A Practical Approach for Predicting Power in a Small-Scale Off-Grid Photovoltaic System using Machine Learning Algorithms
Climate change and the energy crisis substantially motivated the use and development of renewable energy resources. Solar power generation is being identified as the most promising and abundant source for bulk power generation. However, solar photovoltaic panel is heavily dependent on meteorological...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | International Journal of Photoenergy |
| Online Access: | http://dx.doi.org/10.1155/2022/9194537 |
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| author | Aadyasha Patel O. V. Gnana Swathika Umashankar Subramaniam T. Sudhakar Babu Alok Tripathi Samriddha Nag Alagar Karthick M. Muhibbullah |
| author_facet | Aadyasha Patel O. V. Gnana Swathika Umashankar Subramaniam T. Sudhakar Babu Alok Tripathi Samriddha Nag Alagar Karthick M. Muhibbullah |
| author_sort | Aadyasha Patel |
| collection | DOAJ |
| description | Climate change and the energy crisis substantially motivated the use and development of renewable energy resources. Solar power generation is being identified as the most promising and abundant source for bulk power generation. However, solar photovoltaic panel is heavily dependent on meteorological data of the installation site and weather fluctuations. To overcome these issues, collecting performance data at the remotely installed photovoltaic panel and predicting future power generation is important. The key objective of this paper is to develop a scaled-down prototype of an IoT-enabled datalogger for photovoltaic system that is installed in a remote location where human intervention is not possible due to harsh weather conditions or other circumstances. An Internet of Things platform is used to store and visualize the captured data from a standalone photovoltaic system. The collected data from the datalogger is used as a training set for machine learning algorithms. The estimation of power generation is done by a linear regression algorithm. The results are been compared with results obtained by another machine learning algorithm such as polynomial regression and case-based reasoning. Further, a website is developed wherein the user can key in the date and time. The output of that transaction is predicted temperature, humidity, and forecasted power generation of the specific standalone photovoltaic system. The presented results and obtained characteristics confirm the superiority of the proposed techniques in predicting power generation. |
| format | Article |
| id | doaj-art-d485217b717d49f9b963e9fce246d46f |
| institution | Kabale University |
| issn | 1687-529X |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Photoenergy |
| spelling | doaj-art-d485217b717d49f9b963e9fce246d46f2025-02-03T01:02:14ZengWileyInternational Journal of Photoenergy1687-529X2022-01-01202210.1155/2022/9194537A Practical Approach for Predicting Power in a Small-Scale Off-Grid Photovoltaic System using Machine Learning AlgorithmsAadyasha Patel0O. V. Gnana Swathika1Umashankar Subramaniam2T. Sudhakar Babu3Alok Tripathi4Samriddha Nag5Alagar Karthick6M. Muhibbullah7School of Electrical EngineeringSchool of Electrical EngineeringDepartment of Communications and NetworksDepartment of Electrical and Electronics EngineeringSchool of Electrical EngineeringSchool of Electrical EngineeringRenewable Energy labDepartment of Electrical and Electronic EngineeringClimate change and the energy crisis substantially motivated the use and development of renewable energy resources. Solar power generation is being identified as the most promising and abundant source for bulk power generation. However, solar photovoltaic panel is heavily dependent on meteorological data of the installation site and weather fluctuations. To overcome these issues, collecting performance data at the remotely installed photovoltaic panel and predicting future power generation is important. The key objective of this paper is to develop a scaled-down prototype of an IoT-enabled datalogger for photovoltaic system that is installed in a remote location where human intervention is not possible due to harsh weather conditions or other circumstances. An Internet of Things platform is used to store and visualize the captured data from a standalone photovoltaic system. The collected data from the datalogger is used as a training set for machine learning algorithms. The estimation of power generation is done by a linear regression algorithm. The results are been compared with results obtained by another machine learning algorithm such as polynomial regression and case-based reasoning. Further, a website is developed wherein the user can key in the date and time. The output of that transaction is predicted temperature, humidity, and forecasted power generation of the specific standalone photovoltaic system. The presented results and obtained characteristics confirm the superiority of the proposed techniques in predicting power generation.http://dx.doi.org/10.1155/2022/9194537 |
| spellingShingle | Aadyasha Patel O. V. Gnana Swathika Umashankar Subramaniam T. Sudhakar Babu Alok Tripathi Samriddha Nag Alagar Karthick M. Muhibbullah A Practical Approach for Predicting Power in a Small-Scale Off-Grid Photovoltaic System using Machine Learning Algorithms International Journal of Photoenergy |
| title | A Practical Approach for Predicting Power in a Small-Scale Off-Grid Photovoltaic System using Machine Learning Algorithms |
| title_full | A Practical Approach for Predicting Power in a Small-Scale Off-Grid Photovoltaic System using Machine Learning Algorithms |
| title_fullStr | A Practical Approach for Predicting Power in a Small-Scale Off-Grid Photovoltaic System using Machine Learning Algorithms |
| title_full_unstemmed | A Practical Approach for Predicting Power in a Small-Scale Off-Grid Photovoltaic System using Machine Learning Algorithms |
| title_short | A Practical Approach for Predicting Power in a Small-Scale Off-Grid Photovoltaic System using Machine Learning Algorithms |
| title_sort | practical approach for predicting power in a small scale off grid photovoltaic system using machine learning algorithms |
| url | http://dx.doi.org/10.1155/2022/9194537 |
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