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Optimizing Energy Consumption in Smart Homes Using Hybrid Deep Learning Models

ABSTRACT

This paper investigates a novel hybrid deep learning framework for optimizing energy usage in smart home environments. By integrating convolutional neural networks (CNNs) and long short-term memory (LSTM) networks with reinforcement learning (RL), the proposed model predicts energy demand patterns and dynamically adjusts device operation to reduce consumption while maintaining user comfort. Comprehensive experiments on real-world smart home datasets demonstrate significant reductions in energy use, outperforming baseline approaches.

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