A Context-Aware IoT and Deep-Learning-Based Smart Classroom for Controlling Demand and Supply of Power Load

Electronics

Prabesh Paudel¹  Soonyoung Park¹  Sangkyoon Kim¹  Kyoung-Ho Choi¹ 

[1] Department of Electronics Engineering, Mokpo National University, Jeollanam-do 534-729, Korea;

关键词: IoT;    context-aware sensors;    energy saving;    renewable energy;    action recognition;    transfer learning;   

DOI  :  10.3390/electronics9061039

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown