抄録
The world is more aware of the need for saving energy because of increasing world energy consumption and environmental problems. To promote saving energy in the domestic field, the use of home energy management systems (HEMSs) is rapidly spreading. The HEMS which has automatically controlling function can control domestic electrical appliances including air-conditioners (ACs). In this research, we focus on AC operation plans to improve thermal comfort and reduce electricity costs for residents. However, AC control planning is generally a difficult task because the operation results greatly depend on the environmental characteristics in which the HEMS is installed. To solve this problem, we proposed an AC planning method that accounts for environmental characteristics and uncertainty in prediction by using historical data.
| 元の言語 | English |
|---|---|
| ホスト出版物のタイトル | 2017 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2017 |
| 出版者 | Institute of Electrical and Electronics Engineers Inc. |
| ISBN（電子版） | 9781538628904 |
| DOI | |
| 出版物ステータス | Published - 2017 10 26 |
| イベント | 2017 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2017 - Washington, United States 継続期間: 2017 4 23 → 2017 4 26 |
Other
| Other | 2017 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2017 |
|---|---|
| 国 | United States |
| 市 | Washington |
| 期間 | 17/4/23 → 17/4/26 |
Fingerprint
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Networks and Communications
- Energy Engineering and Power Technology
- Renewable Energy, Sustainability and the Environment
- Control and Optimization
これを引用
Development of prediction-based operation planning method for domestic air-conditioner with adaptive learning of installation environment. / Kuroha, Ryoichi; Fujimoto, Yu; Hirohashi, Wataru; Amano, Yoshiharu; Tanabe, Shinichi; Hayashi, Yasuhiro.
2017 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2017. Institute of Electrical and Electronics Engineers Inc., 2017. 8085983.研究成果: Conference contribution
}
TY - GEN
T1 - Development of prediction-based operation planning method for domestic air-conditioner with adaptive learning of installation environment
AU - Kuroha, Ryoichi
AU - Fujimoto, Yu
AU - Hirohashi, Wataru
AU - Amano, Yoshiharu
AU - Tanabe, Shinichi
AU - Hayashi, Yasuhiro
PY - 2017/10/26
Y1 - 2017/10/26
N2 - The world is more aware of the need for saving energy because of increasing world energy consumption and environmental problems. To promote saving energy in the domestic field, the use of home energy management systems (HEMSs) is rapidly spreading. The HEMS which has automatically controlling function can control domestic electrical appliances including air-conditioners (ACs). In this research, we focus on AC operation plans to improve thermal comfort and reduce electricity costs for residents. However, AC control planning is generally a difficult task because the operation results greatly depend on the environmental characteristics in which the HEMS is installed. To solve this problem, we proposed an AC planning method that accounts for environmental characteristics and uncertainty in prediction by using historical data.
AB - The world is more aware of the need for saving energy because of increasing world energy consumption and environmental problems. To promote saving energy in the domestic field, the use of home energy management systems (HEMSs) is rapidly spreading. The HEMS which has automatically controlling function can control domestic electrical appliances including air-conditioners (ACs). In this research, we focus on AC operation plans to improve thermal comfort and reduce electricity costs for residents. However, AC control planning is generally a difficult task because the operation results greatly depend on the environmental characteristics in which the HEMS is installed. To solve this problem, we proposed an AC planning method that accounts for environmental characteristics and uncertainty in prediction by using historical data.
KW - AC Operation Planning
KW - Air conditioner (AC)
KW - Home Energy Management System (HEMS)
KW - Particle Swarm Optimization (PSO)
KW - Predicted Mean Vote (PMV)
KW - Smart House
KW - Support Vector Regression (SVR)
UR - http://www.scopus.com/inward/record.url?scp=85040198577&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85040198577&partnerID=8YFLogxK
U2 - 10.1109/ISGT.2017.8085983
DO - 10.1109/ISGT.2017.8085983
M3 - Conference contribution
AN - SCOPUS:85040198577
BT - 2017 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2017
PB - Institute of Electrical and Electronics Engineers Inc.
ER -