Deep Learning Based on Wireless Remote Sensing Model for Monitoring the Solar System Inverter
Traditional energy sources have become one of the most serious causes of environmental pollution because of the growing demand for energy. Because of the carbon emissions that have recently increased greatly, we had to search for a safe, cheap, and environmentally friendly energy source. Many photov...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/5561975 |
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| author | Xiaoyan Wang Gaokui Xu |
| author_facet | Xiaoyan Wang Gaokui Xu |
| author_sort | Xiaoyan Wang |
| collection | DOAJ |
| description | Traditional energy sources have become one of the most serious causes of environmental pollution because of the growing demand for energy. Because of the carbon emissions that have recently increased greatly, we had to search for a safe, cheap, and environmentally friendly energy source. Many photovoltaic (PV) solar panels are used as an energy source because of free and environmental friendliness. However, this technology has become a source of inspiration for many researchers. The proposed method suggests to extract useful features from PV and wind generators and then train the system to control them and update the inputs according to prediction results. Solar energy produces energy that varies according to the external influences and the immediate changes in weather conditions. Solar panels are connected through an inverter with the grid, through which the work of the solar panels is monitored using the Internet. It is worth using neural networks (NN) to control variables and adopt system output of previous iteration in processing operations. Use of deep learning (DL) in the control of solar energy panels helps reduce the direct surveillance of the system online. Solar power generation systems mainly depend on reducing the pollution resulting from carbon emissions. Saving CO2 emission is the main purpose of PV panel cells, so smart monitoring can achieve better result in that case. |
| format | Article |
| id | doaj-art-275db7249a3c42abbfc5ae91f9d88e65 |
| institution | OA Journals |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-275db7249a3c42abbfc5ae91f9d88e652025-08-20T02:04:48ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/55619755561975Deep Learning Based on Wireless Remote Sensing Model for Monitoring the Solar System InverterXiaoyan Wang0Gaokui Xu1Southwest Forestry University School of Mathematics and Science, Kunming, ChinaKunming Railway Vocational and Technical College, Kunming, ChinaTraditional energy sources have become one of the most serious causes of environmental pollution because of the growing demand for energy. Because of the carbon emissions that have recently increased greatly, we had to search for a safe, cheap, and environmentally friendly energy source. Many photovoltaic (PV) solar panels are used as an energy source because of free and environmental friendliness. However, this technology has become a source of inspiration for many researchers. The proposed method suggests to extract useful features from PV and wind generators and then train the system to control them and update the inputs according to prediction results. Solar energy produces energy that varies according to the external influences and the immediate changes in weather conditions. Solar panels are connected through an inverter with the grid, through which the work of the solar panels is monitored using the Internet. It is worth using neural networks (NN) to control variables and adopt system output of previous iteration in processing operations. Use of deep learning (DL) in the control of solar energy panels helps reduce the direct surveillance of the system online. Solar power generation systems mainly depend on reducing the pollution resulting from carbon emissions. Saving CO2 emission is the main purpose of PV panel cells, so smart monitoring can achieve better result in that case.http://dx.doi.org/10.1155/2021/5561975 |
| spellingShingle | Xiaoyan Wang Gaokui Xu Deep Learning Based on Wireless Remote Sensing Model for Monitoring the Solar System Inverter Complexity |
| title | Deep Learning Based on Wireless Remote Sensing Model for Monitoring the Solar System Inverter |
| title_full | Deep Learning Based on Wireless Remote Sensing Model for Monitoring the Solar System Inverter |
| title_fullStr | Deep Learning Based on Wireless Remote Sensing Model for Monitoring the Solar System Inverter |
| title_full_unstemmed | Deep Learning Based on Wireless Remote Sensing Model for Monitoring the Solar System Inverter |
| title_short | Deep Learning Based on Wireless Remote Sensing Model for Monitoring the Solar System Inverter |
| title_sort | deep learning based on wireless remote sensing model for monitoring the solar system inverter |
| url | http://dx.doi.org/10.1155/2021/5561975 |
| work_keys_str_mv | AT xiaoyanwang deeplearningbasedonwirelessremotesensingmodelformonitoringthesolarsysteminverter AT gaokuixu deeplearningbasedonwirelessremotesensingmodelformonitoringthesolarsysteminverter |