Short-Term Power Prediction of Building Integrated Photovoltaic (BIPV) System Based on Machine Learning Algorithms
One of the biggest challenges is towards ensuring large-scale integration of photovoltaic systems into buildings. This work is aimed at presenting a building integrated photovoltaic system power prediction concerning the building’s various orientations based on the machine learning data science tool...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | International Journal of Photoenergy |
| Online Access: | http://dx.doi.org/10.1155/2021/5582418 |
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| author | R. Kabilan V. Chandran J. Yogapriya Alagar Karthick Priyesh P. Gandhi V. Mohanavel Robbi Rahim S. Manoharan |
| author_facet | R. Kabilan V. Chandran J. Yogapriya Alagar Karthick Priyesh P. Gandhi V. Mohanavel Robbi Rahim S. Manoharan |
| author_sort | R. Kabilan |
| collection | DOAJ |
| description | One of the biggest challenges is towards ensuring large-scale integration of photovoltaic systems into buildings. This work is aimed at presenting a building integrated photovoltaic system power prediction concerning the building’s various orientations based on the machine learning data science tools. The proposed prediction methodology comprises a data quality stage, machine learning algorithm, weather clustering assessment, and an accuracy assessment. The results showed that the application of linear regression coefficients to the forecast outputs of the developed photovoltaic power generation neural network improved the PV power generation’s forecast output. The final model resulted from accurate forecasts, exhibiting a root mean square error of 4.42% in NN, 16.86% in QSVM, and 8.76% in TREE. The results are presented with the building facade and roof application such as flat roof, south façade, east façade, and west façade. |
| format | Article |
| id | doaj-art-2e03610c0b04406db86cdbc84e213be0 |
| institution | Kabale University |
| issn | 1110-662X 1687-529X |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Photoenergy |
| spelling | doaj-art-2e03610c0b04406db86cdbc84e213be02025-02-03T05:58:26ZengWileyInternational Journal of Photoenergy1110-662X1687-529X2021-01-01202110.1155/2021/55824185582418Short-Term Power Prediction of Building Integrated Photovoltaic (BIPV) System Based on Machine Learning AlgorithmsR. Kabilan0V. Chandran1J. Yogapriya2Alagar Karthick3Priyesh P. Gandhi4V. Mohanavel5Robbi Rahim6S. Manoharan7Department of Electronics and Communication Engineering, Francis Xavier Engineering College, Tirunelveli, 627003 Tamil Nadu, IndiaDepartment of Electronics and Communication Engineering, KPR Institute of Engineering and Technology, Avinashi Road, Arasur, Coimbatore, 641407 Tamil Nadu, IndiaDepartment of Computer Science and Engineering, Kongunadu College of Engineering and Technology, Trichy, 621215 Tamil Nadu, IndiaDepartment of Electrical and Electronics Engineering, KPR Institute of Engineering and Technology, Avinashi Road, Arasur, Coimbatore, 641407 Tamil Nadu, IndiaDepartment of Electronics and Communication Engineering, Sigma Institute of Engineering, Vadodara, 390019 Gujarat, IndiaCentre for Materials Engineering and Regenerative Medicine, Bharath Institute of Higher Education and Research, Chennai-600073, Tamil Nadu, IndiaDepartment of Informatics Management, Sekolah Tinggi Ilmu Manajemen Sukma, Medan, Sumatera Utara 20219, IndonesiaDepartment of Computer Science, School of Informatics and Electrical Engineering, Institute of Technology, Ambo University, Ambo, Post Box No.: 19, EthiopiaOne of the biggest challenges is towards ensuring large-scale integration of photovoltaic systems into buildings. This work is aimed at presenting a building integrated photovoltaic system power prediction concerning the building’s various orientations based on the machine learning data science tools. The proposed prediction methodology comprises a data quality stage, machine learning algorithm, weather clustering assessment, and an accuracy assessment. The results showed that the application of linear regression coefficients to the forecast outputs of the developed photovoltaic power generation neural network improved the PV power generation’s forecast output. The final model resulted from accurate forecasts, exhibiting a root mean square error of 4.42% in NN, 16.86% in QSVM, and 8.76% in TREE. The results are presented with the building facade and roof application such as flat roof, south façade, east façade, and west façade.http://dx.doi.org/10.1155/2021/5582418 |
| spellingShingle | R. Kabilan V. Chandran J. Yogapriya Alagar Karthick Priyesh P. Gandhi V. Mohanavel Robbi Rahim S. Manoharan Short-Term Power Prediction of Building Integrated Photovoltaic (BIPV) System Based on Machine Learning Algorithms International Journal of Photoenergy |
| title | Short-Term Power Prediction of Building Integrated Photovoltaic (BIPV) System Based on Machine Learning Algorithms |
| title_full | Short-Term Power Prediction of Building Integrated Photovoltaic (BIPV) System Based on Machine Learning Algorithms |
| title_fullStr | Short-Term Power Prediction of Building Integrated Photovoltaic (BIPV) System Based on Machine Learning Algorithms |
| title_full_unstemmed | Short-Term Power Prediction of Building Integrated Photovoltaic (BIPV) System Based on Machine Learning Algorithms |
| title_short | Short-Term Power Prediction of Building Integrated Photovoltaic (BIPV) System Based on Machine Learning Algorithms |
| title_sort | short term power prediction of building integrated photovoltaic bipv system based on machine learning algorithms |
| url | http://dx.doi.org/10.1155/2021/5582418 |
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