AI-Based Methods for Improving Energy Efficiency of Home Appliances through Air Quality Monitoring

Abstract

Energy efficiency in home appliances is becoming more and more important especially with the increasing need to save electricity and move towards sustainable living. With the growing use of artificial intelligence new ways are being explored to make appliances smarter and more energy-efficient. This research work has proposed a method that uses intelligent algorithms to improve the energy efficiency of home appliances by analyzing indoor air quality (IAQ) data. Traditional methods often struggle due to small datasets,high computational requirements and poor adaptability. To tackle these issues data preprocessing techniques like SMOTE-ENN to handle class imbalance and Z-score normalization for proper feature scaling are used. This study tested several models and found that Bidirectional GRU and Stacked LSTM performed the best reaching high validation accuracies of 99.81% and 99.64% respectively. What makes this approach different is how it links indoor environmental factors like CO₂ levels, humidity and temperature with energy usage patterns. This kind of integration can lead to more efficient and eco-friendly home energy systems. Overall, this work shows how smart algorithms can help us take a step forward in building greener and smarter homes.