A Context-Aware IoT and Deep-Learning-Based Smart Classroom for Controlling Demand and Supply of Power Load
| Autor: | Kyoung-Ho Choi, Soonyoung Park, Sangkyoon Kim, Prabesh Paudel |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
IoT
Computer Networks and Communications Computer science lcsh:TK7800-8360 Context (language use) 02 engineering and technology transfer learning Supply and demand energy saving 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering action recognition business.industry Deep learning lcsh:Electronics 020206 networking & telecommunications context-aware sensors Grid renewable energy Renewable energy Hardware and Architecture Control and Systems Engineering Signal Processing 020201 artificial intelligence & image processing Artificial intelligence Enhanced Data Rates for GSM Evolution business Telecommunications Energy (signal processing) |
| Zdroj: | Electronics Volume 9 Issue 6 Electronics, Vol 9, Iss 1039, p 1039 (2020) |
| ISSN: | 2079-9292 |
| DOI: | 10.3390/electronics9061039 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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