TY - JOUR
T1 - Demonstration of Intelligent HVAC Load Management With Deep Reinforcement Learning
T2 - Real-World Experience of Machine Learning in Demand Control
AU - Du, Yan
AU - Li, Fangxing
AU - Kurte, Kuldeep
AU - Munk, Jeffrey
AU - Zandi, Helia
N1 - Publisher Copyright:
© 2003-2012 IEEE.
PY - 2022
Y1 - 2022
N2 - Buildings account for 40% of total primary energy consumption and 30% of all CO2 emissions worldwide. A large portion of building energy consumption is due to heating, ventilation, and air-conditioning (HVAC) systems. In the summer, for example, more than 50% of a building's electricity consumption is used for cooling. With proper energy management, buildings can provide load shifting, peak shaving, frequency regulation, and many other demand response services.
AB - Buildings account for 40% of total primary energy consumption and 30% of all CO2 emissions worldwide. A large portion of building energy consumption is due to heating, ventilation, and air-conditioning (HVAC) systems. In the summer, for example, more than 50% of a building's electricity consumption is used for cooling. With proper energy management, buildings can provide load shifting, peak shaving, frequency regulation, and many other demand response services.
UR - http://www.scopus.com/inward/record.url?scp=85129304530&partnerID=8YFLogxK
U2 - 10.1109/MPE.2022.3150825
DO - 10.1109/MPE.2022.3150825
M3 - Article
AN - SCOPUS:85129304530
SN - 1540-7977
VL - 20
SP - 42
EP - 53
JO - IEEE Power and Energy Magazine
JF - IEEE Power and Energy Magazine
IS - 3
ER -
