TY - GEN
T1 - Thermal Comfort Aware Online Energy Management Framework for a Smart Residential Building
AU - Watari, Daichi
AU - Taniguchi, Ittetsu
AU - Catthoor, Francky
AU - Marantos, Charalampos
AU - Siozios, Kostas
AU - Shirazi, Elham
AU - Soudris, Dimitrios
AU - Onoye, Takao
N1 - Publisher Copyright:
© 2021 EDAA.
PY - 2021/7/16
Y1 - 2021/7/16
N2 - Energy management in buildings equipped with renewable energy is vital for reducing electricity costs and maximizing occupant comfort. Despite several studies on the scheduling of appliances, a battery, and heating, ventilating, and air-conditioning (HVAC), there is a lack of a comprehensive and time-scalable approach that integrates predictive information such as renewable generation and thermal comfort. In this paper, we propose an online energy management framework to incorporate the optimal energy scheduling and prediction model of PV generation and thermal comfort by the model predictive control (MPC) approach. The energy management problem is formulated as coordinated three optimization problems covering a fast and slow time-scale.This reduces the time complexity without a significant negative impact on the global nature and quality of the result. Experimental results show that the proposed framework achieves optimal energy management that takes into account the trade-off between the electricity bill and thermal comfort.
AB - Energy management in buildings equipped with renewable energy is vital for reducing electricity costs and maximizing occupant comfort. Despite several studies on the scheduling of appliances, a battery, and heating, ventilating, and air-conditioning (HVAC), there is a lack of a comprehensive and time-scalable approach that integrates predictive information such as renewable generation and thermal comfort. In this paper, we propose an online energy management framework to incorporate the optimal energy scheduling and prediction model of PV generation and thermal comfort by the model predictive control (MPC) approach. The energy management problem is formulated as coordinated three optimization problems covering a fast and slow time-scale.This reduces the time complexity without a significant negative impact on the global nature and quality of the result. Experimental results show that the proposed framework achieves optimal energy management that takes into account the trade-off between the electricity bill and thermal comfort.
KW - model predictive control
KW - online energy management
KW - smart PV system
KW - thermal comfort
KW - n/a OA procedure
UR - http://www.scopus.com/inward/record.url?scp=85111007533&partnerID=8YFLogxK
U2 - 10.23919/DATE51398.2021.9473922
DO - 10.23919/DATE51398.2021.9473922
M3 - Conference contribution
AN - SCOPUS:85111007533
T3 - Proceedings -Design, Automation and Test in Europe, DATE
SP - 535
EP - 538
BT - 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE)
PB - IEEE
T2 - Design, Automation and Test in Europe Conference and Exhibition, DATE 2021
Y2 - 1 February 2021 through 5 February 2021
ER -