Deep Learning Techniques to Enhance Energy Efficiency of Home Appliances by Analyzing Air Quality Levels

Energy efficiency in home appliances is a critical area of research that addresses the growing demand for reducing energy consumption. The rapid growth in artificial intelligence has prioritized the development of advanced methods to improve sustainable energy consumption, particularly by optimizing the energy efficiency of home appliances. The research introduces a novel deep learning-based framework to enhance energy efficiency in home appliances by leveraging insights from Indoor Air Quality (IAQ) metrics. Unlike conventional energy management approaches, which face challenges such as limited datasets, computational inefficiencies, and a lack of generalizability, the research incorporates advanced preprocessing and augmentation techniques. Specifically, a hybrid Synthetic Minority Over-sampling Technique - Edited Nearest Neighbors (SMOTE-ENN) approach addresses class imbalance, while Z-score normalization ensures consistent feature scaling. Among the evaluated models, the Bidirectional Gated Recurrent Unit (GRU) and the Stacked Long Short-Term Memory (LSTM) stand out, achieving exceptional validation accuracies of 99.81% and 99.64%, respectively, demonstrating superior generalization. This framework uniquely integrates IAQ data to optimize energy usage dynamically, showcasing how environmental factors such as CO2, humidity, and temperature can inform sustainable energy practices. These findings underscore the transformative potential of deep learning in fostering ecofriendly innovations for smart home energy management. They show the broader potential for integrating artificial intelligence-driven approaches into energy policies and sustainability strategies, enabling more effective reductions in residential energy consumption and combating climate change.