Real-time implementation of an IoT-based intelligent source allocation system using machine learning technique in a commercial building

Building Energy Management is one of the prominent means of devising building-specific energy strategy and most of the research studies address the approach from the demand end. While, this study presents supply side management toward the challenge of implementing supply side management, due to the uncertainty of renewable power sources which are effectively tackled in the real-time scenario. In this paper, a novel Internet of Things-based Intelligent Source Allocation System (IoT-ISAS) has been designed specifically for the implementation within commercial buildings. The primary goal of this system is to ensure consistent and reliable power supply to critical loads within the building's direct current (DC) infrastructure. By leveraging a machine learning Decision Tree algorithm, the IoT-ISAS continuously allocates power sources to critical loads, thereby optimizing efficiency and minimizing downtime. The IoT-ISAS has been successfully deployed in a real-time commercial establishment at M/s Quantanics Techserv Private Limited. The IoT-ISAS incentivizes the commercial consumers to increase their utilization of renewable power sources and thereby, alleviates the burden on the grid. Based on the obtained results, it is observed that the sources are effectively shared and the tariff is found to be reduced by 9.76