Energy Monitoring and Prediction System using IoT and Machine Learning

Abstract

The amalgamation of machine learning and the Internet of Things (IoT) within Home Energy Management Systems (HEMS) markedly improves predictive maintenance and diminishes energy consumption by facilitating real-time data processing, anomaly detection, and the implementation of optimized energy utilization strategies. This collaborative framework fosters the creation of sophisticated systems capable of forecasting equipment malfunctions and refining energy consumption behaviors, thereby promoting enhanced sustainability and financial savings. The subsequent sections elucidate the manner in which these technologies contribute to predictive maintenance and energy efficiency. The incorporation of IoT facilitates continuous observation and data aggregation, which is imperative for training machine learning models to accurately forecast maintenance requirements. Despite the considerable advantages presented by the integration of machine learning and IoT within HEMS, challenges pertaining to data privacy, security, and the necessity for robust infrastructure must be addressed to fully harness the potential of these technologies. The smart grid assumes a pivotal role in contemporary society and within our networks. Smart meters are integral to this framework. Smart meters provide instantaneous monitoring of reliable status, automated information collection, user engagement, and energy regulation. Additionally, they deliver real-time consumption metrics and enable power management. In instances where a customer's peak load demand surpasses the maximum threshold, the electricity supply to consumers will be disconnected through the implementation of an IoT-based system for HEMS. This paper introduces the utilization of smart meters in conjunction with IoT technology. This paper concentrates on predicting the electrical energy consumption of domestic appliances utilizing a machine learning paradigm in low-energy consumption residences within an apartment complex. Furthermore, this paper emphasizes a smart system that wirelessly profiles energy consumption by calculating the energy utilized by individual consumers.