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

Ryoichi Kuroha, Yu Fujimoto, Wataru Hirohashi, Yoshiharu Amano, Shinichi Tanabe, Yasuhiro Hayashi

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)351-362
Number of pages12
JournalEnergy and Buildings
Volume177
DOIs
Publication statusPublished - 2018 Oct 15

Fingerprint

Planning
Energy management systems
Air
Electricity
Thermal comfort
Energy management
Costs
Experiments
Temperature

Keywords

  • Air Conditioner (AC)
  • Characteristics of Installation Environment (CIE)
  • Home energy Management System (HEMS)
  • Machine learning
  • Operation planning
  • Particle Swarm Optimization (PSO)
  • Predicted Mean Vote (PMV)
  • Real-world Experiment
  • Smart house
  • Support Vector Regression (SVR)

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • Mechanical Engineering
  • Electrical and Electronic Engineering

Cite this

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title = "Operation planning method for home air-conditioners considering characteristics of installation environment",
abstract = "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.",
keywords = "Air Conditioner (AC), Characteristics of Installation Environment (CIE), Home energy Management System (HEMS), Machine learning, Operation planning, Particle Swarm Optimization (PSO), Predicted Mean Vote (PMV), Real-world Experiment, Smart house, Support Vector Regression (SVR)",
author = "Ryoichi Kuroha and Yu Fujimoto and Wataru Hirohashi and Yoshiharu Amano and Shinichi Tanabe and Yasuhiro Hayashi",
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AU - Kuroha, Ryoichi

AU - Fujimoto, Yu

AU - Hirohashi, Wataru

AU - Amano, Yoshiharu

AU - Tanabe, Shinichi

AU - Hayashi, Yasuhiro

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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