The findings demonstrate that combining deep representation learning with adaptive optimization improves classification accuracy and stability, offering practical value for sector-aware energy planning, load prioritization, and data-driven decision support in smart city energy management.
An artificial-intelligence-driven evaluation system that combines BIM, IoT monitoring data, graph neural networks, deep reinforcement learning, and interpretable analysis is constructed that achieves an RMSE of 2.91 kWh/m2·a and reduces prediction error by 66.3% compared with static indicators.
An IoT-based energy management system for EVs by combining the harbor seal whisker optimization (HSWO) and the improved Elman spike neural network (IESNN) has been proposed.
In order to handle the growing energy demand, water scarcity, waste production, and environmental sustainability issues, smart cities need intelligent resource management. But current resource management frameworks generally optimize resource use in each infrastructure separately, reducing the ability of coordinated de...
This paper introduces a dynamic and innovative approach to energy management aimed at cost reduction and enhancing grid stability. The proposed system integrates Tiny Machine Learning (TinyML) and load forecasting into appliance scheduling and the real-time monitoring and control of building energy consumption. Unlike...
The integration of electric vehicles (EVs) in smart city energy networks creates substantial challenges for managingenergy, stabilizing the grid, and optimizing infrastructure. For this reason, this paper studies both AI and IoT (in conjunctionwith digital twins) to manage wireless charging and Vehicle-to-Grid (V2G). W...
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