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Smart city article details
| Title | Reliability Improvement And Landscape Planning For Renewable Energy Integration In Smart Cities: A Case Study By Digital Twin |
|---|---|
| ID_Doc | 44956 |
| Authors | Zhang H.; Feng X. |
| Year | 2024 |
| Published | Sustainable Energy Technologies and Assessments, 64 |
| DOI | http://dx.doi.org/10.1016/j.seta.2024.103714 |
| Abstract | Due to increasing concerns about global warming, demand for renewable energies has increased significantly. Among all sources of renewable energy, tidal energy is one of the most efficient. In order to accurately simulate and predict tidal current (TC), a novel two-stage methodology is suggested in this paper which not only can benefit the smart cities, but also can help for a more fitting landscape design in offshore areas. The integration of digital twin technology in landscape planning facilitates a comprehensive understanding of renewable energy systems in smart cities, enabling proactive decision-making and sustainable development strategies. To extract the most desirable characteristics from the dataset measuring TC speed and direction, the suggested method uses a unique fuzzy feature selection (FFS) technique. The Long short-term memory (LSTM) is further trained for precise prediction using the chosen characteristics. In order to improve the performance of the LSTM, an evolutionary optimization technique is developed to train the parameters of the proposed model to reach the best training objectives. The efficiency and precision of the model are evaluated using real tidal data from the Qinhuangdao beach. The outcomes demonstrate the proposed model's suitable performance and excellent precision in contrast to other well-known techniques like shallow NNs and well-known linear techniques. © 2024 Elsevier Ltd |
| Author Keywords | Digital twin; Fuzzy feature selection; Landscape design; Long short-term memory; Renewable energy |
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