Prediction of Photovoltaic Power by ANN Based on Various Environmental Factors in India
Extreme weather conditions, which affect photovoltaic output power, can have a major impact on electricity generated by PV systems. In India, an annual PV power density of 2000kWh/m-2 may be used. Renewable energy (RE) is expected to play a rising part in the nations in coming years. The sun’s radia...
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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/10.1155/2022/4905980 |
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| author | B. Suresh Kumar Jenifer Mahilraj R. K. Chaurasia Chitaranjan Dalai A. H. Seikh S. M. A. K. Mohammed Ram Subbiah Abdi Diriba |
| author_facet | B. Suresh Kumar Jenifer Mahilraj R. K. Chaurasia Chitaranjan Dalai A. H. Seikh S. M. A. K. Mohammed Ram Subbiah Abdi Diriba |
| author_sort | B. Suresh Kumar |
| collection | DOAJ |
| description | Extreme weather conditions, which affect photovoltaic output power, can have a major impact on electricity generated by PV systems. In India, an annual PV power density of 2000kWh/m-2 may be used. Renewable energy (RE) is expected to play a rising part in the nations in coming years. The sun’s radiation is the primary source of renewable energy (RE). With the objective of predicting PV output power with the least amount of error in mind, it is vital to analyse the impact of major environmental parameters on it. The researchers looked at a variety of environmental factors in this study, including irradiance, humidity levels, meteorological conditions, wind velocity, PV global temperature and dust deposition. Countries such as India would gain immensely from this since it will increase the quantity of PV power generated in their national networks. ANN-based prediction models and multiple regression models were used to predict PV system hourly power output. There were three ANN models that predicted PV output power with RMSEs of 2.1436, 6.1555, and 5.3551, respectively, utilising all features using the correlation feature selection (CFS) or relief feature selection (ReliefF) approaches. It is possible to reduce bias to enhance accuracy by employing two distinct bias calculation methodologies, which were applied in this study. For example, the ANN model outperforms linear regression, M5P decision trees and GAUSSIAN process regression (GPR) models in terms of performance. |
| format | Article |
| id | doaj-art-86f870d0b72c4a38a68f678b6ba119bb |
| 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-86f870d0b72c4a38a68f678b6ba119bb2025-02-03T07:24:27ZengWileyInternational Journal of Photoenergy1687-529X2022-01-01202210.1155/2022/4905980Prediction of Photovoltaic Power by ANN Based on Various Environmental Factors in IndiaB. Suresh Kumar0Jenifer Mahilraj1R. K. Chaurasia2Chitaranjan Dalai3A. H. Seikh4S. M. A. K. Mohammed5Ram Subbiah6Abdi Diriba7Department of Electrical and Electronics EngineeringDepartment of CSE & ITDepartment of Electronic and Communication EngineeringDepartment of Civil EngineeringMechanical Engineering DepartmentDepartment of Mechanical and Industrial EngineeringDepartment of Mechanical EngineeringDepartment of Mechanical EngineeringExtreme weather conditions, which affect photovoltaic output power, can have a major impact on electricity generated by PV systems. In India, an annual PV power density of 2000kWh/m-2 may be used. Renewable energy (RE) is expected to play a rising part in the nations in coming years. The sun’s radiation is the primary source of renewable energy (RE). With the objective of predicting PV output power with the least amount of error in mind, it is vital to analyse the impact of major environmental parameters on it. The researchers looked at a variety of environmental factors in this study, including irradiance, humidity levels, meteorological conditions, wind velocity, PV global temperature and dust deposition. Countries such as India would gain immensely from this since it will increase the quantity of PV power generated in their national networks. ANN-based prediction models and multiple regression models were used to predict PV system hourly power output. There were three ANN models that predicted PV output power with RMSEs of 2.1436, 6.1555, and 5.3551, respectively, utilising all features using the correlation feature selection (CFS) or relief feature selection (ReliefF) approaches. It is possible to reduce bias to enhance accuracy by employing two distinct bias calculation methodologies, which were applied in this study. For example, the ANN model outperforms linear regression, M5P decision trees and GAUSSIAN process regression (GPR) models in terms of performance.http://dx.doi.org/10.1155/2022/4905980 |
| spellingShingle | B. Suresh Kumar Jenifer Mahilraj R. K. Chaurasia Chitaranjan Dalai A. H. Seikh S. M. A. K. Mohammed Ram Subbiah Abdi Diriba Prediction of Photovoltaic Power by ANN Based on Various Environmental Factors in India International Journal of Photoenergy |
| title | Prediction of Photovoltaic Power by ANN Based on Various Environmental Factors in India |
| title_full | Prediction of Photovoltaic Power by ANN Based on Various Environmental Factors in India |
| title_fullStr | Prediction of Photovoltaic Power by ANN Based on Various Environmental Factors in India |
| title_full_unstemmed | Prediction of Photovoltaic Power by ANN Based on Various Environmental Factors in India |
| title_short | Prediction of Photovoltaic Power by ANN Based on Various Environmental Factors in India |
| title_sort | prediction of photovoltaic power by ann based on various environmental factors in india |
| url | http://dx.doi.org/10.1155/2022/4905980 |
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