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Editorial Board Members’ Collection Series: Building Energy, Physics, Environment, and...
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Editorial Board Members’ Collection Series: Building Energy, Physics, Environment, and Systems I

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Building Energy, Physics, Environment, and Systems".

Deadline for manuscript submissions: closed (20 August 2023) | Viewed by 5906

Special Issue Editors

Prof. Dr. Shi-Jie Cao

E-Mail Website
Guest Editor
1. School of Architecture, Southeast University, Nanjing 210096, China
2. Global Centre for Clean Air Research (GCARE), Department of Civil and Environmental Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH, UK
Interests: building environment and control; air quality and health; urban environment and design; fast prediction of built environment
Special Issues, Collections and Topics in MDPI journals
Dr. Antonio Caggiano

E-Mail Website
Guest Editor
Department of Civil, Chemical and Environmental Engineering, University of Genova, 16145 Genova, Italy
Interests: sustainability in construction and building materials; recycling; smart materials; smart buildings; energy-saving; green buildings; eco-friendly materials; nearly zero-energy buildings; energy efficiency; energy storage; phase change materials; renewable energy resources; zero CO2 emissions; CO2 storage in materials; modeling; multiscale; multiphysics; micro- and meso-scale
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Wei Yu

E-Mail Website
Guest Editor
School of Civil Engineering, Chongqing University, Chongqing 400045, China
Interests: green buildings and building energy efficiency; indoor environment and human comfort and health
Prof. Dr. Christopher Yu-Hang Chao

E-Mail Website
Guest Editor
Department of Building Environment and Energy Engineering, The Hong Kong Polytechnic University, Hong Kong, China
Interests: indoor air quality; aerosol deposition and detachment behavior; biomimicry; smart building materials; energy use in building; intelligent building energy management system; solar and waste heat driven cooling and refrigeration system; passive radiative cooling; nanofluid; fire dynamics and combustion
Dr. Graziano Salvalai

E-Mail Website
Guest Editor
Department of Architecture, Construction Engineering and Built Environment, Polytechnic University of Milan, 20133 Milan, Italy
Interests: energy efficiency in buildings; zero energy building design; passive heating and cooling systems; innovative materials for buildings; building energy simulation and monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce a new Collection titled “Editorial Board Members’ Collection Series: "Building Energy, Physics, Environment, and Systems Ⅰ”, which will collect papers invited by the Editorial Board Members.

The aim of this Collection is to provide a venue for networking and communication between Buildings and scholars in the field of Building Energy, Physics, Environment, and Systems. All papers will be published in open access following peer review.

Prof. Dr. Shi-Jie Cao
Prof. Dr. Antonio Caggiano
Prof. Dr. Wei Yu
Prof. Dr. Christopher Yu-Hang Chao
Dr. Graziano Salvalai
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. Buildings is an international peer-reviewed open access monthly 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 2600 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
  • building environment
  • building physics
  • building systems

Published Papers (4 papers)

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Research

22 pages, 7456 KiB  
Article
Analysis of Cooling Load Characteristics in Chinese Residential Districts for HVAC System Design
by Jingjing An, Xin Zhou and Da Yan
Buildings 2023, 13(10), 2450; https://doi.org/10.3390/buildings13102450 - 26 Sep 2023
Viewed by 892
Abstract
Energy consumption in residential buildings accounts for a large portion of global energy use. Understanding residential building load characteristics is important in both the design and technical suitability analysis of residential air conditioning systems in terms of energy efficiency and carbon reduction. However, [...] Read more.
Energy consumption in residential buildings accounts for a large portion of global energy use. Understanding residential building load characteristics is important in both the design and technical suitability analysis of residential air conditioning systems in terms of energy efficiency and carbon reduction. However, most current research mainly focuses on the load characteristics of individual buildings and not on the variation in load characteristics of building aggregation. In addition, the load characteristics of building aggregations vary with the building scale; however, most studies have compared those of buildings under a certain scale, and the change with the increase in building scale is still unclear. The main purpose of this study is to explore load characteristic differences among residential buildings of different scales and the impacts of those differences on HVAC system design. Based on the monitoring data collected in a residential district in Zhengzhou, China, we analyzed the load characteristics among different households and combinations of different numbers of households from the variation in peak load, total consumption and load distribution, as well as the daily load volatility. We indicate that the load characteristics of heating, ventilation and air conditioning systems of different scales should be considered in the design and operation stage. Full article
(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Building Energy, Physics, Environment, and Systems I)
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21 pages, 12936 KiB  
Article
A New Principle for Building Simulation of Radiative Heat Transfer in the Presence of Spherical Surfaces
by Joseph Cabeza-Lainez
Buildings 2023, 13(6), 1447; https://doi.org/10.3390/buildings13061447 - 01 Jun 2023
Cited by 1 | Viewed by 873
Abstract
Radiant heat interchanges are pivotal to assessing the energy use of buildings and facilities that channel some sort of solar radiation. Form factor integrals are needed for an accurate simulation of the main features of the envelope of such buildings. However, the expressions [...] Read more.
Radiant heat interchanges are pivotal to assessing the energy use of buildings and facilities that channel some sort of solar radiation. Form factor integrals are needed for an accurate simulation of the main features of the envelope of such buildings. However, the expressions required when the space under analysis is curved, for instance, in domes and vaults, are not feasible. The calculation process of algorithms is usually addressed by cumbersome analytical deductions or else by rough statistical approximations included in the simulations, such as ray-tracing methods. Neither of which works properly under curved geometries. The following article deals with an innovative methodology for employing an exact property that solves any spherical configuration of the radiant surfaces. The newly found relationship is validated by comparison with other solutions previously deducted by the author and by numerical simulations when available. Since there is no other exact method of calculating radiation exchanges within spherical fragments, we consider that this finding represents an advance which contributes to overcoming a variety of unexplained and practical problems of radiative heat transfer applicable to architectural developments, lighting elements and aircraft components. Full article
(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Building Energy, Physics, Environment, and Systems I)
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20 pages, 6132 KiB  
Article
Deep Reinforcement Learning-Based Joint Optimization Control of Indoor Temperature and Relative Humidity in Office Buildings
by Changcheng Chen, Jingjing An, Chuang Wang, Xiaorong Duan, Shiyu Lu, Hangyu Che, Meiwei Qi and Da Yan
Buildings 2023, 13(2), 438; https://doi.org/10.3390/buildings13020438 - 04 Feb 2023
Cited by 4 | Viewed by 1728
Abstract
Indoor temperature and relative humidity control in office buildings is crucial, which can affect thermal comfort, work efficiency, and even health of the occupants. In China, fan coil units (FCUs) are widely used as air-conditioning equipment in office buildings. Currently, conventional FCU control [...] Read more.
Indoor temperature and relative humidity control in office buildings is crucial, which can affect thermal comfort, work efficiency, and even health of the occupants. In China, fan coil units (FCUs) are widely used as air-conditioning equipment in office buildings. Currently, conventional FCU control methods often ignore the impact of indoor relative humidity on building occupants by focusing only on indoor temperature as a single control object. This study used FCUs with a fresh-air system in an office building in Beijing as the research object and proposed a deep reinforcement learning (RL) control algorithm to adjust the air supply volume for the FCUs. To improve the joint control satisfaction rate of indoor temperature and relative humidity, the proposed RL algorithm adopted the deep Q-network algorithm. To train the RL algorithm, a detailed simulation environment model was established in the Transient System Simulation Tool (TRNSYS), including a building model and FCUs with a fresh-air system model. The simulation environment model can interact with the RL agent in real time through a self-developed TRNSYS–Python co-simulation platform. The RL algorithm was trained, tested, and evaluated based on the simulation environment model. The results indicate that compared with the traditional on/off and rule-based controllers, the RL algorithm proposed in this study can increase the joint control satisfaction rate of indoor temperature and relative humidity by 12.66% and 9.5%, respectively. This study provides preliminary direction for a deep reinforcement learning control strategy for indoor temperature and relative humidity in office building heating, ventilation, and air-conditioning (HVAC) systems. Full article
(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Building Energy, Physics, Environment, and Systems I)
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30 pages, 8205 KiB  
Article
Building Energy Models at Different Time Scales Based on Multi-Output Machine Learning
by Guangchen Li, Wei Tian, Hu Zhang and Bo Chen
Buildings 2022, 12(12), 2109; https://doi.org/10.3390/buildings12122109 - 01 Dec 2022
Cited by 3 | Viewed by 1787
Abstract
Machine learning techniques are widely applied in the field of building energy analysis to provide accurate energy models. The majority of previous studies, however, apply single-output machine learning algorithms to predict building energy use. Single-output models are unable to concurrently predict different time [...] Read more.
Machine learning techniques are widely applied in the field of building energy analysis to provide accurate energy models. The majority of previous studies, however, apply single-output machine learning algorithms to predict building energy use. Single-output models are unable to concurrently predict different time scales or various types of energy use. Therefore, this paper investigates the performance of multi-output energy models at three time scales (daily, monthly, and annual) using the Bayesian adaptive spline surface (BASS) and deep neural network (DNN) algorithms. The results indicate that the multi-output models based on the BASS approach combined with the principal component analysis can simultaneously predict accurate energy use at three time scales. The energy predictions also have the same or similar correlation structure as the energy data from the engineering-based EnergyPlus models. Moreover, the results from the multi-time scale BASS models have consistent accumulative features, which means energy use at a larger time scale equals the summation of energy use at a smaller time scale. The multi-output models at various time scales for building energy prediction developed in this research can be used in uncertainty analysis, sensitivity analysis, and calibration of building energy models. Full article
(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Building Energy, Physics, Environment, and Systems I)
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