Application of Machine Learning in Multi-Directional Model to Follow Solar Energy Using Photo Sensor Matrix
In this paper, we introduce a deep neural network (DNN) for forecasting the intra-day solar irradiance, photovoltaic PV plants, regardless of whether or not they have energy storage, can benefit from the work being done here. The proposed DNN utilises a number of different methodologies, two of whic...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | International Journal of Photoenergy |
| Online Access: | http://dx.doi.org/10.1155/2022/5756610 |
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| _version_ | 1832567535088697344 |
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| author | P. Dhanalakshmi V. Venkatesh P. S. Ranjit N. Hemalatha S. Divyapriya R. Sandhiya Sumit Kushwaha Asmita Marathe Mekete Asmare Huluka |
| author_facet | P. Dhanalakshmi V. Venkatesh P. S. Ranjit N. Hemalatha S. Divyapriya R. Sandhiya Sumit Kushwaha Asmita Marathe Mekete Asmare Huluka |
| author_sort | P. Dhanalakshmi |
| collection | DOAJ |
| description | In this paper, we introduce a deep neural network (DNN) for forecasting the intra-day solar irradiance, photovoltaic PV plants, regardless of whether or not they have energy storage, can benefit from the work being done here. The proposed DNN utilises a number of different methodologies, two of which are cloud motion analysis and machine learning, in order to make forecasts regarding the climatological conditions of the future. In addition to this, the accuracy of the model was evaluated in light of the data sources that were easily accessible. In general, four different cases have been investigated. According to the findings, the DNN is capable of making more accurate and reliable predictions of the incoming solar irradiance than the persistent algorithm. This is the case across the board. Even without any actual data, the proposed model is considered to be state-of-the-art because it outperforms the current NWP forecasts for the same time horizon as those forecasts. When making predictions for the short term, using actual data to reduce the margin of error can be helpful. When making predictions for the long term, however, weather information can be beneficial. |
| format | Article |
| id | doaj-art-9a305d397e1e4406831638dc34a3357f |
| 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-9a305d397e1e4406831638dc34a3357f2025-02-03T01:01:19ZengWileyInternational Journal of Photoenergy1687-529X2022-01-01202210.1155/2022/5756610Application of Machine Learning in Multi-Directional Model to Follow Solar Energy Using Photo Sensor MatrixP. Dhanalakshmi0V. Venkatesh1P. S. Ranjit2N. Hemalatha3S. Divyapriya4R. Sandhiya5Sumit Kushwaha6Asmita Marathe7Mekete Asmare Huluka8Department of Computer Science and Systems EngineeringDepartment of Electrical and Electronics EngineeringDepartment of Mechanical EngineeringInstitute of Electronics and Communication EngineeringDepartment of Electrical and Electronics EngineeringDepartment of Computer Science EngineeringDepartment of Computer ApplicationsDepartment of TechnologyDepartment of Electrical and Computer EngineeringIn this paper, we introduce a deep neural network (DNN) for forecasting the intra-day solar irradiance, photovoltaic PV plants, regardless of whether or not they have energy storage, can benefit from the work being done here. The proposed DNN utilises a number of different methodologies, two of which are cloud motion analysis and machine learning, in order to make forecasts regarding the climatological conditions of the future. In addition to this, the accuracy of the model was evaluated in light of the data sources that were easily accessible. In general, four different cases have been investigated. According to the findings, the DNN is capable of making more accurate and reliable predictions of the incoming solar irradiance than the persistent algorithm. This is the case across the board. Even without any actual data, the proposed model is considered to be state-of-the-art because it outperforms the current NWP forecasts for the same time horizon as those forecasts. When making predictions for the short term, using actual data to reduce the margin of error can be helpful. When making predictions for the long term, however, weather information can be beneficial.http://dx.doi.org/10.1155/2022/5756610 |
| spellingShingle | P. Dhanalakshmi V. Venkatesh P. S. Ranjit N. Hemalatha S. Divyapriya R. Sandhiya Sumit Kushwaha Asmita Marathe Mekete Asmare Huluka Application of Machine Learning in Multi-Directional Model to Follow Solar Energy Using Photo Sensor Matrix International Journal of Photoenergy |
| title | Application of Machine Learning in Multi-Directional Model to Follow Solar Energy Using Photo Sensor Matrix |
| title_full | Application of Machine Learning in Multi-Directional Model to Follow Solar Energy Using Photo Sensor Matrix |
| title_fullStr | Application of Machine Learning in Multi-Directional Model to Follow Solar Energy Using Photo Sensor Matrix |
| title_full_unstemmed | Application of Machine Learning in Multi-Directional Model to Follow Solar Energy Using Photo Sensor Matrix |
| title_short | Application of Machine Learning in Multi-Directional Model to Follow Solar Energy Using Photo Sensor Matrix |
| title_sort | application of machine learning in multi directional model to follow solar energy using photo sensor matrix |
| url | http://dx.doi.org/10.1155/2022/5756610 |
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