TY - JOUR
T1 - Operation planning method for home air-conditioners considering characteristics of installation environment
AU - Kuroha, Ryoichi
AU - Fujimoto, Yu
AU - Hirohashi, Wataru
AU - Amano, Yoshiharu
AU - Tanabe, Shin ichi
AU - Hayashi, Yasuhiro
N1 - Funding Information:
This research was supported by the Japan Science and Technology (JST), CREST program, Grant Number JPMJCR15K5 .
Publisher Copyright:
© 2018 Elsevier B.V.
PY - 2018/10/15
Y1 - 2018/10/15
N2 - Home energy management systems (HEMSs) are the system to manage the energy usage in houses. The use of HEMSs, and especially those which are capable of automatically controlling home energy appliances such as air-conditioners (ACs), is expected to manage energy utilized in domestic field effectively. In the present study, we focused on automatic AC operation by HEMS with the combined goal of improving thermal comfort while reducing electricity costs. In general, the room temperature and electricity consumption of an AC are highly dependent on the characteristics of the installation environment, so that the derivation of an appropriate AC operation plan is generally a difficult task. To tackle this problem, an energy management method to provide AC operation plan tailor-made for the target AC installation environmental by learning the characteristics of the installation environment (CIE) from the historical operation result data is proposed. The efficacy of the proposed method is verified via numerical and real-world experiments.
AB - Home energy management systems (HEMSs) are the system to manage the energy usage in houses. The use of HEMSs, and especially those which are capable of automatically controlling home energy appliances such as air-conditioners (ACs), is expected to manage energy utilized in domestic field effectively. In the present study, we focused on automatic AC operation by HEMS with the combined goal of improving thermal comfort while reducing electricity costs. In general, the room temperature and electricity consumption of an AC are highly dependent on the characteristics of the installation environment, so that the derivation of an appropriate AC operation plan is generally a difficult task. To tackle this problem, an energy management method to provide AC operation plan tailor-made for the target AC installation environmental by learning the characteristics of the installation environment (CIE) from the historical operation result data is proposed. The efficacy of the proposed method is verified via numerical and real-world experiments.
KW - Air Conditioner (AC)
KW - Characteristics of Installation Environment (CIE)
KW - Home energy Management System (HEMS)
KW - Machine learning
KW - Operation planning
KW - Particle Swarm Optimization (PSO)
KW - Predicted Mean Vote (PMV)
KW - Real-world Experiment
KW - Smart house
KW - Support Vector Regression (SVR)
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U2 - 10.1016/j.enbuild.2018.08.015
DO - 10.1016/j.enbuild.2018.08.015
M3 - Article
AN - SCOPUS:85052474969
SN - 0378-7788
VL - 177
SP - 351
EP - 362
JO - Energy and Buildings
JF - Energy and Buildings
ER -