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

A topical collection in Buildings (ISSN 2075-5309). This collection belongs to the section "Building Energy, Physics, Environment, and Systems".

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Editors


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Collection 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
Department of Civil and Building Engineering, University of Sherbrooke, Sherbrooke, QC, Canada
Interests: building mechanical systems; building energy; fire smoke control
Special Issues, Collections and Topics in MDPI journals

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Collection 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

Topical Collection 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 Topical Collection aims to gather innovative research and development in intelligent buildings to create a low-carbon, healthy, and comfortable building environment. The Topical Collection 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
Collection Editors

Manuscript Submission Information

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Published Papers (14 papers)

2024

Jump to: 2022, 2021

20 pages, 5645 KiB  
Article
Contemporary Evaporative Cooling System with Indirect Interaction in Construction Implementations: A Theoretical Exploration
by Pinar Mert Cuce, Erdem Cuce and Saffa Riffat
Buildings 2024, 14(4), 994; https://doi.org/10.3390/buildings14040994 - 3 Apr 2024
Viewed by 567
Abstract
The construction sector, including in developed countries, plays a notable part in the overall energy consumption worldwide, being responsible for 40% of it. In addition to this, heating, ventilating and air-conditioning (HVAC) systems constitute the largest share in this sector, accounting for 40% [...] Read more.
The construction sector, including in developed countries, plays a notable part in the overall energy consumption worldwide, being responsible for 40% of it. In addition to this, heating, ventilating and air-conditioning (HVAC) systems constitute the largest share in this sector, accounting for 40% of energy usage in construction and 16% globally. To address this, stringent rules and performance measures are essential to reduce energy consumption. This study focuses on mathematical optimisation modelling to enhance the performance of indirect-contact evaporative cooling systems (ICESs), a topic with a significant gap in the literature. This modelling is highly comprehensive, covering various aspects: (1) analysing the impact of the water-spraying unit (WSU) size, working air (WA) velocity and hydraulic diameter (Dh) on the evaporated water vapour (EWV) amount; (2) evaluating temperature and humidity distribution for a range of temperatures without considering humidity at the outlet of the WSU, (3) presenting theoretical calculations of outdoor temperature (Tout) and humidity with a constant WSU size and air mass flow rate (MFR), (4) examining the combined effect of the WA MFR and relative humidity (ϕ) on Tout and (5) investigating how Tout influences the indoor environment’s humidity. The study incorporates an extensive optimisation analysis. The findings indicate that the model could contribute to the development of future low-carbon houses, considering factors such as the impact of Tout on indoor ϕ, the importance of low air velocity for achieving a low air temperature, the positive effects of Dh on outdoor air and the necessity of a WSU with a size of at least 8 m for adiabatic saturation. Full article
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28 pages, 11735 KiB  
Article
Influences of Heat Rejection from Split A/C Conditioners on Mixed-Mode Buildings: Energy Use and Indoor Air Pollution Exposure Analysis
by Xuyang Zhong, Ming Cai, Zhe Wang, Zhiang Zhang and Ruijun Zhang
Buildings 2024, 14(2), 318; https://doi.org/10.3390/buildings14020318 - 23 Jan 2024
Viewed by 617
Abstract
The heat rejected by outdoor units of split A/C conditioners can impact the ambient outdoor environment of mixed-mode buildings. Nevertheless, how this environmental impact may affect the space-conditioning energy use and indoor air pollution is poorly understood. By coupling EnergyPlus and Fluent, this [...] Read more.
The heat rejected by outdoor units of split A/C conditioners can impact the ambient outdoor environment of mixed-mode buildings. Nevertheless, how this environmental impact may affect the space-conditioning energy use and indoor air pollution is poorly understood. By coupling EnergyPlus and Fluent, this study examines the effects of outdoor units’ heat rejection on the building surroundings, building cooling load, and indoor PM2.5 exposure of a six-storey mixed-mode building. The building had an open-plan room on each floor, with the outdoor unit positioned below the window. The coupled model was run for a selected day when the building was cooled by air conditioning and natural ventilation. Five mixed-mode cooling strategies were simulated, reflecting different window-opening schedules, airflow rates of outdoor units, and cooling set-points. The results indicate that compared with the always-air-conditioned mode, the mixed-mode operation could significantly mitigate the negative impact of heat rejection on space-cooling energy consumption. Increasing the airflow rate of outdoor units led to a lower increase in demand for space cooling and lower indoor PM2.5 exposure. If one of the six rooms needs to be cooled to a lower temperature than the others; choosing the bottom-floor room helped achieve more energy savings and better indoor air quality. Full article
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2022

Jump to: 2024, 2021

19 pages, 64307 KiB  
Article
A Hybrid Residential Short-Term Load Forecasting Method Using Attention Mechanism and Deep Learning
by Xinhui Ji, Huijie Huang, Dongsheng Chen, Kangning Yin, Yi Zuo, Zhenping Chen and Rui Bai
Buildings 2023, 13(1), 72; https://doi.org/10.3390/buildings13010072 - 28 Dec 2022
Cited by 3 | Viewed by 1403
Abstract
Development in economics and social society has led to rapid growth in electricity demand. Accurate residential electricity load forecasting is helpful for the transformation of residential energy consumption structure and can also curb global climate warming. This paper proposes a hybrid residential short-term [...] Read more.
Development in economics and social society has led to rapid growth in electricity demand. Accurate residential electricity load forecasting is helpful for the transformation of residential energy consumption structure and can also curb global climate warming. This paper proposes a hybrid residential short-term load forecasting framework (DCNN-LSTM-AE-AM) based on deep learning, which combines dilated convolutional neural network (DCNN), long short-term memory network (LSTM), autoencoder (AE), and attention mechanism (AM) to improve the prediction results. First, we design a T-nearest neighbors (TNN) algorithm to preprocess the original data. Further, a DCNN is introduced to extract the long-term feature. Secondly, we combine the LSTM with the AE (LSTM-AE) to learn the sequence features hidden in the extracted features and decode them into output features. Finally, the AM is further introduced to extract and fuse the high-level stage features to achieve the prediction results. Experiments on two real-world datasets show that the proposed method is good at capturing the oscillation characteristics of low-load data and outperforms other methods. Full article
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22 pages, 6071 KiB  
Article
Deep Forest-Based DQN for Cooling Water System Energy Saving Control in HVAC
by Zhicong Han, Qiming Fu, Jianping Chen, Yunzhe Wang, You Lu, Hongjie Wu and Hongguan Gui
Buildings 2022, 12(11), 1787; https://doi.org/10.3390/buildings12111787 - 25 Oct 2022
Cited by 5 | Viewed by 1444
Abstract
Currently, reinforcement learning (RL) has shown great potential in energy saving in HVAC systems. However, in most cases, RL takes a relatively long period to explore the environment before obtaining an excellent control policy, which may lead to an increase in cost. To [...] Read more.
Currently, reinforcement learning (RL) has shown great potential in energy saving in HVAC systems. However, in most cases, RL takes a relatively long period to explore the environment before obtaining an excellent control policy, which may lead to an increase in cost. To reduce the unnecessary waste caused by RL methods in exploration, we extended the deep forest-based deep Q-network (DF-DQN) from the prediction problem to the control problem, optimizing the running frequency of the cooling water pump and cooling tower in the cooling water system. In DF-DQN, it uses the historical data or expert experience as a priori knowledge to train a deep forest (DF) classifier, and then combines the output of DQN to attain the control frequency, where DF can map the original action space of DQN to a smaller one, so DF-DQN converges faster and has a better energy-saving effect than DQN in the early stage. In order to verify the performance of DF-DQN, we constructed a cooling water system model based on historical data. The experimental results show that DF-DQN can realize energy savings from the first year, while DQN realized savings from the third year. DF-DQN’s energy-saving effect is much better than DQN in the early stage, and it also has a good performance in the latter stage. In 20 years, DF-DQN can improve the energy-saving effect by 11.035% on average every year, DQN can improve by 7.972%, and the model-based control method can improve by 13.755%. Compared with traditional RL methods, DF-DQN can avoid unnecessary waste caused by exploration in the early stage and has a good performance in general, which indicates that DF-DQN is more suitable for engineering practice. Full article
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21 pages, 5230 KiB  
Article
Machine Learning-Based Method for Detached Energy-Saving Residential Form Generation
by Haixu Guo, Ding Duan, Jincheng Yan, Keyuan Ding, Fengkui Xiang and Ran Peng
Buildings 2022, 12(10), 1504; https://doi.org/10.3390/buildings12101504 - 22 Sep 2022
Cited by 3 | Viewed by 2381
Abstract
In recent years, machine learning has gradually been applied to building energy-saving designs to reduce the time consumption of the optimization screening stage. However, since most of the existing research scholars come from the fields of computers and engineering, the application of machine [...] Read more.
In recent years, machine learning has gradually been applied to building energy-saving designs to reduce the time consumption of the optimization screening stage. However, since most of the existing research scholars come from the fields of computers and engineering, the application of machine learning technology mostly involves complex programming as well as software in the field of engineering, which requires multiple software to be coupled to achieve. In view of the differences between disciplines and the high learning threshold, these theories are difficult to apply and promote in practical work in the field of architecture. In this regard, this paper focuses on the improvement of methods, based on the Grasshopper platform, proposes a detached energy-saving residential form generation design method and process, to explore the optimal energy-saving building form in a more concise and efficient way. Based on this new method, on the basis of verifying its feasibility through a residential building case, two machine learning algorithms, neural network (ANN) and support vector machine (SVM), are compared and studied, and the applicability of these two algorithms in different building performance indicators is further discussed. The results show that the ANN model has the highest accuracy and is more suitable for the prediction of building energy consumption; in view of the simple and fast operation of SVM, it is more suitable for comfort prediction with relatively low accuracy requirements. By combining the above two machine learning methods, work efficiency can be improved while satisfying the prediction of relevant performance indicators. This method can help architects quickly search for the best building energy-saving form design scheme in the scheme design stage and provide data support and information feedback for architects in design conception and deepening. Full article
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20 pages, 947 KiB  
Article
Leakage Diagnosis of Air Conditioning Water System Networks Based on an Improved BP Neural Network Algorithm
by Rundong Liu, Yuhang Zhang and Zhengwei Li
Buildings 2022, 12(5), 610; https://doi.org/10.3390/buildings12050610 - 6 May 2022
Cited by 3 | Viewed by 1605
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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9 pages, 652 KiB  
Article
Optimal Control of Chilled Water System Based on Improved Sparrow Search Algorithm
by Qixin Zhu, Mengxiang Zhuang, Hongli Liu and Yonghong Zhu
Buildings 2022, 12(3), 269; https://doi.org/10.3390/buildings12030269 - 24 Feb 2022
Cited by 7 | Viewed by 1999
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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21 pages, 3228 KiB  
Article
Building Energy Consumption Prediction Using a Deep-Forest-Based DQN Method
by Qiming Fu, Ke Li, Jianping Chen, Junqi Wang, You Lu and Yunzhe Wang
Buildings 2022, 12(2), 131; https://doi.org/10.3390/buildings12020131 - 27 Jan 2022
Cited by 22 | Viewed by 3101
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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15 pages, 5587 KiB  
Article
A New Configuration of Roof Photovoltaic System for Limited Area Applications—A Case Study in KSA
by Ayman Al-Quraan, Mohammed Al-Mahmodi, Taha Al-Asemi, Abdulqader Bafleh, Mathhar Bdour, Hani Muhsen and Ahmad Malkawi
Buildings 2022, 12(2), 92; https://doi.org/10.3390/buildings12020092 - 19 Jan 2022
Cited by 9 | Viewed by 3398
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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2021

Jump to: 2024, 2022

16 pages, 6885 KiB  
Article
Numerical Investigation on Energy Efficiency of Heat Pump with Tunnel Lining Ground Heat Exchangers under Building Cooling
by Xiaohua Liu, Chenglin Li, Guozhu Zhang, Linfeng Zhang and Bin Wei
Buildings 2021, 11(12), 611; https://doi.org/10.3390/buildings11120611 - 4 Dec 2021
Cited by 8 | Viewed by 2643
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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21 pages, 2512 KiB  
Article
The Framework of Technical Evaluation Indicators for Constructing Low-Carbon Communities in China
by Yifei Bai, Weirong Zhang, Xiu Yang, Shen Wei and Yang Yu
Buildings 2021, 11(10), 479; https://doi.org/10.3390/buildings11100479 - 15 Oct 2021
Cited by 5 | Viewed by 2653
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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25 pages, 13181 KiB  
Article
Predicting Indoor Temperature Distribution Based on Contribution Ratio of Indoor Climate (CRI) and Mobile Sensors
by Yanan Zhao, Zihan Zang, Weirong Zhang, Shen Wei and Yingli Xuan
Buildings 2021, 11(10), 458; https://doi.org/10.3390/buildings11100458 - 4 Oct 2021
Cited by 5 | Viewed by 1811
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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20 pages, 6812 KiB  
Article
Improving Comfort and Health: Green Retrofit Designs for Sunken Courtyards during the Summer Period in a Subtropical Climate
by Gang Han, Yueming Wen, Jiawei Leng and Lijun Sun
Buildings 2021, 11(9), 413; https://doi.org/10.3390/buildings11090413 - 16 Sep 2021
Cited by 15 | Viewed by 4410
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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13 pages, 2787 KiB  
Article
Detection of District Heating Pipe Network Leakage Fault Using UCB Arm Selection Method
by Yachen Shen, Jianping Chen, Qiming Fu, Hongjie Wu, Yunzhe Wang and You Lu
Buildings 2021, 11(7), 275; https://doi.org/10.3390/buildings11070275 - 27 Jun 2021
Cited by 7 | Viewed by 3424
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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