Development of an IoT-Driven Building Environment for Prediction of Electric Energy Consumption

Abstract

This article demonstrates the importance of integrating the Internet-of-Things (IoT) devices and technologies for efficient energy management in a building environment. An Elman recurrent neural network (RNN) model and an exponential model are developed for electric energy consumption prediction in an IoT-driven building environment. The models predict electric energy consumption by electric loads in the near future by: 1) recognizing the existence of a relationship between the net electric energy consumption of the building’s electric loads and the ambient temperature along with the occupancy state of the building and 2) employing the detected relationship to predict electric energy consumption using the forecasted temperature and the scheduled occupancy state. The building environment under consideration is the Real-Time Power and Intelligent Systems (RTPIS) laboratory integrated with intelligent monitoring and control capabilities using IoT devices and technologies. The electric loads under consideration include heating, ventilation, and air conditioning (HVAC) units and light panels. The developed Elman RNN and exponential models are also compared. These prediction capabilities are beneficial in overcoming variabilities in electric energy consumption by supplying electric energy as needed to meet the demands of the electric loads, thereby minimizing wasted electric energy, reducing carbon emissions, and generating cost savings.