Performance based thermal comfort control (PTCC) using deep reinforcement learning for space cooling-Reference-Cited by-同舟云学术

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Performance based thermal comfort control (PTCC) using deep reinforcement learning for space cooling

Published:2019-11 Issue: Volume:203 Page:109420
ISSN:0378-7788
Container-title:Energy and Buildings
language:en
Short-container-title:Energy and Buildings

Author:

Yoon Young Ran,Moon Hyeun Jun

Funder

National Research Foundation of Korea

Ministry of Trade, Industry & Energy(MOTIE) of the Republic of Korea

Ministry of Land, Infrastructure and Transport

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Mechanical Engineering,Building and Construction,Civil and Structural Engineering

Reference44 articles.

1. EIA (2009) Emissions of greenhouse gases in the United States, Report number DOE/EIA-0573(2009).

2. Use of model predictive control and weather forecasts for energy efficient building climate control;Oldewurtel;Energy Build.,2012

3. Fast stochastic predictive control for building temperature regulation;Ma,2012

4. Reducing transient and steady state electricity consumption in HVAC using learning-based model predictive control;Aswani;Proc. IEEE,2012

5. Predictive control for energy efficient buildings with thermal storage: modeling, stimulation, and experiments;Ma;IEEE Control Syst.,2012

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