoltaic system |
| url | http://dx.doi.org/10.1155/2021/7894849 |
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