The Smart Energy Consumption Forecasting system is an intelligent energy analytics platform designed to monitor, analyze, and predict electricity usage in residential, commercial, and institutional environments. The proposed system integrates Internet of Things (IoT) sensors, cloud connectivity, machine learning models, and a responsive web application into a unified architecture for real-time energy management. Smart meters and environmental sensors continuously collect voltage, current, power factor, occupancy, temperature, and appliance-level usage data. These data streams are processed through a backend analytics engine, where machine learning algorithms such as Linear Regression, Random Forest Regressor, and Long Short-Term Memory (LSTM) networks are applied to forecast short-term and medium-term energy demand. The web application provides live dashboards, anomaly alerts, historical trend visualization, and consumption forecasting reports for users and administrators. Experimental observations indicate that the proposed platform improves energy visibility, supports proactive load balancing, and enhances decision-making for efficient power utilization. The system contributes toward sustainable smart building management by reducing energy wastage, improving forecast accuracy, and enabling intelligent, data-driven energy optimization.
Babu et al. (Tue,) studied this question.