Green energy forecasting using multiheaded convolutional LSTM model for sustainable life
Using distributed energy resources can fulfil an individual's energy requirement, reducing electricity bills and creating sustainable energy solutions. Earlier, customers needed help utilising energy resources due to their limited knowledge. Technological advancement helps to utilise distribute...
| Main Authors: | , , , , , |
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| Format: | Article |
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Elsevier
2024
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| _version_ | 1825939326609915904 |
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| author | Liu, Peng Quan, Feng Gao, Yuxuan Alotaibi, Badr Alsenani, Theyab R. Abuhussain, Mohammed |
| author_facet | Liu, Peng Quan, Feng Gao, Yuxuan Alotaibi, Badr Alsenani, Theyab R. Abuhussain, Mohammed |
| author_sort | Liu, Peng |
| collection | UPM |
| description | Using distributed energy resources can fulfil an individual's energy requirement, reducing electricity bills and creating sustainable energy solutions. Earlier, customers needed help utilising energy resources due to their limited knowledge. Technological advancement helps to utilise distributed energy sources using machine learning, deep learning, the Internet of Things, Wireless technologies, big data, etc. Although there are a lot of provisions for utilisation, the central issue is forecasting the generated renewable energy without wasting the generated power. Data is generated based on long periods of energy generated from wind and solar irradiance. Then, the generated data is trained using deep learning models. The trained models can predict the generated power through green energy resources by accurately forecasting the wind speed and solar irradiance. In this research, we propose an efficient approach for microgrid-level energy management in an intelligent community based on integrating energy resources and forecasting wind speed and solar irradiance using a deep learning model. An intellectual community with several smart homes and a microgrid is considered. This work proposes a multiheaded convolutional LSTM and particle swarm optimisation (PSO) technique (MHCLSTM-PSO). The results are obtained from data using wind speed and solar irradiance. The accuracy rate of CNN was 72.52%, LSTM was 78.16%, CLSTM was 85.56%, and our proposed work produced 93.54 %. |
| first_indexed | 2024-12-09T02:17:26Z |
| format | Article |
| id | upm.eprints-106172 |
| institution | Universiti Putra Malaysia |
| last_indexed | 2024-12-09T02:17:26Z |
| publishDate | 2024 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | upm.eprints-1061722024-10-02T02:54:52Z http://psasir.upm.edu.my/id/eprint/106172/ Green energy forecasting using multiheaded convolutional LSTM model for sustainable life Liu, Peng Quan, Feng Gao, Yuxuan Alotaibi, Badr Alsenani, Theyab R. Abuhussain, Mohammed Using distributed energy resources can fulfil an individual's energy requirement, reducing electricity bills and creating sustainable energy solutions. Earlier, customers needed help utilising energy resources due to their limited knowledge. Technological advancement helps to utilise distributed energy sources using machine learning, deep learning, the Internet of Things, Wireless technologies, big data, etc. Although there are a lot of provisions for utilisation, the central issue is forecasting the generated renewable energy without wasting the generated power. Data is generated based on long periods of energy generated from wind and solar irradiance. Then, the generated data is trained using deep learning models. The trained models can predict the generated power through green energy resources by accurately forecasting the wind speed and solar irradiance. In this research, we propose an efficient approach for microgrid-level energy management in an intelligent community based on integrating energy resources and forecasting wind speed and solar irradiance using a deep learning model. An intellectual community with several smart homes and a microgrid is considered. This work proposes a multiheaded convolutional LSTM and particle swarm optimisation (PSO) technique (MHCLSTM-PSO). The results are obtained from data using wind speed and solar irradiance. The accuracy rate of CNN was 72.52%, LSTM was 78.16%, CLSTM was 85.56%, and our proposed work produced 93.54 %. Elsevier 2024-03 Article PeerReviewed Liu, Peng and Quan, Feng and Gao, Yuxuan and Alotaibi, Badr and Alsenani, Theyab R. and Abuhussain, Mohammed (2024) Green energy forecasting using multiheaded convolutional LSTM model for sustainable life. Sustainable Energy Technologies and Assessments, 63. art. no. 103609. ISSN 2213-1388 https://linkinghub.elsevier.com/retrieve/pii/S2213138824000055 10.1016/j.seta.2024.103609 |
| spellingShingle | Liu, Peng Quan, Feng Gao, Yuxuan Alotaibi, Badr Alsenani, Theyab R. Abuhussain, Mohammed Green energy forecasting using multiheaded convolutional LSTM model for sustainable life |
| title | Green energy forecasting using multiheaded convolutional LSTM model for sustainable life |
| title_full | Green energy forecasting using multiheaded convolutional LSTM model for sustainable life |
| title_fullStr | Green energy forecasting using multiheaded convolutional LSTM model for sustainable life |
| title_full_unstemmed | Green energy forecasting using multiheaded convolutional LSTM model for sustainable life |
| title_short | Green energy forecasting using multiheaded convolutional LSTM model for sustainable life |
| title_sort | green energy forecasting using multiheaded convolutional lstm model for sustainable life |
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