applsci-logo

Journal Browser

Journal Browser

Commissioning New and Existing Buildings

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: closed (20 April 2022) | Viewed by 4611

Special Issue Editors


E-Mail Website
Guest Editor
School of Architecture, Yeungnam University, Gyeongsan 38541, Republic of Korea
Interests: building commissioning; smart building; building energy simulation; building energy management system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the late 80’s, a group of HVAC scientist and engineer proposed building commissioning to ensure that system performance met design specifications and has developed building commissioning technology, which is an ongoing process to resolve operating problems, improve comfort, optimize energy use and identify retrofits for existing commercial and institutional buildings and central plant facilities. This Special Issue invites original research in the area of recent research and development efforts in design, modeling and optimization aspects of energy and environmental systems for building energy audit, assessment and commissioning, such as Heating, ventilating and air conditioning(HVAC) system, renewable energy and passive system, smart and intelligent building, building automation control and operation, building energy management system(BEMS), fault detection diagnosis(FDD) and calibration and sustainable and net-zero energy buildings. Topics of this Special Issue include but are not limited to the following specific issues:

  • Building automation control and operation
  • Building energy audit, assessment and commissioning
  • Building energy management System(BEMS)
  • Building HVAC system
  • Fault detection diagnosis(FDD) and calibration
  • Heating, ventilating and air conditioning(HVAC) system
  • Machine and deep learning control
  • Renewable energy and passive system
  • Sustainable and net-zero energy building
  • Smart and intelligent building
  • Smart sensors for indoor air monitoring
  • Human comfort and indoor environmental quality
  • Ventilation and air circulation
  • Artificial intelligence and neural network
  • Building energy and environmental control
  • Building energy systems design, modeling and optimization

Dr. Luisa F. Cabeza
Professor Young-Hum Cho
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Building energy audit, assessment and commissioning
  • Building energy management System(BEMS)
  • Building HVAC system
  • Fault detection diagnosis(FDD) and calibration
  • Heating, ventilating and air conditioning(HVAC) system
  • Machine and deep learning control
  • Renewable energy and passive system
  • Sustainable and net-zero energy building
  • Smart and intelligent building
  • Smart sensors for indoor air monitoring
  • Human comfort and indoor environmental quality
  • Ventilation and air circulation
  • Artificial intelligence and neural network
  • Building energy and environmental control
  • Building energy systems design, modeling and optimization

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

10 pages, 2077 KiB  
Article
Predictive Model for the Optimized Mixed-Air Temperature of a Single-Duct VAV System
by Jin-Hyun Lee and Young-Hum Cho
Appl. Sci. 2022, 12(14), 6880; https://doi.org/10.3390/app12146880 - 7 Jul 2022
Cited by 1 | Viewed by 1063
Abstract
As global warming accelerates due to greenhouse gas emissions, more efforts are required to reduce greenhouse gas emissions. One of the methods used to save building energy is the efficient management of building mechanical systems. The economizer control of HVAC systems is an [...] Read more.
As global warming accelerates due to greenhouse gas emissions, more efforts are required to reduce greenhouse gas emissions. One of the methods used to save building energy is the efficient management of building mechanical systems. The economizer control of HVAC systems is an energy-efficient measure that improves operating methods by introducing outdoor air to save cooling energy when the outdoor-air temperature is sufficiently low. When the HVAC system is operated using economizer control, cooling energy can be saved, and the set-point of the mixed-air temperature is kept constant. Several studies are being conducted on the saving of energy using economizers. Although various studies have been conducted on the control of economizers, there is insufficient research dealing with the optimal control of mixed-air temperature in economizers that consider real-time changes. Therefore, in this study, predictive model-based mixed-air temperature optimization for a single-duct VAV system was constructed. For this, an ANN (Artificial Neural Network) that could be analyzed regardless of the variables was applied to predict the load and energy consumption and a simulator was constructed for the optimized mixed air temperature of the system. The predictive model-based control was evaluated in terms of its thermal comfort and energy, along with the existing economizer control. According to the application of the optimal economizer control, the energy consumption of the building was reduced by 28.9% compared to the existing dry-bulb temperature control, and was within ±1 °C of the indoor-air temperature set point. Full article
(This article belongs to the Special Issue Commissioning New and Existing Buildings)
Show Figures

Figure 1

14 pages, 4976 KiB  
Article
Machine-Learning-Based Coefficient of Performance Prediction Model for Heat Pump Systems
by Ji-Hyun Shin and Young-Hum Cho
Appl. Sci. 2022, 12(1), 362; https://doi.org/10.3390/app12010362 - 30 Dec 2021
Cited by 13 | Viewed by 2686
Abstract
In a heat pump system, performance is an important indicator that should be monitored for system optimization, fault diagnosis, and operational efficiency improvement. Real-time performance measurement and monitoring during heat pump operation is difficult because expensive performance measurement devices or additional installation of [...] Read more.
In a heat pump system, performance is an important indicator that should be monitored for system optimization, fault diagnosis, and operational efficiency improvement. Real-time performance measurement and monitoring during heat pump operation is difficult because expensive performance measurement devices or additional installation of various monitoring sensors required for performance calculation are required. When using a data-based machine-learning model, it is possible to predict and monitor performance by finding the relationship between input and output values through an existing sensor. In this study, the performance prediction model of the air-cooled heat pump system was developed and verified using artificial neural network, support vector machine, random forest, and K-nearest neighbor model. The operation data of the heat pump system installed in the university laboratory was measured and a prediction model for each machine-learning stage was developed. The mean bias error analysis is −3.6 for artificial neural network, −5 for artificial neural network, −7.7 for random forest, and −8.3 for K-nearest neighbor. The artificial neural network model with the highest accuracy and the shortest calculation time among the developed prediction models was applied to the Building Automation System to enable real-time performance monitoring and to confirm the field applicability of the developed model. Full article
(This article belongs to the Special Issue Commissioning New and Existing Buildings)
Show Figures

Figure 1

Back to TopTop