Energy Efficiency in Buildings using Internet of Things (IoT) and Big Data Analytics

Abstract

In order to assess the energy efficiency of building activities in real-time, this research offers a data-driven methodology. Efficiently managing building energy usage while minimizing negative effects on the environment is the focus of this study. Using a large dataset that includes efficiency ratings obtained from sophisticated analytics and continuous monitoring, as well as specific energy consumption (SEC) measurements, our study reveals intricate patterns in energy use. Reducing energy consumption by 15% during peak hours is possible with the use of predictive modeling tools, which show the possibility of proactive actions. With dynamic modifications resulting in a 20% reduction in total energy use, there are substantial benefits to implementing adaptive techniques based on real-time data. The method’s dependability is confirmed by comparing it to industry-standard standards, which highlights how strong the real-time evaluation system is. Building managers may benefit greatly from this research’s findings on energy efficiency, which will help to create more sustainable and financially feasible building systems.