A Smart Energy Management System for Residential Buildings Using IoT and Machine Learning

• Published in: E3S Web of Conferences Vol. 387; p. 4009
• Main Authors: Joy, Kiruba P, Ahila, R, Biruntha, M, Kalpana, R
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Les Ulis EDP Sciences 01.01.2023
• Subjects: Algorithms; artificial neural network; Artificial neural networks; back propagation; Back propagation networks; Buildings; Data collection; Electronic devices; Energy consumption; Energy management; energy management system; Environmental impact; Internet of Things; iot; Machine learning; Motion sensors; Neural networks; Optimization; Residential areas; Residential buildings; Residential energy; Sensors; Smart sensors; Temperature sensors; Weather
• ISSN: 2267-1242; 2555-0403; 2267-1242
• DOI: 10.1051/e3sconf/202338704009

The Smart Energy Management System (SEMS) for Residential Buildings using IOT-based back propagation with ANN is a novel approach to optimize energy consumption in buildings by leveraging data from internet of things (IOT) devices. This system collects data on energy consumption, weather conditions, occupancy patterns, and sensor data from IOT devices such as motion sensors, temperature sensors, and smart appliances. The collected data is then preprocessed and used to train an artificial neural network (ANN) using back propagation algorithm. The trained model can then predict future energy demands, leading to cost savings and reduced environmental impact by optimizing energy consumption in a residential building. The proposed algorithm can be used as a foundation for building an effective SEMS using IOT-based back propagation with ANN.