A Context-Aware IoT and Deep-Learning-Based Smart Classroom for Controlling Demand and Supply of Power Load
Prabesh Paudel, Sang-Kyoon Kim, Soonyoung Park, Kyoung-Ho Choi
Abstract
With the demand for clean energy increasing, novel research is presented in this paper on providing sustainable, clean energy for a university campus. The Internet of Things (IoT) is now a leading factor in saving energy. With added deep learning for action recognition, IoT sensors implemented in real-time appliances monitor and control the extra usage of energy in buildings. This gives an extra edge on digitizing energy usage and, ultimately, reducing the power load in the electric grid. Here, we present a novel proposal through context-aware architecture for energy saving in classrooms, combining Internet of Things (IoT) sensors and video action recognition. Using this method, we can save a significant amount of energy usage in buildings.