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Advances in Building Performance Simulation and Building Energy Consumption Analysis
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Advances in Building Performance Simulation and Building Energy Consumption Analysis

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: 31 August 2024 | Viewed by 7297

Special Issue Editors

Dr. Yu Huang

E-Mail Website
Guest Editor
School of Civil Engineering, Guangzhou University, Guangzhou 510006, China
Interests: building performance simulation; energy-efficient building design; indoor environment quality; occupant behavior
Dr. Siwei Lou

E-Mail Website
Guest Editor
School of Civil Engineering, Guangzhou University, Guangzhou 510006, China
Interests: building performance simulation; indoor visual and thermal environment; building occupant behavior; solar radiation and daylighting
Dr. Yukai Zou

E-Mail Website
Guest Editor
School of Architecture and Urban Planning, Guangzhou University, Guangzhou 510006, China
Interests: architectural design; building simulation; energy-efficient building; data-driven method; building retrofit

Special Issue Information

Dear Colleagues,

As we stride deeper into an era of sustainability and energy efficiency, the focus on building performance simulation and energy consumption analysis becomes increasingly critical. This sphere of study, rich in complexity and opportunities, is transforming the way we design, construct, and manage built environments. Leveraging advanced simulation techniques and energy consumption analysis, we're capable of optimizing the energy profile of buildings, a critical task as we grapple with the dual challenges of escalating climate change and looming energy security concerns. The primary objective of this Special Issue, "Advances in Building Performance Simulation and Building Energy Consumption Analysis", is to highlight the most recent, cutting-edge innovations and emerging trends within these pivotal disciplines. It's a platform to explore advances in technology, theory, and application that underpin the evolving landscape of building performance and energy management. We invite contributions that delve into a wide spectrum of topics, including but not limited to:

1) Novel methodologies for building performance simulation,

2) Technological advancements in energy consumption analysis,

3) Energy-efficient design and retrofitting strategies,

4) Insights from data-driven and AI-based approaches,

5) Impact of climate change on building performance and energy consumption,

6) Case studies illustrating successful applications.

Dr. Yu Huang
Dr. Siwei Lou
Dr. Yukai Zou
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Buildings is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • building performance simulation
  • energy consumption analysis
  • energy-efficient design
  • retrofitting strategies
  • data-driven building design approaches
  • artificial intelligence in building analysis
  • climate change impact
  • energy security
  • building management

Published Papers (10 papers)

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Research

26 pages, 24582 KiB  
Article
Exploring the Impact of Urban Morphology on Building Energy Consumption and Outdoor Comfort: A Comparative Study in Hot-Humid Climates
by Shuyan Zhu, Chenlong Ma, Zhongping Wu, Yuqing Huang and Xiao Liu
Buildings 2024, 14(5), 1381; https://doi.org/10.3390/buildings14051381 (registering DOI) - 11 May 2024
Viewed by 110
Abstract
Research simultaneously examining building energy consumption and outdoor thermal comfort within urban environments remains limited. Few studies have delved into the sensitivity of design parameters based on building energy consumption and outdoor thermal comfort. The purpose of this study is to investigate the [...] Read more.
Research simultaneously examining building energy consumption and outdoor thermal comfort within urban environments remains limited. Few studies have delved into the sensitivity of design parameters based on building energy consumption and outdoor thermal comfort. The purpose of this study is to investigate the correlations between urban morphological design parameters and performance indicators, focusing on building energy consumption and outdoor thermal comfort (UTCI), across different urban block layouts in hot-humid regions, like Guangzhou. By establishing six fundamental morphological models—three individual unit layouts and three group layouts—the research explores both control and descriptive parameters through extensive simulation studies. Scatter plot visualizations provide insights into the impacts of various design parameters on energy consumption and UTCI, facilitating a comprehensive analysis of trends and quantitative relationships. Additionally, the study conducts sensitivity analyses on design parameters under different layout conditions to highlight their influences on target performance indicators. The findings reveal common trends, such as the significant impacts of plan dimensions and the Floor Area Ratio (FAR) on energy efficiency and outdoor comfort, as well as differential patterns, such as the varying sensitivities of the Shape Factor (S/V) and the Sky View Factor (SVF), across individual and collective layouts. Ultimately, this study offers a nuanced understanding of urban block morphology’s role in creating sustainable, comfortable, and energy-efficient urban environments, providing valuable guidelines for urban form design in hot-humid climates. Full article
(This article belongs to the Special Issue Advances in Building Performance Simulation and Building Energy Consumption Analysis)
20 pages, 3469 KiB  
Article
Assessment of Passive Solar Heating Systems’ Energy-Saving Potential across Varied Climatic Conditions: The Development of the Passive Solar Heating Indicator (PSHI)
by Wensheng Mo, Gaochuan Zhang, Xingbo Yao, Qianyu Li and Bart Julien DeBacker
Buildings 2024, 14(5), 1364; https://doi.org/10.3390/buildings14051364 (registering DOI) - 10 May 2024
Viewed by 174
Abstract
This study aims to evaluate the energy-saving potential of passive solar heating systems in diverse global climates and introduce a new indicator, the passive solar heating indicator (PSHI), to enhance the efficiency of building designs. By collecting climate data from 600 cities worldwide [...] Read more.
This study aims to evaluate the energy-saving potential of passive solar heating systems in diverse global climates and introduce a new indicator, the passive solar heating indicator (PSHI), to enhance the efficiency of building designs. By collecting climate data from 600 cities worldwide through a simulation model, the present study employs polynomial regression to analyze the impact of outdoor temperature and solar radiation intensity on building energy savings. It also uses K-means cluster analysis to scientifically categorize cities based on their energy-saving potential. The findings underscore the benefits of both direct and indirect solar heating strategies in different climates. Significantly, the PSHI shows superior predictive accuracy and applicability over traditional indices, such as the irradiation temperature difference ratio (ITR) and the irradiation degree hour ratio (C-IDHR), especially when outdoor temperatures are close to indoor design temperatures. Moreover, the application of a cluster analysis provides hierarchical guidance on passive heating designs globally, paving the way for more accurate and customized energy-efficient building strategies. Full article
(This article belongs to the Special Issue Advances in Building Performance Simulation and Building Energy Consumption Analysis)
16 pages, 6757 KiB  
Article
Optimizing the Return Vent Height for Improved Performance in Stratified Air Distribution Systems
by Danping Qiao, Shihai Wu, Nan Zhang and Chao Qin
Buildings 2024, 14(4), 1008; https://doi.org/10.3390/buildings14041008 - 5 Apr 2024
Viewed by 410
Abstract
One of the factors that strongly impacts the efficacy of stratified air distribution (STRAD) systems is the return vent height (H), for which different studies have yielded different suggested values. This theoretical research uses a displacement ventilation (DV) system as an [...] Read more.
One of the factors that strongly impacts the efficacy of stratified air distribution (STRAD) systems is the return vent height (H), for which different studies have yielded different suggested values. This theoretical research uses a displacement ventilation (DV) system as an example to examine how the H affects the efficacy of STRAD systems through analysis of the trade-offs between the cost of the vertical temperature gradient and the benefits of energy reduction. The key results are as follows: (a) The energy savings due to a lower H are smaller than the cost of the vertical temperature gradient for all STRAD systems. (b) With a supply temperature (Ts) set at 18 °C, elevated return vent positions can result in excessively cooled areas, while extremely low vent positions create a temperature gradient exceeding 3 °C between the head and ankles. (c) The TOPSIS methodology reveals that the optimal H value lies in the range of 1.5–2.3 m when Ts is 18 °C. (d) When adjusting the Ts value to achieve thermal neutrality, 2.3 m is identified as the optimal H value, demonstrating superior performance over the 1.5 m to 2.3 m range at 18 °C Ts. These findings highlight the benefit of a higher H for STRAD systems and the significance of configuring ventilation systems for thermal neutrality. Full article
(This article belongs to the Special Issue Advances in Building Performance Simulation and Building Energy Consumption Analysis)
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23 pages, 8954 KiB  
Article
Evaluation of Design Parameters for Daylighting Performance in Secondary School Classrooms Based on Field Measurements and Physical Simulations: A Case Study of Secondary School Classrooms in Guangzhou
by Jianhe Luo, Gaoliang Yan, Lihua Zhao, Xue Zhong and Xinyu Su
Buildings 2024, 14(3), 637; https://doi.org/10.3390/buildings14030637 - 28 Feb 2024
Viewed by 853
Abstract
The quality of natural lighting within secondary school classrooms can significantly affect the physical and mental well-being of both teachers and students. While numerous studies have explored various aspects of daylighting performance and its related factors, there is no universal standard for predicting [...] Read more.
The quality of natural lighting within secondary school classrooms can significantly affect the physical and mental well-being of both teachers and students. While numerous studies have explored various aspects of daylighting performance and its related factors, there is no universal standard for predicting and optimizing daylighting performance from a design perspective. In this study, a method was developed that combines measurements and simulations to enhance the design parameters associated with daylighting performance. This approach facilitates the determination of precise ranges for multiple design parameters and allows for the efficient attainment of optimal daylighting performance. Daylight glare probability (DGP), point-in-time illuminance (PIT), daylight factor (DF), and lighting energy consumption were simulated based on existing control parameters of operational classrooms. The simulation results were then validated using field measurements. Genetic algorithms (GAs) were employed to optimize the control parameters, yielding a set of optimal solutions for improving daylight performance. The differences between daylighting performance indicators corresponding to the optimal solution set and those of the basic model were compared to test the performance of the optimized parameters. The proposed method is a robust process for optimizing daylight design parameters based on GAs, which not only enhances daylighting performance but also offers scientifically grounded guidelines for the design phase. It is a valuable framework for creating healthier and more productive educational environments within secondary school classrooms. Full article
(This article belongs to the Special Issue Advances in Building Performance Simulation and Building Energy Consumption Analysis)
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15 pages, 2378 KiB  
Article
Research on Energy Consumption Prediction Models for High-Rise Hotels in Guangzhou, Based on Different Machine Learning Algorithms
by Jin Zhang, Chuyan Yuan, Junyi Yang and Lihua Zhao
Buildings 2024, 14(2), 356; https://doi.org/10.3390/buildings14020356 - 28 Jan 2024
Viewed by 813
Abstract
With the advancement of information technology, energy consumption prediction models are widely used for various types of buildings (office, residential, and commercial buildings) as guidance during the design and management stages. This article will establish an efficient building energy consumption prediction model for [...] Read more.
With the advancement of information technology, energy consumption prediction models are widely used for various types of buildings (office, residential, and commercial buildings) as guidance during the design and management stages. This article will establish an efficient building energy consumption prediction model for hotel buildings. To achieve this, we collected 78 architectural drawings of high-rise hotel buildings to establish 6 kinds of typical energy consumption models in 2 standard floor layouts and 3 public area levels. Then, on this basis, we used the total energy consumption calculated by EnergyPlus as an indicator to conduct sensitivity analysis on geometric feature parameters, internal heat source parameters, and thermal parameters, respectively. Finally, we generated a building database with 5000 samples through the R programming language to calculate and verify the energy consumption. As a result, it was proved that the energy consumption of hotel buildings can be predicted accurately, and that quadratic polynomial regression, with the best accuracy and stability, is the most suitable optimization model for hotel energy consumption prediction in Guangzhou. These conclusions provide a good theoretical basis for the analysis, prediction, and optimization of energy consumption in high-rise hotel buildings in the future. Full article
(This article belongs to the Special Issue Advances in Building Performance Simulation and Building Energy Consumption Analysis)
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38 pages, 10164 KiB  
Article
Data Center Energy Evaluation Tool Development and Analysis of Power Usage Effectiveness with Different Economizer Types in Various Climate Zones
by Ji Hye Kim, Dae Uk Shin and Heegang Kim
Buildings 2024, 14(1), 299; https://doi.org/10.3390/buildings14010299 - 22 Jan 2024
Viewed by 899
Abstract
Data centers are energy-intensive facilities, with over 95% of their total cooling load attributed to the heat generated by information technology equipment (ITE). Various energy-saving techniques have been employed to enhance data center efficiency and to reduce power usage effectiveness (PUE). Among these, [...] Read more.
Data centers are energy-intensive facilities, with over 95% of their total cooling load attributed to the heat generated by information technology equipment (ITE). Various energy-saving techniques have been employed to enhance data center efficiency and to reduce power usage effectiveness (PUE). Among these, economizers using outdoor air for cooling are the most effective for addressing year-round cooling demands. Despite the simplicity of the load composition, analyzing data center cooling systems involves dynamic considerations, such as weather conditions, system conditions, and economizer control. A PUE interpretation tool was specifically developed for use in data centers, aimed at addressing the simplicity of data center loads and the complexity of system analysis. The tool was verified through a comparison with results from DesignBuilder implementing the EnergyPlus algorithm. Using the developed tool, a comparative analysis of economizer strategies based on the PUE distribution was conducted, with the aim of reducing the PUE of data centers across various climatic zones. The inclusion of evaporative cooling (EC) further improved cooling efficiency, leading to reductions in PUE by approximately 0.02 to 0.05 in dry zones. Additionally, wet zones exhibited PUE reductions, ranging from approximately 0.03 to 0.07, with the implementation of indirect air-side economizer (IASE). Sensitivity and uncertainty analysis were further conducted. The computer room air handler (CRAH) supply temperature and CRAH temperature difference were the most influential factors affecting the annual PUE. For the direct air-side economizer (DASE) and DASE + EC systems, higher PUE uncertainty was observed in zones 1B, 3B, 4B, and 5B, showing ranges of 1.17–1.39 and 1.15–1.17, respectively. In the case of the IASE and IASE + EC systems, higher PUE uncertainty was noted in zones 0A, 0B, 1A, 1B, and 2A, with ranges of 1.22–1.43 and 1.17–1.43, respectively. The distinctive innovation of the tool developed in this study is characterized by its integration of specific features unique to data centers. It streamlines the computation of cooling loads, thus minimizing the burden of input, and delivers energy consumption data for data center cooling systems with a level of precision comparable to that of commercial dynamic energy analysis tools. It provides data center engineers with a valuable resource to identify optimal alternatives and system design conditions for data centers. This empowers them to make informed decisions based on energy efficiency enhancements, thereby strengthening their ability to improve energy efficiency. Full article
(This article belongs to the Special Issue Advances in Building Performance Simulation and Building Energy Consumption Analysis)
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23 pages, 9370 KiB  
Article
Urban Building Energy Modeling with Parameterized Geometry and Detailed Thermal Zones for Complex Building Types
by Hongyan Xi, Qilin Zhang, Zhiyi Ren, Guangchen Li and Yixing Chen
Buildings 2023, 13(11), 2675; https://doi.org/10.3390/buildings13112675 - 24 Oct 2023
Viewed by 944
Abstract
Urban building energy modeling (UBEM) has attracted wide attention to the requirement for global carbon emission reduction. This paper presents a UBEM tool, AutoBPS-Param, to generate building energy models (BEMs) with parameterized geometry and detailed thermal zones, especially for complex building types, considering [...] Read more.
Urban building energy modeling (UBEM) has attracted wide attention to the requirement for global carbon emission reduction. This paper presents a UBEM tool, AutoBPS-Param, to generate building energy models (BEMs) with parameterized geometry and detailed thermal zones, especially for complex building types, considering the shading effect from surrounding buildings simultaneously. Three building number scales and four scenarios were analyzed in the hotel-related buildings in Changsha, China. For the prototype modeling of Scenario 1, eighteen prototype building energy models for six building types in three vintages were created, and their simulation results were aggregated based on their representative floor areas. For AutoBPS-Param of Scenario 4, the method created one EnergyPlus (Version: 9.3.0) model for each building. The geometry of the prototype model was scaled and modified based on the target building’s length, width, and number of stories. The surrounding buildings were also added to the AutoBPS-Param simulation to better capture the urban dynamic impact. The results showed that the annual electricity and natural gas energy use intensity (EUI) of the pre-2005 HotelOffice prototype model was 172.25 and 140.45 kWh/m2. In contrast, with the AutoBPS-Param method, the annual electricity EUIs of 71 HotelOffice buildings constructed before 2005 ranged from 159.51 to 213.58 kWh/m2 with an average of 173.14 kWh/m2, and the annual gas EUIs ranged from 68.02 to 229.12 kWh/m2 with an average of 108.89 kWh/m2. The proposed method can better capture the diversity of urban building energy consumption. Full article
(This article belongs to the Special Issue Advances in Building Performance Simulation and Building Energy Consumption Analysis)
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26 pages, 25415 KiB  
Article
Evaluation of an Existing Validated Emirati House versus a New Parametric Design Based on the Local Social Environment through the Application of Advanced Tools
by Lindita Bande, Yosan Asmelash, Anwar Ahmad, Aybin Cyiza and Jose Berengueres
Buildings 2023, 13(10), 2627; https://doi.org/10.3390/buildings13102627 - 18 Oct 2023
Viewed by 1023
Abstract
Al Ain is the second-largest city in the Abu Dhabi Emirate, and the population of Al Ain has been growing rapidly for the last 50 years. The residential units in Al Ain are arranged using different concepts in relation to household social and [...] Read more.
Al Ain is the second-largest city in the Abu Dhabi Emirate, and the population of Al Ain has been growing rapidly for the last 50 years. The residential units in Al Ain are arranged using different concepts in relation to household social and economic behaviors. While Al Ain city has mostly low-rise and mid-rise residential buildings, the local population tends to live in traditional low-rise villas. The governmental statistics show a high ratio of energy consumption in the form of electricity for cooling loads, and it is estimated to increase with the rapid growth of the population. In this context, it is important to investigate different strategies to control the energy consumption of residential buildings. The purpose of this study was to assess the energy usage and demand of an existing villa in Al Ain and see how a newer design approach can help to reduce the annual energy consumption of households. The newer design option is based on a parametric (application of a parametric façade) approach whilst taking sustainable design approaches. The newer design options are compared to the existing villa and a traditional extension villa attached to the existing villa in terms of annual electricity consumption. The process of design and energy modeling of all cases used the Estidama baseline standards for technical and construction specifications. The process started with selecting an existing six-bedroom villa in Al Ain. Moreover, the selected villa had a planned extension to be constructed in the future. Then, an annual energy model of the existing villa was created in Rhinoceros 7.0 with the Grasshopper 3D plug-in. The energy results were validated against the real energy bills of the villa. Once the energy model was validated, the newer options of the design were modeled, and the projected energy consumption was compared with the base case results to see how energy-efficient the newer model would be. The research shows that it is possible to save up to 60% of electricity annually by carefully selecting a sustainable design in the early stages. Full article
(This article belongs to the Special Issue Advances in Building Performance Simulation and Building Energy Consumption Analysis)
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17 pages, 4544 KiB  
Article
Building Performance under Untypical Weather Conditions: A 40-Year Study of Hong Kong
by Siwei Lou, Zhengjie Peng, Jilong Cai, Yukai Zou and Yu Huang
Buildings 2023, 13(10), 2587; https://doi.org/10.3390/buildings13102587 - 13 Oct 2023
Cited by 1 | Viewed by 657
Abstract
As a common engineering practice, the buildings are usually evaluated under the Typical Meteorological Year (TMY), which represents the common weather situation. The warm and cool conditions, however, can affect the building performance considerably, yet building performances under such conditions cannot fully be [...] Read more.
As a common engineering practice, the buildings are usually evaluated under the Typical Meteorological Year (TMY), which represents the common weather situation. The warm and cool conditions, however, can affect the building performance considerably, yet building performances under such conditions cannot fully be given by the conventional TMY. This paper gives approaches to constructing the weather data that represents several warm and cool conditions and compares their differences by studying the cumulative cooling demands of a typical building in a hot and humid climate. Apart from the Extreme Weather Year (EWY), the Near-Extreme Weather Year (NEWY) and Common warm/cool Years (CY) data are proposed according to the occurrence distributions of the weather over the long term. It was found that the cooling demands of NEWY and EWY differ by 4.8% from the cooling needs of TMY. The difference between the cooling demands of NEWY and CY for most calendar months can be 20% and 15%, respectively. For the hot months, the cooling demands under NEWY and CY take 7.4–11.6% and 2.3–5.6% differences from those under TMY. The uncertainties of building performance due to the ever-changing weather conditions can be essential to the robustness of building performance evaluations. Full article
(This article belongs to the Special Issue Advances in Building Performance Simulation and Building Energy Consumption Analysis)
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20 pages, 5464 KiB  
Article
AppSimV: A Cyber–Physical Simulation and Verification Platform for Software Applications of Intelligent Buildings
by Haining Jia, Qiliang Yang, Ziyan Jiang, Wenjie Chen and Qizhen Zhou
Buildings 2023, 13(10), 2404; https://doi.org/10.3390/buildings13102404 - 22 Sep 2023
Cited by 1 | Viewed by 644
Abstract
Testing and verifying applications (Apps) are essential for a software-driven intelligent building system. Traditional methods connect App programs to hardware devices for debugging and testing on the engineering site. However, App bugs can hardly be found out before they are being deployed and [...] Read more.
Testing and verifying applications (Apps) are essential for a software-driven intelligent building system. Traditional methods connect App programs to hardware devices for debugging and testing on the engineering site. However, App bugs can hardly be found out before they are being deployed and thus always require an extended debugging cycle. To address this issue, we propose a cyber–physical simulation and verification platform named AppSimV, which enables the testing and verification of Apps in a mimic real scene. Taking swarm intelligence building as an example, this paper focuses on the cyber–physical architecture of AppSimV and its implementation mechanisms, including the standardized encapsulation of software components for the building physics model, a multitask scheduling simulation engine, a cyber–physical interaction interface, and the visual monitoring of the simulation process. The implementation mechanisms not only accurately simulate actual engineering scenarios but also facilitate the early detection and correction of issues that may arise during the App’s runtime, thus reducing the debugging time required for the App. With 1200 intelligent physical nodes connected in a swarm hardware system, AppSimV was validated by conducting the strict testing and verification of a set of Apps for an intelligent building. The results show that AppSimV is sound and reliable. Full article
(This article belongs to the Special Issue Advances in Building Performance Simulation and Building Energy Consumption Analysis)
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