AI and Data Analytics for Energy-Efficient and Healthy Buildings

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 (29 February 2024) | Viewed by 16630

Special Issue Editors


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Guest Editor
Data-Centric Engineering, The Alan Turing Institute, London NW1 2DB, UK
Interests: air-conditioning systems; energy efficiency in buildings; AI and data analytics for the built environment
Department of Civil Engineering, Faculty of Engineering Sciences, KU Leuven, 3000 Leuven, Belgium
Interests: building/district energy use modelling; indoor environment; building performance improvement; advanced control of building energy systems

E-Mail Website
Guest Editor
Department of Building Environment and Energy Engineering, The Hong Kong Polytechnic University, Hong Kong 100872, China
Interests: built environment; building envelope; building ventilation; smart buildings
School of Marine Equipment and Mechanical Engineering, Jimei University, Xiamen 361021, China
Interests: hybrid air-conditioning systems; energy conservation in buildings; numerical modelling and optimization; sustainable energy technologies for built environment

Special Issue Information

Dear Colleagues,

The COVID-19 pandemic is revolutionizing building designs, operations and commissioning, with an increased emphasis on healthier, smarter and more efficient environments. With the increasing penetration of smart sensors, increasing electrification of buildings and overwhelming amounts of data, artificial intelligence (AI) and big data analytics have shown extraordinary potential for building performance improvement. However, the actual performance of emerging technologies has not been fully tested due to the complex, interdependent, time-dependent stochastic nature of building systems spanning various types, functions, vintages and climates. 

In the context of this Special Issue, paper submissions related to the application of AI and data analytics for the built environment are welcome, especially in the smart buildings and smart cities domains. Topics of interest include, but are not limited to: smart digital technology for energy conservation and COVID-19 prevention; transfer learning for modelling, diagnosis and optimization in smart buildings; probabilistic modelling and risk-based decision support for building energy systems; data-driven ensemble AI models for energy and infection risk forecast; big data analytics for building and facility management; etc.

The purpose of the Special Issue is to develop AI-based guidelines and protocols for built environments, responding better to the ongoing pandemic, carbon reduction and future emergencies.

Dr. Chaoqun Zhuang
Dr. Rui Guo
Dr. Chong Zhang
Dr. Yunran Min
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

  • energy-efficient and healthy buildings
  • AI and data analytics
  • data-driven modelling
  • smart digital technology
  • transfer learning
  • reinforcement learning
  • building decarbonization

Published Papers (17 papers)

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22 pages, 8353 KiB  
Article
Workflow for Window Composition Detection to Aid Energy-Efficient Renovation in Low-Income Housing in Korea
by Jong-Won Lee
Buildings 2024, 14(4), 966; https://doi.org/10.3390/buildings14040966 - 01 Apr 2024
Viewed by 600
Abstract
Enhancing the efficiency of windows is important for improving the energy efficiency of buildings. The Korean government has performed numerous building renovation projects to reduce greenhouse gas emissions and mitigate energy poverty. To reduce the costs and manpower requirements of conventional field surveys, [...] Read more.
Enhancing the efficiency of windows is important for improving the energy efficiency of buildings. The Korean government has performed numerous building renovation projects to reduce greenhouse gas emissions and mitigate energy poverty. To reduce the costs and manpower requirements of conventional field surveys, this study presents a deep-learning model to examine the insulation performance of windows using photographs taken in low-income housing. A smartphone application using crowdsourcing was developed for data collection. The insulation performance of windows was determined based on U-value, derived considering the frame-material type, number of panes, and area of windows. An image-labeling tool was designed to identify and annotate window components within photographs. Furthermore, software utilizing open-source computer vision was developed to estimate the window area. After training on a dataset with ResNet and EfficientNet, an accuracy of approximately 80% was achieved. Thus, this study introduces a novel workflow to evaluate the insulation performance of windows, which can support the energy-efficient renovation of low-income housing. Full article
(This article belongs to the Special Issue AI and Data Analytics for Energy-Efficient and Healthy Buildings)
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13 pages, 3121 KiB  
Article
A Filling Method Based on K-Singular Value Decomposition (K-SVD) for Missing and Abnormal Energy Consumption Data of Buildings
by Lihong Su, Manjia Liu, Zaixun Ling, Wenjie Gang, Chong Zhang, Ying Zhang and Xiuxia Hao
Buildings 2024, 14(3), 696; https://doi.org/10.3390/buildings14030696 - 05 Mar 2024
Viewed by 567
Abstract
Massive data can be collected from meters to analyze the energy use behavior and detect the operation problems of buildings. However, missing and abnormal data often occur for the raw data. Effective data filling and smoothing methods are required to improve data quality [...] Read more.
Massive data can be collected from meters to analyze the energy use behavior and detect the operation problems of buildings. However, missing and abnormal data often occur for the raw data. Effective data filling and smoothing methods are required to improve data quality before conducting the analysis. This paper introduces a data filling method based on K-SVD. The complete dictionary is trained and then utilized to reconstruct incomplete samples to fill the missing or abnormal data. The impacts of the dictionary size, the data missing continuity, and the sample size on the performance of the proposed method are studied. The results show that a smaller dictionary size is recommended considering the computational complexity and accuracy. The K-SVD method outperforms traditional methods, showing a reduction in the MAPE and CVRMSE by 3.8–5.4% and 6.7–87.8%. The proposed K-SVD filling method performs better for non-consecutive missing data, with an improvement in the MAPE and CVRMSE by 0.1–4% and 5.1–6.7%. Smaller training samples are recommended. The method proposed in this study would provide an effective solution for data preprocessing in building and energy systems. Full article
(This article belongs to the Special Issue AI and Data Analytics for Energy-Efficient and Healthy Buildings)
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14 pages, 3392 KiB  
Article
Examining Recognition of Occupants’ Cooking Activity Based on Sound Data Using Deep Learning Models
by Yuhwan Kim, Chang-Ho Choi, Chang-Young Park and Seonghyun Park
Buildings 2024, 14(2), 515; https://doi.org/10.3390/buildings14020515 - 13 Feb 2024
Viewed by 501
Abstract
In today’s society, where people spend over 90% of their time indoors, indoor air quality (IAQ) is crucial for sustaining human life. However, as various indoor activities such as cooking generate diverse types of pollutants in indoor spaces, IAQ has emerged as a [...] Read more.
In today’s society, where people spend over 90% of their time indoors, indoor air quality (IAQ) is crucial for sustaining human life. However, as various indoor activities such as cooking generate diverse types of pollutants in indoor spaces, IAQ has emerged as a serious issue. Previous studies have employed methods such as CO2 sensors, smart floor systems, and video-based pattern recognition to distinguish occupants’ activities; however, each method has its limitations. This study delves into the classification of occupants’ cooking activities using sound recognition technology. Four deep learning-based sound recognition models capable of recognizing and classifying sounds generated during cooking were presented and analyzed. Experiments were carried out using sound data collected from real kitchen environments and online data-sharing websites. Additionally, changes in performance according to the amount of collected data were observed. Among the developed models, the most efficient is found to be the convolutional neural network, which is relatively unaffected by fluctuations in the amount of sound data and consistently delivers excellent performance. In contrast, other models exhibited a tendency for reduced performance as the amount of sound data decreased. Consequently, the results of this study offer insights into the classification of cooking activities based on sound data and underscore the research potential for sound-based occupant behavior recognition classification models. Full article
(This article belongs to the Special Issue AI and Data Analytics for Energy-Efficient and Healthy Buildings)
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21 pages, 6538 KiB  
Article
Nearly Zero-Energy Building Load Forecasts through the Competition of Four Machine Learning Techniques
by Haosen Qin, Zhen Yu, Zhengwei Li, Huai Li and Yunyun Zhang
Buildings 2024, 14(1), 147; https://doi.org/10.3390/buildings14010147 - 07 Jan 2024
Viewed by 673
Abstract
Heating, ventilation and air conditioning (HVAC) systems account for approximately 50% of the total energy consumption in buildings. Advanced control and optimal operation, seen as key technologies in reducing the energy consumption of HVAC systems, indispensably rely on an accurate prediction of the [...] Read more.
Heating, ventilation and air conditioning (HVAC) systems account for approximately 50% of the total energy consumption in buildings. Advanced control and optimal operation, seen as key technologies in reducing the energy consumption of HVAC systems, indispensably rely on an accurate prediction of the building’s heating/cooling load. Therefore, the goal of this research is to develop a model capable of making such accurate predictions. To streamline the process, this study employs sensitivity and correlation analysis for feature selection, thereby eliminating redundant parameters, and addressing distortion problems caused by multicollinearity among input parameters. Four model identification methods including multivariate polynomial regression (MPR), support vector regression (SVR), multilayer perceptron (MLP), and extreme gradient boosting (XGBoost) are implemented in parallel to extract value from diverse building datasets. These models are trained and selected autonomously based on statistical performance criteria. The prediction models were deployed in a nearly zero-energy office building, and the impacts of feature selection, training set size, and real-world uncertainty factors were analyzed and compared. The results showed that feature selection considerably improved prediction accuracy while reducing model dimensionality. The research also recognized that prediction accuracy during model deployment can be influenced significantly by factors like personnel mobility during holidays and weather forecast uncertainties. Additionally, for nearly zero-energy buildings, the thermal inertia of the building itself can considerably impact prediction accuracy in certain scenarios. Full article
(This article belongs to the Special Issue AI and Data Analytics for Energy-Efficient and Healthy Buildings)
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28 pages, 12226 KiB  
Article
Exploring the Layout Optimization of Commercial Service Space Affecting Energy Consumption in Communities Using Machine Learning
by Yiwen Liu, Chunlu Liu, Xiaolong Wang, Junjie Zhang, Yang Yang and Yi Wang
Buildings 2024, 14(1), 108; https://doi.org/10.3390/buildings14010108 - 31 Dec 2023
Viewed by 769
Abstract
The current design of commercial service spaces in many communities faces issues like incoherence, irrational resource allocation, and low utilization rates. These challenges contribute to increased energy consumption in communities, hindering the overall sustainable development of cities. As a representative community space in [...] Read more.
The current design of commercial service spaces in many communities faces issues like incoherence, irrational resource allocation, and low utilization rates. These challenges contribute to increased energy consumption in communities, hindering the overall sustainable development of cities. As a representative community space in the urban environment, the commercial space within the campus requires continuous energy input. Its energy-efficient layout aligns with the principles of sustainable development. This paper uses the university campus as a case study to examine energy-efficient commercial space layout and community practices for environmental protection. Various factors influence the layout of inter-community commercial spaces, and the parameters for measuring the layout structure are diverse, considering the large sample size. Employing machine learning and big data processing to quantify development indicators across various industries and optimize their structure, resource allocation, and energy use has emerged as a viable tool for sustainable urban planning practices. This research seeks to utilize machine learning and data-driven optimization techniques to formulate a comprehensive framework for the sustainable allocation and design of business service spaces within communities. Firstly, we conduct a comprehensive investigation, which includes data collected by applying questionnaire surveys and field research, to assess and model the factors influencing the spatial layout of commercial services on university campuses. Secondly, the AEL machine learning model is constructed by combining the analytic hierarchy process to determine subjective weights, the entropy weight method to calculate objective weights, and the Lagrange algorithm to determine comprehensive weights. Thirdly, we assess and improve the layout of commercial service spaces. Then, by training and testing the Neural Network Model, we apply cases to ensure the accuracy of the machine learning calculation results. Qualitative analysis elucidates the varying factors influencing the sustainable layout of different commercial spaces. Quantitative analysis indicates that, within university campuses, the distance between commercial service spaces and residence halls is a crucial factor in fostering a more sustainable layout. Other significant factors include their location along major student routes and proximity to teaching areas. This study makes contributions not only to the specific field of optimizing commercial service space in communities but also to the broader discourse on sustainable urban development. It advances our understanding of the complex dynamics involved in crafting urban environments that are both efficient and environmentally friendly. Beyond theoretical considerations, the study provides practical solutions and recommendations applicable to implementing tangible improvements in resource allocation. These contributions aim to foster urban environments that are not only environmentally conscious but also economically viable. Full article
(This article belongs to the Special Issue AI and Data Analytics for Energy-Efficient and Healthy Buildings)
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22 pages, 8486 KiB  
Article
Application of Deep Reinforcement Learning for Proportional–Integral–Derivative Controller Tuning on Air Handling Unit System in Existing Commercial Building
by Dongkyu Lee, Jinhwa Jeong and Young Tae Chae
Buildings 2024, 14(1), 66; https://doi.org/10.3390/buildings14010066 - 25 Dec 2023
Viewed by 823
Abstract
An effective control of air handling unit (AHU) systems is crucial not only for managing the energy consumption of buildings but ensuring indoor thermal comfort for occupants. Although the initial control schema of AHU is appropriate at installation and testing, it is frequently [...] Read more.
An effective control of air handling unit (AHU) systems is crucial not only for managing the energy consumption of buildings but ensuring indoor thermal comfort for occupants. Although the initial control schema of AHU is appropriate at installation and testing, it is frequently necessary to adjust the control variables due to the changing thermal response of the building envelope and space usage. This paper presents a novel optimization process for the control parameters of old AHU systems in existing commercial buildings without system downtime and massive operational data. First, calibrating the building and system simulator with limited system operation data and unknown building parameters can provide identical responses to the system operation with the Hooke–Jeeves algorithm during the cooling season. The deep deterministic policy gradient algorithm is employed to determine the optimal control parameters for the valve opening position of the cooling coil within less than three hours of training based on the calibrated simulator. By using actual implementations with the developed optimal control variables for an old AHU in a real building, the proposed auto-tuned PID control in the simulator and with machine learning improves thermal environments with a steady room temperature (23.5 ± 0.5 °C) by 97% in occupied periods. It is also proved that this can reduce cooling energy consumption by up to 13.71% on a daily average. The successful AHU controller can improve not only the stability of AHU systems but the efficiency of a building’s energy use and indoor thermal comfort. Full article
(This article belongs to the Special Issue AI and Data Analytics for Energy-Efficient and Healthy Buildings)
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24 pages, 5979 KiB  
Article
The Performance of Reinforcement Learning for Indoor Climate Control Devices according to the Level of Outdoor Air Particulate Matters
by Sun Ho Kim and Hyeun Jun Moon
Buildings 2023, 13(12), 3062; https://doi.org/10.3390/buildings13123062 - 08 Dec 2023
Viewed by 754
Abstract
As people spend more than 90% of their time indoors, indoor environmental quality (IEQ) has become an important factor in maintaining a healthy space for the occupants. There are many indoor climate control devices for improving IEQ. However, it is difficult to maintain [...] Read more.
As people spend more than 90% of their time indoors, indoor environmental quality (IEQ) has become an important factor in maintaining a healthy space for the occupants. There are many indoor climate control devices for improving IEQ. However, it is difficult to maintain an appropriate IEQ with changing outdoor air conditions and occupant behavior in a building. This study proposes a reinforcement learning (RL) algorithm to maintain indoor air quality (IAQ) with low energy consumption in a residential environment by optimally operating indoor climate control devices such as ventilation systems, air purifiers, and kitchen hoods. The proposed artificial intelligence algorithm (AI2C2) employs DDQN (double deep Q-network) to determine the optimal operation of various indoor climate control devices, reflecting IAQ and energy consumption via different outdoor levels of particulate matter. This approach considers the outdoor air condition and occupant activities in training the developed algorithm, which are the most significant factors affecting IEQ and building energy performance. A co-simulation platform using Python and EnergyPlus is applied to train and evaluate the model. As a result, the proposed approach reduced energy consumption and maintained good IAQ. The developed RL algorithm for energy and IAQ showed different performances based on the outdoor PM 2.5 level. The results showed the RL-based control can be more effective when the outdoor PM 2.5 level is higher (or unhealthy) compared to moderate (or healthy) conditions. Full article
(This article belongs to the Special Issue AI and Data Analytics for Energy-Efficient and Healthy Buildings)
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21 pages, 9151 KiB  
Article
Effects and Improvements in Carpentry for Thermal Comfort in Educational Spaces in Andean Mild Equatorial Climate
by Jessica Paltán-Cuenca, Esteban Zalamea-León, Mateo Astudillo-Flores, Alfredo Ordoñez-Castro and Edgar A. Barragan-Escandón
Buildings 2023, 13(12), 3049; https://doi.org/10.3390/buildings13123049 - 07 Dec 2023
Viewed by 667
Abstract
Environmental comfort is fundamental for teaching and learning processes. This work focuses on identifying shortcomings and proposing improvements for educational buildings in the Andean equatorial climate. A quantitative experimental methodology was employed, which included collecting thermal comfort data to calibrate the use of [...] Read more.
Environmental comfort is fundamental for teaching and learning processes. This work focuses on identifying shortcomings and proposing improvements for educational buildings in the Andean equatorial climate. A quantitative experimental methodology was employed, which included collecting thermal comfort data to calibrate the use of the DesignBuilder v7 environmental simulation software. Issues with thermal weakness in the carpentry were identified, both due to the choice of materials and construction sealing. These are common weaknesses that arise in the context of the Andean Ecuadorian climate, but which affect moments of thermal discomfort during study hours. With the calibrated simulator, thermal improvements achievable by working on the carpentry to reduce infiltrations by half and improving glazing with double-glazed and triple-glazed windows, achieving even uniformity in thermal transmittance compared to other envelope materials, were analyzed. By reducing infiltrations alone, the average temperature increased by between 1.07 °C and 1.61 °C, surpassing the minimum comfort threshold and remaining within locally accepted temperatures throughout the day. With very-high-standard glazing, additional improvements are made, increasing the average temperature by an additional 0.30 °C to 0.69 °C, resulting in a less efficient alternative. Full article
(This article belongs to the Special Issue AI and Data Analytics for Energy-Efficient and Healthy Buildings)
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14 pages, 3544 KiB  
Article
Anomaly Detection Based on LSTM Learning in IoT-Based Dormitory for Indoor Environment Control
by Seol-Hyun Noh and Hyeun Jun Moon
Buildings 2023, 13(11), 2886; https://doi.org/10.3390/buildings13112886 - 19 Nov 2023
Viewed by 716
Abstract
This study focuses on gathering environmental data concerning the indoor climate within a dormitory, encompassing variables such as air temperature, relative humidity, CO2 concentration, fine dust concentration, illuminance, and total volatile organic compounds. Subsequently, an anomaly detection long short-term memory model (LSTM) [...] Read more.
This study focuses on gathering environmental data concerning the indoor climate within a dormitory, encompassing variables such as air temperature, relative humidity, CO2 concentration, fine dust concentration, illuminance, and total volatile organic compounds. Subsequently, an anomaly detection long short-term memory model (LSTM) model, utilizing a two-stacked LSTM model, was developed and trained to enhance indoor environment control. The study demonstrated that the trained model effectively identified anomalies within eight environmental variables. Graphical representations illustrate the model’s accuracy in anomaly detection. The trained model has the capacity to monitor indoor environmental data collected and transmitted using an Internet-of-Things sensor. In the event of an anomaly domain prediction, it proactively alerts the building manager, facilitating timely indoor environment control. Furthermore, the model can be seamlessly integrated into indoor environment control systems to actively detect anomalies, thereby contributing to the automation of control processes. Full article
(This article belongs to the Special Issue AI and Data Analytics for Energy-Efficient and Healthy Buildings)
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19 pages, 3937 KiB  
Article
Alternative Approaches to HVAC Control of Chat Generative Pre-Trained Transformer (ChatGPT) for Autonomous Building System Operations
by Ki Uhn Ahn, Deuk-Woo Kim, Hyun Mi Cho and Chang-U Chae
Buildings 2023, 13(11), 2680; https://doi.org/10.3390/buildings13112680 - 24 Oct 2023
Cited by 1 | Viewed by 1336
Abstract
Artificial intelligence (AI) technology has rapidly advanced and transformed the nature of scientific inquiry. The recent release of the large language model Chat Generative Pre-Trained Transformer (ChatGPT) has attracted significant attention from the public and various industries. This study applied ChatGPT to autonomous [...] Read more.
Artificial intelligence (AI) technology has rapidly advanced and transformed the nature of scientific inquiry. The recent release of the large language model Chat Generative Pre-Trained Transformer (ChatGPT) has attracted significant attention from the public and various industries. This study applied ChatGPT to autonomous building system operations, specifically coupling it with an EnergyPlus reference office building simulation model. The operational objective was to minimize the energy use of the building systems, including four air-handling units, two chillers, a cooling tower, and two pumps, while ensuring that indoor CO2 concentrations remain below 1000 ppm. The performance of ChatGPT in an autonomous operation was compared with control results based on a deep Q-network (DQN), which is a reinforcement learning method. The ChatGPT and DQN lowered the total energy use by 16.8% and 24.1%, respectively, compared with the baseline operation, while maintaining an indoor CO2 concentration below 1000 ppm. Notably, compared with the DQN, ChatGPT-based control does not require a learning process to develop intelligence for building control. In real-world applications, the high generalization capabilities of the ChatGPT-based control, resulting from its extensive training on vast and diverse data, could potentially make it more effective. Full article
(This article belongs to the Special Issue AI and Data Analytics for Energy-Efficient and Healthy Buildings)
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20 pages, 8501 KiB  
Article
Hourly Heat Load Prediction for Residential Buildings Based on Multiple Combination Models: A Comparative Study
by Wenhan An, Xiangyuan Zhu, Kaimin Yang, Moon Keun Kim and Jiying Liu
Buildings 2023, 13(9), 2340; https://doi.org/10.3390/buildings13092340 - 14 Sep 2023
Cited by 1 | Viewed by 724
Abstract
The accurate prediction of residential heat load is crucial for effective heating system design, energy management, and cost optimization. In order to further improve the prediction accuracy of the model, this study introduced principal component analysis (PCA), the minimum sum of squares of [...] Read more.
The accurate prediction of residential heat load is crucial for effective heating system design, energy management, and cost optimization. In order to further improve the prediction accuracy of the model, this study introduced principal component analysis (PCA), the minimum sum of squares of the combined prediction errors (minSSE), genetic algorithm (GA), and firefly algorithm (FA) into back propagation (BP) and ELMAN neural networks, and established three kinds of combined prediction models. The proposed methodologies are evaluated using real-world data collected from residential buildings over a period of one year. The obtained results of the PCA-BP-ELMAN, FA-ELMAN, and GA-BP models are compared with the neural network before optimization. The experimental results show that the combined prediction models have higher prediction accuracy. The Mean Absolute Percentage Error (MAPE) evaluation indices of the three combined models are distributed between 5.95% and 7.05%. The FA-ELMAN model is the combination model with the highest prediction accuracy, and its MAPE is 5.95%, which is 2.25% lower than the MAPE of an individual neural network. This research contributes to the field by providing a comprehensive and effective framework for residential heat load prediction, which can be valuable for building energy management and optimization. Full article
(This article belongs to the Special Issue AI and Data Analytics for Energy-Efficient and Healthy Buildings)
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19 pages, 3664 KiB  
Article
Improved Data-Driven Building Daily Energy Consumption Prediction Models Based on Balance Point Temperature
by Hao Yang, Maoyu Ran and Haibo Feng
Buildings 2023, 13(6), 1423; https://doi.org/10.3390/buildings13061423 - 31 May 2023
Cited by 1 | Viewed by 1046
Abstract
The data-driven models have been widely used in building energy analysis due to their outstanding performance. The input variables of the data-driven models are crucial for their predictive performance. Therefore, it is meaningful to explore the input variables that can improve the predictive [...] Read more.
The data-driven models have been widely used in building energy analysis due to their outstanding performance. The input variables of the data-driven models are crucial for their predictive performance. Therefore, it is meaningful to explore the input variables that can improve the predictive performance, especially in the context of the global energy crisis. In this study, an algorithm for calculating the balance point temperature was proposed for an apartment community in Xiamen, China. It was found that the balance point temperature label (BPT label) can significantly improve the daily energy consumption prediction accuracy of five data-driven models (BPNN, SVR, RF, LASSO, and KNN). Feature importance analysis showed that the importance of the BPT label accounts for 25%. Among all input variables, the daily minimum temperature is the decisive factor that affects energy consumption, while the daily maximum temperature has little impact. In addition, this study also provides recommendations for selecting these model tools under different data conditions: when the input variable data is insufficient, KNN has the best predictive performance, while BPNN is the best model when the input data is sufficient. Full article
(This article belongs to the Special Issue AI and Data Analytics for Energy-Efficient and Healthy Buildings)
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19 pages, 22515 KiB  
Article
Numerical Simulation of Airflow in the Main Cable of Suspension Bridge with FPM Model
by Wenhao Sui, Zhihang Guo, Hua Guan, Pei Peng, Qun Liu, Xiaochen Zhang and Xiangdong Cheng
Buildings 2023, 13(6), 1422; https://doi.org/10.3390/buildings13061422 - 31 May 2023
Viewed by 1038
Abstract
The main cable of suspension bridges is subject to corrosion and requires advanced anti-corrosion technology. Consequently, the internal airflow of the main cable has become a significant research focus. This study employs image processing and machine learning to analyze the cross-sectional images of [...] Read more.
The main cable of suspension bridges is subject to corrosion and requires advanced anti-corrosion technology. Consequently, the internal airflow of the main cable has become a significant research focus. This study employs image processing and machine learning to analyze the cross-sectional images of the main cable and reveals the distribution characteristics of pores and fractures within the main cable cross-section. The numerical simulation model of the main cable is divided into inner and outer parts based on porosity, with porosity levels of 18.16% and 32.11%, respectively. Fractures randomly occurred in the inner part, with a probability of 31.37%. A simulation model based on fractured porous media (FPM) is developed, which innovatively incorporates the fracture flow model into the numerical simulation of the internal airflow of the main cable. The numerical simulation clearly explores the intricate details of the internal flow field of the main cable, revealing that the existence of fractures has a great impact on the internal flow field of the main cable. Additionally, the relative deviation of specific frictional head loss between the field experiment and numerical simulation is about 6.83%, indicating that the numerical simulation results are relatively reliable. Full article
(This article belongs to the Special Issue AI and Data Analytics for Energy-Efficient and Healthy Buildings)
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21 pages, 5634 KiB  
Article
Parameter Selection for the Dehumidification System of the Main Cable of Suspension Bridge Based on Ventilation Experiments
by Zhihang Guo, Wenhao Sui, Hua Guan, Pei Peng, Qun Liu and Jianzhong Shi
Buildings 2023, 13(6), 1386; https://doi.org/10.3390/buildings13061386 - 26 May 2023
Viewed by 1117
Abstract
Moisture in the main cable is the main cause of steel wire corrosion. Modern suspension bridges utilize main cable dehumidification systems to prevent corrosion. The main cable ventilation experiment can help the selection of the system parameters. This research is based on the [...] Read more.
Moisture in the main cable is the main cause of steel wire corrosion. Modern suspension bridges utilize main cable dehumidification systems to prevent corrosion. The main cable ventilation experiment can help the selection of the system parameters. This research is based on the ventilation experiment of the main cable of Xihoumen Bridge to obtain the design parameters of the dehumidification system. According to the experiment, the suitable dehumidification distance is 150–180 m; the pressure loss of the main cable with a length of 150 m is 200–300 Pa, so the inlet pressure should be higher than 300 Pa; increasing the outlet clamp can increase the dehumidification efficiency; Under single inlet and double outlet situation, every 100% increase in air volume increases the dehumidification capacity is about 35%. The water content of the main cable of Xihoumen Bridge is 5.74 kg/m3, and 1 m3 of dry air can remove 5.5 g of water under experimental conditions, and the minimum air volume is 30 m3/h. The main factors affecting the dehumidification time are air volume and leakage rate. Input these parameters into the dehumidification system for the dehumidification experiment, and the water content of the outlet clamp will drop by about 37.5% within ten days. Full article
(This article belongs to the Special Issue AI and Data Analytics for Energy-Efficient and Healthy Buildings)
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23 pages, 7329 KiB  
Article
Classification of Low-Strain Foundation Pile Testing Signal Using Recurrent Neural Network
by Haiyuan Wang, Shen Zhang, Jianmin Li, Yang Yuan and Feng Zhang
Buildings 2023, 13(5), 1228; https://doi.org/10.3390/buildings13051228 - 07 May 2023
Viewed by 1482
Abstract
The testing of the foundation pile is an important means to ensure the quality of the foundation pile in the construction process, and the low-strain pile test is one of the most commonly used testing technologies. However, in order to ensure that the [...] Read more.
The testing of the foundation pile is an important means to ensure the quality of the foundation pile in the construction process, and the low-strain pile test is one of the most commonly used testing technologies. However, in order to ensure that the testing signal is effective and reliable, it is necessary to provide the preliminary judgment results when acquiring the testing signal in the field. In this paper, we propose a data classification method for low-strain pile testing data using a recurrent neural network as the core. In this method, after identification, tailoring, and normalization, the input feature vector with a sequential structure is sent into this model. The model ensures the efficient use of data values while considering the sequential relationship among the data. At last, we designed and produced one complete model pile and six asymmetric model piles, which can form thirteen kinds of testing signals. The optimal application model was selected by the 10-fold cross verification method, and the influence of increasing the input feature dimension on the accuracy was discussed. Finally, compared with the other two methods, this model has the highest accuracy, at 98.46%, but it requires more training parameters and a longer training time. Full article
(This article belongs to the Special Issue AI and Data Analytics for Energy-Efficient and Healthy Buildings)
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18 pages, 4875 KiB  
Article
Energy Consumption Patterns and Characteristics of College Dormitory Buildings Based on Unsupervised Data Mining Method
by Yunchun Yang, Wenjie Gang, Jiaqi Yuan, Zhenying Zhang and Changqing Tian
Buildings 2023, 13(3), 666; https://doi.org/10.3390/buildings13030666 - 02 Mar 2023
Viewed by 1482
Abstract
The college building is a large energy consumer with a high density of energy consumption. However, less attention is paid to college buildings, particularly college dormitory buildings. Based on the one-year historical data collected from 20 college dormitory buildings located in Wuhan, China, [...] Read more.
The college building is a large energy consumer with a high density of energy consumption. However, less attention is paid to college buildings, particularly college dormitory buildings. Based on the one-year historical data collected from 20 college dormitory buildings located in Wuhan, China, this study aims to propose a three-stage strategy to identify and analyze the energy consumption patterns and characteristics of college dormitories in detail, including determining energy consumption patterns, analyzing key characteristics based on four indexes, and examining three influencing factors (occupants’ gender and floor and orientation location of rooms). The results show that the heavy energy users (around 10% of all occupants) consume around 20% of the total energy and have the narrowest comfort temperature range. However, the light energy users, 42% of total occupants, consume only approximately 27% of total energy. Their different tolerance to coldness is the main reason contributing to different energy consumption. The dormitories of males and location of the top floor and corner tend to consume significantly more energy in hot weather. This study would help campus facilities to understand the energy use behavior of occupants and formulate adequate policies so as to improve the energy management of campuses. Full article
(This article belongs to the Special Issue AI and Data Analytics for Energy-Efficient and Healthy Buildings)
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Review

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22 pages, 4918 KiB  
Review
A Scientometric Examination on Performance-Driven Optimization in Urban Block Design Research: State of the Art and Future Perspectives
by Yuya Xiong, Taiyu Liu, Yinghong Qin and Hong Chen
Buildings 2024, 14(2), 403; https://doi.org/10.3390/buildings14020403 - 01 Feb 2024
Viewed by 694
Abstract
The study of performance-driven optimization (PDO) in urban block design is essential in the context of architectural form and urban sustainability. PDO focuses on the integrated and comprehensive optimization of various quantifiable performances of buildings, such as solar energy usage, thermal comfort, and [...] Read more.
The study of performance-driven optimization (PDO) in urban block design is essential in the context of architectural form and urban sustainability. PDO focuses on the integrated and comprehensive optimization of various quantifiable performances of buildings, such as solar energy usage, thermal comfort, and energy efficiency. This method aligns urban spaces with sustainable development principles, ensuring they are not only aesthetically pleasing but also functionally efficient. This study explores the existing deficiency in the literature by conducting an in-depth scientometric analysis of PDO in urban block design. Employing science mapping coupled with bibliometric analysis using Python, this study meticulously analyzes the prevailing literature to map out the current intellectual landscape, understand trends, and identify key themes within this domain. This review identifies the key trends, methodologies, and influential works shaping the dynamic field of PDO. It emphasizes the critical roles of computational simulation, artificial intelligence integration, and big data analytics in refining urban block design strategies. This study highlights the growing importance of energy efficiency, environmental sustainability, and human-centric design elements. This review points to an increasing trend towards using sophisticated modeling techniques and data-driven analysis as essential tools in urban planning, crucial for developing sustainable, resilient, and adaptable urban spaces. Full article
(This article belongs to the Special Issue AI and Data Analytics for Energy-Efficient and Healthy Buildings)
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