Operation planning method for home air-conditioners considering characteristics of installation environment

被引:10
|
作者
Kuroha, Ryoichi [1 ]
Fujimoto, Yu [1 ]
Hirohashi, Wataru [1 ]
Amano, Yoshiharu [1 ]
Tanabe, Shin-ichi [1 ]
Hayashi, Yasuhiro [1 ]
机构
[1] Waseda Univ, Shinjuku Ku, 3-4-1 Okubo, Tokyo 1698555, Japan
关键词
Home energy Management System (HEMS); Smart house; Air Conditioner (AC); Operation planning; Machine learning; Support Vector Regression (SVR); Particle Swarm Optimization (PSO); Predicted Mean Vote (PMV); Characteristics of Installation Environment (CIE); Real-world Experiment; ENERGY MANAGEMENT-SYSTEM; RESIDENTIAL BUILDINGS; HVAC MANAGEMENT; DEMAND RESPONSE; PRICING SCHEMES; OPTIMIZATION; CONSUMPTION; APPLIANCES; NETWORKS; POWER;
D O I
10.1016/j.enbuild.2018.08.015
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
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. (C) 2018 Elsevier B.V. All rights reserved.
引用
收藏
页码:351 / 362
页数:12
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