Smart City Gnosys
Smart city article details
| Title | Deep Learning And Heuristic Optimization For Secure And Efficient Energy Management In Smart Communities |
|---|---|
| ID_Doc | 17807 |
| Authors | Khan M.; Faisal M.; Albogamy F.R.; Diyan M. |
| Year | 2025 |
| Published | CMES - Computer Modeling in Engineering and Sciences, 143, 2 |
| DOI | http://dx.doi.org/10.32604/cmes.2025.063764 |
| Abstract | The rapid advancements in distributed generation technologies, the widespread adoption of distributed energy resources, and the integration of 5G technology have spurred sharing economy businesses within the electricity sector. Revolutionary technologies such as blockchain, 5G connectivity, and Internet of Things (IoT) devices have facilitated peer-to-peer distribution and real-time response to fluctuations in supply and demand. Nevertheless, sharing electricity within a smart community presents numerous challenges, including intricate design considerations, equitable allocation, and accurate forecasting due to the lack of well-organized temporal parameters. To address these challenges, this proposed system is focused on sharing extra electricity within the smart community. The working of the proposed system is composed of five main phases. In phase 1, we develop a model to forecast the energy consumption of the appliances using the Long Short-Term Memory (LSTM) integrated with the attention module. In phase 2, based on the predicted energy consumption, we designed a smart scheduler with attention-induced Genetic Algorithm (GA) to schedule the appliances to reduce energy consumption. In phase 3, a dynamic Feed-in Tariff (dFIT) algorithm makes real-time tariff adjustments using LSTM for demand prediction and SHapley Additive exPlanations (SHAP) values to improve model transparency. In phase 4, the energy saved from solar systems and smart scheduling is shared with the community grid. Finally, in phase 5, SDP security ensures the integrity and confidentiality of shared energy data. To evaluate the performance of energy sharing and scheduling for houses with and without solar support, we simulated the above phases using data obtained from the energy consumption of 17 household appliances in our IoT laboratory. Finally, the simulation results show that the proposed scheme reduces energy consumption and ensures secure and efficient distribution with peers, promoting a more sustainable energy management and resilient smart community. Copyright © 2025 The Authors. |
| Author Keywords | Community-centric; deep learning; energy management; internet of things; micro-grids; prediction; security; smart homes |
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