TY - GEN
T1 - Home energy management based on Bayesian network considering resident convenience
AU - Shoji, Tomoaki
AU - Hirohashi, Wataru
AU - Fujimoto, Yu
AU - Hayashi, Yasuhiro
N1 - Publisher Copyright:
© 2014 IEEE.
Copyright:
Copyright 2015 Elsevier B.V., All rights reserved.
PY - 2014/11/17
Y1 - 2014/11/17
N2 - Total electricity consumption in Japan increased rapidly and the power consumption per household is also continuing to increase. The framework of demand response (DR) to promote the reduction of electricity consumption in the household sector by regulating the price of the electricity will be introduced in the future. In this situation, residents must operate their appliances so as not to affect much to their lifestyles while taking into account the power cost. A home energy management system (HEMS) will have an essential role to control appliances such as air conditioners (ACs), battery energy storage systems (BESSs), electric vehicles (EVs), and heat pump water heaters (HPWHs) and automatically match their operations to the behavior of a resident when the electricity price changes. In this study, a Bayesian network, a fundamental tool of machine learning, is adapted to an HEMS to learn the behavior of the resident and appropriate operations of controllable appliances.
AB - Total electricity consumption in Japan increased rapidly and the power consumption per household is also continuing to increase. The framework of demand response (DR) to promote the reduction of electricity consumption in the household sector by regulating the price of the electricity will be introduced in the future. In this situation, residents must operate their appliances so as not to affect much to their lifestyles while taking into account the power cost. A home energy management system (HEMS) will have an essential role to control appliances such as air conditioners (ACs), battery energy storage systems (BESSs), electric vehicles (EVs), and heat pump water heaters (HPWHs) and automatically match their operations to the behavior of a resident when the electricity price changes. In this study, a Bayesian network, a fundamental tool of machine learning, is adapted to an HEMS to learn the behavior of the resident and appropriate operations of controllable appliances.
KW - Bayesian network (BN)
KW - demand response (DR)
KW - home energy management system (HEMS)
KW - machine learning
KW - smart grid
UR - http://www.scopus.com/inward/record.url?scp=84915747913&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84915747913&partnerID=8YFLogxK
U2 - 10.1109/PMAPS.2014.6960597
DO - 10.1109/PMAPS.2014.6960597
M3 - Conference contribution
AN - SCOPUS:84915747913
T3 - 2014 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2014 - Conference Proceedings
BT - 2014 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2014 - Conference Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2014
Y2 - 7 July 2014 through 10 July 2014
ER -
