Smart energy management: real-time prediction and optimization for IoT-enabled smart homes

Abstract

The Smart Home Energy Management System (SHEMS) presents an innovative solution for optimizing energy consumption in residential settings by harnessing the synergy between Internet of Things (IoT) technology and Machine Learning (ML) algorithms. SHEMS offers a comprehensive suite of functionalities including monitoring, controlling, and optimizing energy usage while identifying wastage within smart homes. Its architecture comprises IoT sensors for data acquisition, an IoT gateway for preprocessing and storing data, and an Energy Management System (EMS) empowered by ML infrastructure for feature extraction and data transformation. Notably, the incorporation of the Gradient Boosting (GB) mechanism imbues SHEMS with intelligence, enabling it to analyze intricate datasets, detect patterns, and make data-driven decisions regarding energy optimization. Through ML capabilities, SHEMS adapts to dynamic usage patterns, predicts future consumption trends, and identifies opportunities for energy savings. Facilitating seamless data flow from sensors to the EMS, advanced ML techniques drive intelligent decision-making for enhanced energy efficiency. Additionally, SHEMS provides users with actionable insights and user-friendly interfaces for informed energy management, promising significant improvements in energy efficiency, cost reduction, and sustainability. The results showcase the effectiveness of the Gradient Boosting (GB) algorithm in predicting energy consumption for smart homes, with a score of 0.95, RMSE of 6.8, and MAE of 5.2. The Gradient Boosting algorithm consistently outperforms other ML algorithms, including Simple Linear Regression, Decision Tree Regression, Random Forest Regression, K-nearest neighbor Regression, and Support Vector Machine Regression.

Keywords

• Internet of things
• waste detection
• energy management
• machine learning
• smart home
• Artificial Intelligence