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Control Methods for Energy Efficiency Technologies in Buildings

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Civil Engineering".

Deadline for manuscript submissions: closed (20 April 2023) | Viewed by 1255

Special Issue Editors


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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

E-Mail Website
Guest Editor
Department of Building and Plant Engineering, Hanbat National University, Daejeon 34158, Korea
Interests: building energy systems

Special Issue Information

Dear Colleagues,

Since the oil crisis in the 1970s and the climate change agreement in the 1990s, energy reduction and energy efficiency technologies have been important topics in buildings. Recently, in the field of building energy systems, research on building energy efficiency using artificial intelligence and IoT technology has been actively conducted. By using deep learning, cloud-based computing, and optimal algorithms to predict the building environment and implement algorithms that control the building energy system in real time, researchers are striving to realize smart buildings that ensure energy savings and comfort. This Special Issue invites original research in the area of recent research and development efforts in passive and active building energy efficiency technology, the design, modeling and optimization aspects of energy and environmental systems for building energy audits, assessment and commissioning, such as heating, ventilating and air conditioning (HVAC) systems, renewable energy and passive systems, smart and intelligent buildings, building automation control and operation, building energy management systems (BEMS), fault detection diagnosis (FDD) and calibration, and sustainable and net-zero energy buildings. Topics covered by this Special Issue include but are not limited to the following specific issues:

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

Prof. Dr. Young-Hum Cho
Dr. Sung Lok Do
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 efficiency technology
  • building automation control and operation
  • building energy audits, assessment and commissioning
  • building energy management systems (BEMS)
  • building HVAC system
  • fault detection diagnosis (FDD) and calibration
  • heating, ventilating and air conditioning (HVAC) systems
  • machine and deep learning control
  • renewable energy and passive systems
  • sustainable and net-zero energy buildings
  • smart and intelligent buildings
  • smart sensors for indoor air monitoring
  • human comfort and indoor environmental quality
  • ventilation and air circulation
  • artificial intelligence and neural networks
  • building energy and environmental control
  • building energy systems design, modeling and optimization

Published Papers (1 paper)

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Research

19 pages, 3208 KiB  
Article
A COP Prediction Model of Hybrid Geothermal Heat Pump Systems based on ANN and SVM with Hyper-Parameters Optimization
by Jihyun Shin, Jinhyun Lee and Younghum Cho
Appl. Sci. 2023, 13(13), 7771; https://doi.org/10.3390/app13137771 - 30 Jun 2023
Cited by 1 | Viewed by 942
Abstract
When the geothermal heat pump system is operated due to an imbalance in the heating and cooling load, the system performance is lowered due to the occurrence of a thermal environment problem in the ground. To solve the performance degradation, a hybrid geothermal [...] Read more.
When the geothermal heat pump system is operated due to an imbalance in the heating and cooling load, the system performance is lowered due to the occurrence of a thermal environment problem in the ground. To solve the performance degradation, a hybrid geothermal heat pump system with an added auxiliary heat source is used. For the efficient operation of the system, it is necessary to check the performance coefficient of the hybrid geothermal system. The coefficient of performance can be monitored based on a mathematical model using a measuring instrument. However, in the case of mathematical models, there are a lot of input data required, and many measurement sensors are required for this. If there is an input factor that is omitted among the necessary input factors, the accuracy of the predicted performance coefficient is lowered or a problem occurs that it is impossible to predict. In this study, we intend to create a model that predicts the coefficient of performance (COP) by using ANNs and SVMs that can accurately predict at low cost using small input factors. Hyper-parameter optimization is performed to increase prediction accuracy in machine learning models. We compared the accuracy of ANN and SVM-based prediction models. In this study, the ANN model showed higher CvRMSE by 5.4% and SVM by 8%. It is expected that the predictive model will be able to be used in the operation of the hybrid geothermal system in the future. Full article
(This article belongs to the Special Issue Control Methods for Energy Efficiency Technologies in Buildings)
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