A Comprehensive Deep Learning Framework for Smart Home Appliance Load Monitoring and Energy Optimization

The fastest growth of smart homes introduced and demand for sustainable energy optimization and load monitoring appliances systems. This study compare multiples Deep Learning based framework which deploy Particle swarm optimization(PSO)Artificial Neural Network(ANN)Reinforcement Learning(RL) Long Short term memory(LSTM),(Bi-LSTM) Bidirectional Long Short term memory,CNN(Convolutional Neural Network, Temporal Convolutional Networks (TCN),Transformer networks and Graph Neural Networks (GNN) to analyze and forecast the usage patterns of power. Both sequence learning and spatial-temporal analysis of residential load data are supported by the integration of these models. When the new framework is used on the PLAID dataset it outperforms traditional methods in terms of accuracy, energy efficiency and adaptability. Results from experiments verify each model's performance and list the advantages of hybrid architectures for optimizing the energy efficiency of smart homes. The goal of this research is to significantly advance sustainable energy management in IOT-based home settings.