Design and Implementation of a Smart Home Energy
In this paper, the use of deep neural networks (DNNs) in designing and implementing a smart home energy management system using IoT and machine learning has shown critical potential in
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Home Energy Storage Integrated
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Deep learning based optimal energy management for photovoltaic and
Smart homes rely on interconnected devices, such as smart thermostats, lighting, and energy storage systems, to create a responsive energy ecosystem.
A robust optimization framework for smart home energy management
This paper presents a novel two-stage robust optimization framework for smart home energy management, integrating PV-BS, EV charging, and demand response strategies.
AIoT-based Smart Home Energy Management System
This study focuses on AI-powered IoT devices, where machine learning and neural network algorithms are implemented on an IoT based smart home energy management system (SHEMS).
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Home Energy Management Systems: Challenges, Heterogeneity
Proposed structured and layered design architecture for integrating HEMS devices and services towards the de-velopment of a smart city ecosystem. The rest of the paper is organized as follows. Section II
Reinforcement Learning-Based Energy Management of Smart Home
This paper presents a data-driven approach that leverages reinforcement learning to manage the optimal energy consumption of a smart home with a rooftop solar photovoltaic system,
Home Energy Management System Using IoT, AI, and Machine
This work focuses on developing an IoT-based Home Energy Management System (HEMS) that optimizes energy consumption through tariff-based scheduling, predictive control, and automation.
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Cross Validated
Q&A for people interested in statistics, machine learning, data analysis, data mining, and data visualization
Energy Management of Smart Home with Home Appliances, Energy
This paper presents a hierarchical deep reinforcement learning (DRL) method for the scheduling of energy consumptions of smart home appliances and distributed energy resources
Energy Storage Integrated Control Framework for Voltage and
This paper proposes an energy storage–integrated control framework that enhances both frequency and voltage regulation in low-inertia, renewable-dominated grids.
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