Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | 2014 IEEE PES Transmission and Distribution Conference and Exposition, PES T and D-LA 2014 - Conference Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Print) | 9781479962501 |
| DOIs | |
| Publication status | Published - 2014 Nov 10 |
| Event | 2014 IEEE PES Transmission and Distribution Conference and Exposition - Latin America, PES T and D-LA 2014 - Medellin Duration: 2014 Sep 10 → 2014 Sep 13 |
Other
| Other | 2014 IEEE PES Transmission and Distribution Conference and Exposition - Latin America, PES T and D-LA 2014 |
|---|---|
| City | Medellin |
| Period | 14/9/10 → 14/9/13 |
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Keywords
- Bayesian network (BN)
- demand response (DR)
- home energy management system (HEMS)
- machine learning
- smart grid
ASJC Scopus subject areas
- Energy Engineering and Power Technology
- Fuel Technology
Cite this
Home energy management based on Bayesian network considering resident convenience. / Shoji, Tomoaki; Hirohashi, Wataru; Fujimoto, Yu; Hayashi, Yasuhiro.
2014 IEEE PES Transmission and Distribution Conference and Exposition, PES T and D-LA 2014 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc., 2014. 6960597.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
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
PY - 2014/11/10
Y1 - 2014/11/10
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=84916196056&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84916196056&partnerID=8YFLogxK
U2 - 10.1109/PMAPS.2014.6960597
DO - 10.1109/PMAPS.2014.6960597
M3 - Conference contribution
AN - SCOPUS:84915747913
SN - 9781479962501
BT - 2014 IEEE PES Transmission and Distribution Conference and Exposition, PES T and D-LA 2014 - Conference Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
ER -