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ThinkMind // ALLSENSORS 2019, The Fourth International Conference on Advances in Sensors, Actuators, Metering and Sensing // View article allsensors_2019_4_20_70061


Demand Response Enabled Artificial Intelligence based Air Conditioning System

Authors:
Kiwoong Kwon
Sanghun Kim
Jungmee Yun
Byungmin Kim
Sanghak Lee

Keywords: Demand Response, Artificial Intelligence, Predicted Mean Vote, Air Conditioning System

Abstract:
Recently, as energy smart metering and remote control of smart devices become possible, Demand Response (DR) in home and small building is increasing. In this paper, we propose a DR enabled AI based air conditioning system to overcome the energy shortage problem in summer. It derives users' comfortable temperature by learning temperature and humidity, and automatically controls the air conditioner based on derived temperature. In addition, when DR is issued, the energy peak control based on the Predicted Mean Vote (PMV) is performed, thereby minimizing the user comfort degradation and finding DR resource. The experiment was conducted based on the test bed and we have shown its feasibility.

Pages: 39 to 40

Copyright: Copyright (c) IARIA, 2019

Publication date: February 24, 2019

Published in: conference

ISSN: 2519-836X

ISBN: 978-1-61208-691-0

Location: Athens, Greece

Dates: from February 24, 2019 to February 28, 2019

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