DEEP ENERGY OPTIMIZER: CNN-BASED PREDICTION AND MANAGEMENT OF POWER CONSUMPTION IN SMART RESIDENTIAL ENVIRONMENTS

Abstract

Modern smart homes, integrated with various sensors and automated systems, offer significant 
potential for enhancing energy efficiency while maintaining occupant comfort. Studies reveal that 
smart homes contribute to around 40% of overall residential energy consumption, with intelligent 
optimization enabling up to 30% in potential energy savings. Despite this, over 60% of smart homes 
still rely on manual energy control methods, leading to inefficient energy usage. This underscores the 
need for automated, data-driven solutions for energy optimization. Traditional approaches such as 
manual scheduling, rule-based logic, and simple models like Decision Tree Regression (DTR) 
struggle to handle the complex, nonlinear nature of energy consumption patterns and often lack 
adaptability to real-time changes. To overcome these limitations, we propose a hybrid deep learning
based Smart Home Energy Optimizer that combines Convolutional Neural Networks (CNNs) for 
advanced feature extraction with a Random Forest Regressor for accurate energy usage prediction and 
optimization. The dataset undergoes thorough pre-processing, including normalization, outlier 
removal, and feature encoding, followed by a train-test split for performance evaluation. While CNNs 
are leveraged to capture temporal and spatial patterns in energy consumption, the Random Forest 
model provides strong generalization and robust regression capabilities. The proposed system is 
evaluated using standard performance metrics accuracy, precision, recall, and F1-score showing 
notable improvements compared to baseline methods. This intelligent solution ensures efficient power 
allocation, real-time responsiveness, and substantial energy savings in modern smart home 
environments.