Deep Learning-Driven IoT Framework for Smart Home Appliance Load Monitoring and Energy Optimization

The introduction of Internet of Things (IoT) and advances in Deep Learning (DL) have substantially altered the setting of smart home technology. This survey paper provides a comprehensive overview of IoT and DL-based systems designed for smart home appliance energy management. This survey paper provides a comprehensive overview of IoT and DL-based systems designed for smart home appliance energy management. The survey investigates several neural network designs and learning methodologies used for precise energy use prediction, as well as their integration with IoT frameworks to improve functionality. The study offers an analysis of existing methodologies like Reinforcement Learning (RL), Particle Swarm Optimization (PSO) with artificial Neural Networks(ANN), Convolutional Neural networks(CNN) and CNN with Long Short Term Memory(LSTM. The results show that the CNN-LSTM outperforms with an accuracy of 94% than the other methods. The findings highlight the potential of IoT and DL to develop smart and effective and sustainable energy management systems in smart home setups.