IoT and Machine Learning based Green Energy Generation using Hybrid Renewable Energy Sources of Solar, Wind and Hydrogen Fuel Cells
As the world seeks sustainable energy solutions, Internet of Things (IoT) applications demand consistent and efficient power sources. This paper presents an innovative hybrid renewable energy system, seamlessly integrating solar photovoltaic panels, wind turbines, and hydrogen fuel cells, tailored f...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2024-01-01
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| Series: | E3S Web of Conferences |
| Subjects: | |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/02/e3sconf_icregcsd2023_01008.pdf |
| _version_ | 1797343665981489152 |
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| author | Hamed Al Hajri Nawaf Naji Al Harthi Rahaf Pasam Gopi Krishna Natarajan Rajababu |
| author_facet | Hamed Al Hajri Nawaf Naji Al Harthi Rahaf Pasam Gopi Krishna Natarajan Rajababu |
| author_sort | Hamed Al Hajri Nawaf |
| collection | DOAJ |
| description | As the world seeks sustainable energy solutions, Internet of Things (IoT) applications demand consistent and efficient power sources. This paper presents an innovative hybrid renewable energy system, seamlessly integrating solar photovoltaic panels, wind turbines, and hydrogen fuel cells, tailored for IoT applications. Through machine learning algorithms, our proposed system not only optimizes energy generation in real-time but also ensures uninterrupted energy supply to IoT devices and consumers, even in fluctuating environmental conditions. This universal approach markedly diminishes the dependence on non-renewable energy sources, promoting a greener and more resilient energy infrastructure. The incorporation of hydrogen fuel cells uniquely positions our system as a reservoir for excess energy, ensuring consistent power even when solar or wind outputs diminish. Moreover, by synchronizing IoT devices with our energy system, we have procured real-time data on energy dynamics, facilitating unparalleled optimization and reduced wastage. The presented system shows the way for a sustainable future through the efficient green energy generation with the ever-evolving landscape of IoT applications and machine learning techniques. |
| first_indexed | 2024-03-08T10:51:01Z |
| format | Article |
| id | doaj.art-19fb8786954f414d9ebe4c68b9e83aa9 |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-03-08T10:51:01Z |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-19fb8786954f414d9ebe4c68b9e83aa92024-01-26T16:47:00ZengEDP SciencesE3S Web of Conferences2267-12422024-01-014720100810.1051/e3sconf/202447201008e3sconf_icregcsd2023_01008IoT and Machine Learning based Green Energy Generation using Hybrid Renewable Energy Sources of Solar, Wind and Hydrogen Fuel CellsHamed Al Hajri Nawaf0Naji Al Harthi Rahaf1Pasam Gopi Krishna2Natarajan Rajababu3Electrical and Electronics Centre, College of Engineering and Technology, University of Technology and Applied SciencesElectrical and Electronics Centre, College of Engineering and Technology, University of Technology and Applied SciencesElectrical and Electronics Centre, College of Engineering and Technology, University of Technology and Applied SciencesElectrical and Electronics Centre, College of Engineering and Technology, University of Technology and Applied SciencesAs the world seeks sustainable energy solutions, Internet of Things (IoT) applications demand consistent and efficient power sources. This paper presents an innovative hybrid renewable energy system, seamlessly integrating solar photovoltaic panels, wind turbines, and hydrogen fuel cells, tailored for IoT applications. Through machine learning algorithms, our proposed system not only optimizes energy generation in real-time but also ensures uninterrupted energy supply to IoT devices and consumers, even in fluctuating environmental conditions. This universal approach markedly diminishes the dependence on non-renewable energy sources, promoting a greener and more resilient energy infrastructure. The incorporation of hydrogen fuel cells uniquely positions our system as a reservoir for excess energy, ensuring consistent power even when solar or wind outputs diminish. Moreover, by synchronizing IoT devices with our energy system, we have procured real-time data on energy dynamics, facilitating unparalleled optimization and reduced wastage. The presented system shows the way for a sustainable future through the efficient green energy generation with the ever-evolving landscape of IoT applications and machine learning techniques.https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/02/e3sconf_icregcsd2023_01008.pdfhybrid renewable energyiot applicationsmachine learninghydrogen fuel cells |
| spellingShingle | Hamed Al Hajri Nawaf Naji Al Harthi Rahaf Pasam Gopi Krishna Natarajan Rajababu IoT and Machine Learning based Green Energy Generation using Hybrid Renewable Energy Sources of Solar, Wind and Hydrogen Fuel Cells E3S Web of Conferences hybrid renewable energy iot applications machine learning hydrogen fuel cells |
| title | IoT and Machine Learning based Green Energy Generation using Hybrid Renewable Energy Sources of Solar, Wind and Hydrogen Fuel Cells |
| title_full | IoT and Machine Learning based Green Energy Generation using Hybrid Renewable Energy Sources of Solar, Wind and Hydrogen Fuel Cells |
| title_fullStr | IoT and Machine Learning based Green Energy Generation using Hybrid Renewable Energy Sources of Solar, Wind and Hydrogen Fuel Cells |
| title_full_unstemmed | IoT and Machine Learning based Green Energy Generation using Hybrid Renewable Energy Sources of Solar, Wind and Hydrogen Fuel Cells |
| title_short | IoT and Machine Learning based Green Energy Generation using Hybrid Renewable Energy Sources of Solar, Wind and Hydrogen Fuel Cells |
| title_sort | iot and machine learning based green energy generation using hybrid renewable energy sources of solar wind and hydrogen fuel cells |
| topic | hybrid renewable energy iot applications machine learning hydrogen fuel cells |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/02/e3sconf_icregcsd2023_01008.pdf |
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