Smart Building Energy Management System using IoT and Machine Learning

Project Abstract

The increasing demand for energy efficiency and sustainability in the building sector has led to the development of innovative technologies such as Smart Building Energy Management Systems (BEMS) that leverage the Internet of Things (IoT) and Machine Learning algorithms. This research project aims to design and implement a Smart BEMS using IoT and Machine Learning techniques to optimize energy consumption, improve building performance, and reduce operational costs. The research will begin with a comprehensive review of the existing literature on smart buildings, energy management systems, IoT, and Machine Learning. This review will provide a theoretical foundation for the design and implementation of the Smart BEMS. The methodology chapter will outline the research approach, data collection methods, system architecture design, IoT sensor integration, Machine Learning algorithms selection, and evaluation criteria. The research will utilize real-time data from sensors deployed within a building to optimize energy usage based on occupancy patterns, weather conditions, and building characteristics. The discussion of findings chapter will present the results of the Smart BEMS implementation, including energy savings achieved, operational improvements, and user feedback. The findings will be analyzed to evaluate the effectiveness of the system in optimizing energy consumption and enhancing building performance. In conclusion, this research project will contribute to the advancement of Smart Building Energy Management Systems by demonstrating the feasibility and benefits of integrating IoT and Machine Learning technologies. The Smart BEMS developed in this study has the potential to revolutionize energy management practices in buildings, leading to significant cost savings and environmental benefits.

Project Overview