A Scalable and User-Friendly Framework Integrating IoT and
It introduces a novel approach that exploits standardized parametric 3D models, enabling the efficient simulation and optimization of home energy systems.
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It introduces a novel approach that exploits standardized parametric 3D models, enabling the efficient simulation and optimization of home energy systems.
By integrating data on electricity prices, demand patterns, weather forecasts, and renewable energy availability, these systems empower homeowners to make informed choices that
In order to optimize energy consumption and improve overall sustainability while maintaining technical and economic constraints, the paper first investigates the multi-faceted aspects
Against this backdrop, this research paper seeks to explore the design, development, and implementation of a Smart Home Energy Management System (SHEMS) that leverages IoT and
Given these challenges, this paper proposes using the BFMO algorithm combined with DRL to develop a more adaptive and efficient HEMS optimization framework.
Fundamental component of SHEMS is a controller interfacing with all household loads and the main meter, ensuring timely data on power consumption. Notably, during peak times, the
This system includes components such as a shedding methodology for managing household power usage derived sustainable power resources, wireless communication among home
These results confirm the potential of combining deep learning with nature-inspired optimization to support intelligent, low-emission energy management in hydrogen-integrated microgrids.
MPC dynamically adjusts appliance operations, MILP ensures optimal scheduling, and ML predicts user energy consumption patterns, allowing intelligent automation. A web-based dashboard enables users
To manage uncertainties and adaptively schedule the operations of appliances, electric vehicles, and energy storage systems (ESS), the proposed HEMS uses a fuzzy programming
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