Special Issue "Creation of a Low-Carbon Healthy Building Environment with Intelligent Technologies"

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: 30 June 2022 | Viewed by 7890

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. Dahai Qi
E-Mail Website
Guest Editor
Department of Civil and Building Engineering, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada
Interests: building ventilation; building energy system; fire smoke control
Special Issues, Collections and Topics in MDPI journals
Dr. Junqi Wang
E-Mail
Guest Editor
School of Environmental Science and Engineering, Suzhou University of Science and Technology, Suzhou, China
School of Architecture, Southeast University, Nanjing, China
Interests: HVAC; Control and Optimization; Demand-Controlled Ventilation; Occupancy Detection; Machine Learning and Computer Vision; Building Energy Management; Building Environment; Low Carbon Heating and Cooling
Dr. Gwanggil Jeon
E-Mail Website
Guest Editor
Department of Embedded Systems Engineering, Incheon National University, Incheon 22012, Korea
Interests: image processing; particularly image compression, motion estimation, demosaicking and image enhancement, and computational intelligence, such as fuzzy and rough sets theories
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The building sector accounts for 1/3 of global carbon emissions. With various decarbonization plans initiated around the world, the need to reduce carbon emissions from buildings is becoming increasingly critical. Aiming to create a healthy and comfortable indoor environment, building systems are designed and operated to provide required services, e.g., HVAC and lighting systems. However, a healthy or comfortable indoor environment is normally associated with high carbon emissions. Building a healthy and comfortable yet low-carbon building environment thus becomes an urgent research challenge. Given the complex interactions among the environment, buildings, and energy systems, optimized building environment solutions require an interdisciplinary endeavor, e.g., building environment, automatic control, architecture, and artificial intelligence. With the rapid development in information and communication technologies, various intelligent monitoring, diagnosing, control, and optimization technology systems have been applied in buildings.

This Special Issue aims to gather innovative research and development in intelligent buildings to create a low-carbon, healthy, and comfortable building environment. The Special Issue covers original research and review studies, including but not limited to:

  • Online monitoring and prediction;
  • Low-cost sensing and detection;
  • Low carbon heating and cooling;
  • Sustainable architecture design;
  • Demand-based control and optimization;
  • Modeling, control, and optimization of HVAC and lighting systems;
  • Measurement and analysis of building energy and environment data;
  • Intelligent control of building integrated renewable energy systems;
  • Artificial intelligence for building energy and environment systems;
  • Power management, video surveillance, data acquisition, and network.

Prof. Dr. Shi-Jie Cao
Dr. Dahai Qi
Dr. Junqi Wang
Dr. Gwanggil Jeon
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 1800 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.

Published Papers (9 papers)

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Research

Article
Leakage Diagnosis of Air Conditioning Water System Networks Based on an Improved BP Neural Network Algorithm
Buildings 2022, 12(5), 610; https://doi.org/10.3390/buildings12050610 - 06 May 2022
Viewed by 326
Abstract
Compared with traditional pipe networks, the complexity of air conditioning water systems (ACWSs) and the alternation of cooling and heating are more likely to cause pipe network leakage. Pipe leakage failure seriously affects the reliability of the air conditioning system, and can cause [...] Read more.
Compared with traditional pipe networks, the complexity of air conditioning water systems (ACWSs) and the alternation of cooling and heating are more likely to cause pipe network leakage. Pipe leakage failure seriously affects the reliability of the air conditioning system, and can cause energy waste or reduce human comfort. In this study, a two-stage leakage fault diagnosis (LFD) method based on an Adam optimization BP neural network algorithm, which locates leakage faults based on the change values of monitoring data from flow meters and pressure sensors in air conditioning water systems, is proposed. In the proposed LFD method, firstly, the ACWS network’s hydraulic model is built on the Dymola platform. At the same time, a cuckoo algorithm is used to identify the pipe network’s characteristics to modify the model, and the experimental results show that the relative error between the model-simulated value and the actual values is no more than 1.5%. Secondly, all possible leakage conditions in the network are simulated by the model, and the dataset is formed according to the change rate of the observed data, and is then used to train the LFD model. The proposed LFD method is verified in a practical project, where the average accuracy of the first-stage LFD model in locating the leaking pipe is 86.96%; The average R2 of the second-stage LFD model is 0.9028, and the average error between the predicted location and its exact location with the second-stage LFD model is 6.3% of the total length of the leaking pipe. The results show that the proposed method provides a feasible and convenient solution for timely and accurate detection of pipe network leakage faults in air conditioning water systems. Full article
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Article
Optimal Control of Chilled Water System Based on Improved Sparrow Search Algorithm
Buildings 2022, 12(3), 269; https://doi.org/10.3390/buildings12030269 - 24 Feb 2022
Viewed by 603
Abstract
Chilled water systems have large time delays and large inertia, and the traditional PID controller has a poor control effect. In this paper, an improved sparrow search algorithm is proposed to optimize the control of chilled water systems. Firstly, the random walk strategy [...] Read more.
Chilled water systems have large time delays and large inertia, and the traditional PID controller has a poor control effect. In this paper, an improved sparrow search algorithm is proposed to optimize the control of chilled water systems. Firstly, the random walk strategy was used to randomly perturb the sparrows to improve the searching ability of the sparrows. Then, a Gauss mutation was added in the iteration process of sparrows to enhance the local search ability. Finally, the values of the PID parameters as obtained by the above methods were substituted into the controller for simulation. The simulation results show that the method proposed in this paper improves the search accuracy of the sparrow search algorithm and effectively solves the problems of large time delays and large inertia in the chilled water system. The method in this paper took the least amount of time for the system to reach the steady state at only 12.75 s. The control effect of the proposed method was also better than that of the improved ant colony optimization algorithm. The rise time was 2.713 s, and the adjustment time was 4.95 s. Full article
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Article
Building Energy Consumption Prediction Using a Deep-Forest-Based DQN Method
Buildings 2022, 12(2), 131; https://doi.org/10.3390/buildings12020131 - 27 Jan 2022
Cited by 2 | Viewed by 660
Abstract
When deep reinforcement learning (DRL) methods are applied in energy consumption prediction, performance is usually improved at the cost of the increasing computation time. Specifically, the deep deterministic policy gradient (DDPG) method can achieve higher prediction accuracy than deep Q-network (DQN), but it [...] Read more.
When deep reinforcement learning (DRL) methods are applied in energy consumption prediction, performance is usually improved at the cost of the increasing computation time. Specifically, the deep deterministic policy gradient (DDPG) method can achieve higher prediction accuracy than deep Q-network (DQN), but it requires more computing resources and computation time. In this paper, we proposed a deep-forest-based DQN (DF–DQN) method, which can obtain higher prediction accuracy than DDPG and take less computation time than DQN. Firstly, the original action space is replaced with the shrunken action space to efficiently find the optimal action. Secondly, deep forest (DF) is introduced to map the shrunken action space to a single sub-action space. This process can determine the specific meaning of each action in the shrunken action space to ensure the convergence of DF–DQN. Thirdly, state class probabilities obtained by DF are employed to construct new states by considering the probabilistic process of shrinking the original action space. The experimental results show that the DF–DQN method with 15 state classes outperforms other methods and takes less computation time than DRL methods. MAE, MAPE, and RMSE are decreased by 5.5%, 7.3%, and 8.9% respectively, and R2 is increased by 0.3% compared to the DDPG method. Full article
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Article
A New Configuration of Roof Photovoltaic System for Limited Area Applications—A Case Study in KSA
Buildings 2022, 12(2), 92; https://doi.org/10.3390/buildings12020092 - 19 Jan 2022
Cited by 3 | Viewed by 462
Abstract
Increased world energy demand necessitates looking for appropriate alternatives to oil and fossil fuel. Countries encourage institutions and households to create their own photovoltaic (PV) systems to reduce spending money in electricity sectors and address environmental issues. Due to high solar radiation in [...] Read more.
Increased world energy demand necessitates looking for appropriate alternatives to oil and fossil fuel. Countries encourage institutions and households to create their own photovoltaic (PV) systems to reduce spending money in electricity sectors and address environmental issues. Due to high solar radiation in the Kingdom of Saudi Arabia (KSA), the government urges people and institutions to establish PV systems as the best promising renewable energy resource in the country. This paper presents an optimal and complete design of a 300 kW PV system installed in a limited rooftop area to feed the needs of the Ministry of Electricity building, which has a high energy consumption. The design has been suggested for two scenarios in terms of adjusting the orientation angles. The available rooftop area allowed to be used is insufficient if a tilt angle of 22o is used, suggested by the designer, so the tilt angle has been adjusted from 22o to 15o to accommodate the available area and meet the required demand with a minimum shading effect. The authors of this paper propose a modified scenario “third scenario” which accommodates the available area and provides more energy than the installed “second scenario”. The proposed panel distribution and the estimated energy for all scenarios are presented in the paper. The possibility of changing tilt angles and the extent of energy production variations are also discussed. Finally, a comparative study between measured and simulated energy is included. The results show that August has the lowest percentage error, with a value of 2.7%, while the highest percentage error was noticed in November. Full article
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Article
Numerical Investigation on Energy Efficiency of Heat Pump with Tunnel Lining Ground Heat Exchangers under Building Cooling
Buildings 2021, 11(12), 611; https://doi.org/10.3390/buildings11120611 - 04 Dec 2021
Cited by 1 | Viewed by 791
Abstract
For mountain tunnels, ground heat exchangers can be integrated into the tunnel lining to extract geothermal energy for building heating and cooling via a heat pump. In recent decades, many researchers only focused on the thermal performance of tunnel lining Ground Heat Exchangers [...] Read more.
For mountain tunnels, ground heat exchangers can be integrated into the tunnel lining to extract geothermal energy for building heating and cooling via a heat pump. In recent decades, many researchers only focused on the thermal performance of tunnel lining Ground Heat Exchangers (GHEs), ignoring the energy efficiency of the heat pump. A numerical model combining the tunnel lining GHEs and heat pump was established to investigate the energy efficiency of the heat pump. The inlet temperature of an absorber pipe was coupled with the cooling load of GHEs in the numerical model, and the numerical results were calibrated using the in situ test data. The energy efficiency ratio (EER) of the heat pump was calculated based on the correlation of the outlet temperature and EER. The heat pump energy efficiencies under different pipe layout types, pipe pitches and pipe lengths were evaluated. The coupling effect of ventilation and groundwater flow on the energy efficiency of heat pump was investigated. The results demonstrate that (i) the absorber pipes arranged along the axial direction of the tunnel have a greater EER than those arranged along the cross direction; (ii) the EER increases exponentially with increasing absorber pipe pitch and length (the influence of the pipe pitch and length on the growth rate of EER fades gradually as wind speed and groundwater flow rate increase); (iii) the influence of groundwater conditions on the energy efficiency of heat pumps is more obvious compared with ventilation conditions. Moreover, abundant groundwater may lead to a negative effect of ventilation on the heat pump energy efficiency. Hence, the coupling effect of ventilation and groundwater flow needs to be considered for the tunnel lining GHEs design. Full article
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Article
The Framework of Technical Evaluation Indicators for Constructing Low-Carbon Communities in China
Buildings 2021, 11(10), 479; https://doi.org/10.3390/buildings11100479 - 15 Oct 2021
Cited by 1 | Viewed by 644
Abstract
In recent years, in order to promote the construction of low-carbon communities (LCCs) in China, many scholars have proposed an evaluation indicator system of LCC. The existing indicator systems are mostly established from the macro perspective of environmental impact and resource conservation, but [...] Read more.
In recent years, in order to promote the construction of low-carbon communities (LCCs) in China, many scholars have proposed an evaluation indicator system of LCC. The existing indicator systems are mostly established from the macro perspective of environmental impact and resource conservation, but few are from the micro technical perspective. Thus, the aim of this study is to construct a micro technical evaluation indicator system for LCCs. Firstly, the index system was divided into three categories: low-carbon building, low-carbon transportation, and low-carbon environment. Then, the technical indicators were selected through empirical analysis. The indicator weights were assigned by the improved analytic hierarchy process (AHP) and the multi-level fuzzy comprehensive evaluation method was used as the evaluation method of the indicators. Finally, in order to examine the practicality of the indicator system, two typical communities in Tianjin and Shanghai were selected as case studies. The results showed that the indicator system gave a reasonable low-carbon level for the two communities, which was in line with the actual low-carbon construction status of each community. In addition, the evaluation results pointed out that the low-carbon community (LCC) in Tianjin needs to further strengthen the construction of the low-carbon environment, including community compactness, rainwater collection and utilization, and waste recycling. For the LCC in Shanghai, it was pointed out that the construction of the low-carbon building and low-carbon transportation needs to be strengthened. The indicator system can be used as a tool for urban planning and construction personnel to evaluate the construction progress and low-carbon degree of LCC. Full article
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Article
Predicting Indoor Temperature Distribution Based on Contribution Ratio of Indoor Climate (CRI) and Mobile Sensors
Buildings 2021, 11(10), 458; https://doi.org/10.3390/buildings11100458 - 04 Oct 2021
Cited by 1 | Viewed by 621
Abstract
In practical building control, quickly obtaining detailed indoor temperature distribution is necessary for providing satisfying personal comfort and improving building energy efficiency. The aim of this study is to propose a fast prediction method for indoor temperature distribution without knowing the thermal boundary [...] Read more.
In practical building control, quickly obtaining detailed indoor temperature distribution is necessary for providing satisfying personal comfort and improving building energy efficiency. The aim of this study is to propose a fast prediction method for indoor temperature distribution without knowing the thermal boundary conditions in practical applications. In this method, the index of contribution ratio of indoor climate (CRI), which represents the independent contribution of each heat source to the temperature distribution, has been combined with the air temperature collected by one mobile sensor at the height of the working area. Based on a typical office model, the effectiveness of using mobile sensors was discussed, and the influence of its acquisition height and acquisition distance on the prediction accuracy was analyzed as well. The results showed that the proposed prediction method was effective. When the sensors fixed on the wall were used to predict the indoor temperature distribution, the maximum average relative error was 27.7%, whereas when the mobile sensor was used to replace the fixed sensors, the maximum average relative error was 4.8%. This indicates that using mobile sensors with flexible acquisition location can help promote both reliability and accuracy of temperature prediction. In the human activity area, data from a set of mobile sensors were used to predict the temperature distribution at four heights. The prediction accuracy was 2.1%, 2.1%, 2.3%, and 2.7%, respectively. However, the influence of acquisition distance of mobile sensors on prediction accuracy cannot be ignored. The distance should be large enough to disperse the distribution of the acquisition points. Due to the influence of airflow, some distance between the acquisition points and the room boundaries should be given. Full article
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Article
Improving Comfort and Health: Green Retrofit Designs for Sunken Courtyards during the Summer Period in a Subtropical Climate
Buildings 2021, 11(9), 413; https://doi.org/10.3390/buildings11090413 - 16 Sep 2021
Cited by 3 | Viewed by 986
Abstract
The sunken courtyard has long been used in underground spaces and provides an important outdoor environment. It introduces natural elements to create a pleasant space for human activities. However, this study measured a typical sunken courtyard and found potential problems of excessive solar [...] Read more.
The sunken courtyard has long been used in underground spaces and provides an important outdoor environment. It introduces natural elements to create a pleasant space for human activities. However, this study measured a typical sunken courtyard and found potential problems of excessive solar radiation and accumulated air pollutants in summer when at an acceptable outdoor temperature for human activities. To improve the comfort and health of a sunken courtyard, this research proposes some green retrofit designs. Firstly, compared with green wall, water and a tree, sunshade is a primary measure to improve thermal comfort. Combining sunshade, a green wall and water reduces the temperature by up to 5.6 °C in the activity zone during the hottest hour. Secondly, blocking/guiding wind walls can effectively improve the wind environment in a sunken courtyard, but only when the wind direction is close to the prevailing wind. A blocking wind wall was better at affecting velocity and uniformity, while the guiding wind wall was more efficient at discharging air pollutants. This study initially discusses the climate-adaptive design of underground spaces in terms of green, thermal comfort and natural ventilation. Designers should generally integrate above/underground and indoor/outdoor spaces using natural and artificial resources to improve comfort and health in underground spaces. Full article
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Article
Detection of District Heating Pipe Network Leakage Fault Using UCB Arm Selection Method
Buildings 2021, 11(7), 275; https://doi.org/10.3390/buildings11070275 - 27 Jun 2021
Cited by 3 | Viewed by 1030
Abstract
District heating networks make up an important public energy service, in which leakage is the main problem affecting the safety of pipeline network operation. This paper proposes a Leakage Fault Detection (LFD) method based on the Linear Upper Confidence Bound (LinUCB) which is [...] Read more.
District heating networks make up an important public energy service, in which leakage is the main problem affecting the safety of pipeline network operation. This paper proposes a Leakage Fault Detection (LFD) method based on the Linear Upper Confidence Bound (LinUCB) which is used for arm selection in the Contextual Bandit (CB) algorithm. With data collected from end-users’ pressure and flow information in the simulation model, the LinUCB method is adopted to locate the leakage faults. Firstly, we use a hydraulic simulation model to simulate all failure conditions that can occur in the network, and these change rate vectors of observed data form a dataset. Secondly, the LinUCB method is used to train an agent for the arm selection, and the outcome of arm selection is the leaking pipe label. Thirdly, the experiment results show that this method can detect the leaking pipe accurately and effectively. Furthermore, it allows operators to evaluate the system performance, supports troubleshooting of decision mechanisms, and provides guidance in the arrangement of maintenance. Full article
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