Optimizing smart home energy management for sustainability using machine learning techniques

Abstract

Abstract Energy is fundamental to all significant human endeavors and is crucial for sustaining life and realizing human potential. With the advent of smart homes, energy consumption is increasing as new technologies are introduced, leading to shifts in both lifestyle and societal norms. This scenario presents a unique energy challenge that requires extraordinary efforts to meet the anticipated energy demands. Various innovative strategies are being implemented to overcome the drawbacks and address the growing consumer demand for energy. Today, smart homes offer much more than just basic functions; they also focus on resource management, energy efficiency, and enhancing quality of life. Machine Learning (ML) plays a vital role in smart homes as it allows for the training, adjustment, and optimization of various functions. This intelligent, purposeful capacity has the potential to turn homes into dynamic and practical environments that improve daily performance, ease, and personalization. In this research, an ML-based multivariate model is proposed utilizing Long Short-Term Memory (LSTM) for smart homes, aiming to optimize energy utilization and improve management in the realm of energy consumption. This model offers precise predictions of energy consumption, ensuring minimal random errors. Prominent metrics include a low Mean Squared Error (MSE) of 0.02284, a high Mean Absolute Error (MAE) of 0.184, a Mean Absolute Percentage Error (MAPE) of 0.123, the lowest Root Mean Squared Error (RMSE) at 0.15113, a significant Mean Absolute Scaled Error (MASE) of 0.996, and a strong R-squared value (R2) of 0.694. The proposed model delivers exceptional predictive performance as compared to the previous approaches, ensuring high reliability, which aligns with the standards needed for advancing toward a smart and sustainable future.

Keywords

• Sustainable energy management
• Smart home technology
• LSTM
• HEMS
• ML
• MSE