Energy Consumption Prediction in Smart Homes using IoT and Machine Learning

Abstract: This research paper presents an intelligent system for predicting energy consumption in smart homes using Internet of Things (IoT) and Machine Learning (ML) techniques. With the rapid growth of smart devices and automation, efficient energy management has become a critical challenge. The proposed system collects real-time data from IoT- enabled devices such as smart meters, temperature sensors, humidity sensors, and occupancy detectors. The collected data is pre-processed and analysed using machine learning algorithms including Random Forest, Support Vector Machine (SVM), and Linear Regression to forecast future energy consumption. The system not only predicts energy usage but also identifies consumption patterns to optimize energy efficiency and reduce wastage.
Experimental analysis shows that advanced ML models, particularly Random Forest, provide higher accuracy compared to traditional methods. The proposed approach can be effectively applied in smart homes, smart cities, and industrial environments to support sustainable and cost-efficient energy management.

Keywords: Internet of Things (IoT), Machine Learning, Energy Consumption Prediction, Smart Homes, Random Forest, Support Vector Machine (SVM), Linear Regression, Energy Efficiency, Smart Energy Management, Time-Series Forecasting.