Submit to Buildings Review for Buildings Propose a Special Issue

Journal Browser

Building Performance Simulation

Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Construction Management, and Computers & Digitization".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 20242

Share This Special Issue

Special Issue Editors

Prof. Dr. Yiqun Pan

E-Mail Website
Guest Editor

School of Mechanical Engineering, Tongji University, Shanghai 201804, China
Interests: building energy performance simulation

Dr. Mingya Zhu

E-Mail Website
Co-Guest Editor

School of Mechanical Engineering, Tongji University, Shanghai 200092, China
Interests: building energy prediction; data-driven modeling; building performance simulation

Dr. Yan Lyu

E-Mail
Co-Guest Editor

School of Mechanical Engineering, Tongji University, Shanghai 201804, China
Interests: HVAC engineering

Special Issue Information

Dear Colleagues,

Buildings consume more than one-third of the world's primary energy. Energy conservation and efficiency improvement in the building sector are crucial to achieve the global goals of sustainable energy and environment, through reducing building energy use and carbon emissions. Fuelled by rapid technology advancements in computer science and data measurement, building energy performance simulation (BEPS) has increasingly become a practical and supportive tool for energy-efficient designs, operations, and retrofitting at different scales, including HVAC systems, buildings, communities, and urban cities. Opportunities and challenges remain for researchers, tool developers, and practitioners, owing to the applications of multi-disciplinary approaches. Simultaneously, despite the decades-long history of development, this area also requires novel insights and innovations to inspire future perspectives for academic research and industry development.

This Special Issue is being organized to share the latest understanding, technologies, and methods for realizing improved building performance simulation, with the goal of creating energy-efficient, smart, low-carbon buildings by taking the maximized value of BEPS. The journal calls for papers addressing topics including, but not limited to, the following:

Simulation for urban planning and low-carbon community;
Simulation for high energy efficiency of building operations, retrofits, and energy performance diagnosis;
Simulation for design of low-carbon buildings;
Performance gap of simulation and real systems/buildings;
Algorithm and application study on model calibration;
Model fidelity, uncertainty and accuracy;
Energy-oriented modelling of occupant behavior, occupant-centric control, and human-building interaction;
Integrated modelling with data, with advanced algorithms or with simulation tools.

Prof. Dr. Yiqun Pan
Dr. Mingya Zhu
Dr. Yan Lyu
Guest Editors

Manuscript Submission Information

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

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

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

Keywords

building energy performance simulation
low carbon
energy efficiency design
model calibration
model uncertainty
performance gap
occupant behavior modelling
advanced simulation
integrated modelling

Published Papers (11 papers)

Download All Papers

Research

18 pages, 8323 KiB

Open AccessArticle

Verification of a Modeling Toolkit for the Design of Building Electrical Distribution Systems

by Anay Waghale, Shat Pratoomratana, Tianna-Kaye Woodstock, Karthikeya Devaprasad and Michael Poplawski

Buildings 2023, 13(10), 2520; https://0-doi-org.brum.beds.ac.uk/10.3390/buildings13102520 - 5 Oct 2023

Cited by 1 | Viewed by 1004

Abstract

DC electrical distribution systems offer many potential advantages over their AC counterparts. They can facilitate easier integration with distributed energy resources, improve system energy efficiency by eliminating AC/DC converters at end-use devices (e.g., laptop chargers), and reduce installation material, time, and cost. However, DC electrical distribution systems present additional design considerations, largely resulting from potentially greater magnitude and variation in cable losses. Modeling and simulation are rarely used to design such systems. However, the greater dependency of DC system energy efficiency on design choices such as distribution voltages, architecture, and integration of PV and BESS suggests that modeling and simulation may be required. Such system performance analysis is currently not a standard practice, in part due to limited availability and validation of capable software tools. This paper characterizes the accuracy of a Modelica-based Building Electrical Efficiency Analysis Model (BEEAM) toolkit, as a precursor for validating its use to perform system performance analysis and inform design decisions. The study builds upon previous verification research by characterizing complete systems comprised of commercially available equipment, and providing a more detailed analysis of simulation results. Five lighting systems with varying electrical distribution architectures were designed using market-available equipment, installed in a laboratory environment, modeled using BEEAM, and simulated using three Modelica integrated development environments (IDEs). Simulated and measured results were compared to characterize toolkit accuracy. Initial results revealed that simulated performance was mostly within ±5% of measured system-level and device-level performance. While simulation results were not found to be dependent on the IDE, some Modelica compiler interoperability issues were identified. Although the BEEAM toolkit showed promise for the targeted use case, further work is needed to determine whether the demonstrated 5% accuracy is sufficient for making real-world design decisions, and for BEEAM to advance from an interesting research tool to one that can impact real-world building projects. Full article

(This article belongs to the Special Issue Building Performance Simulation)

► Show Figures

Figure 1

22 pages, 8814 KiB

Open AccessArticle

Rapid Building Energy Modeling Using Prototype Model and Automatic Model Calibration for Retrofit Analysis with Uncertainty

by Yixing Chen, Wanlei Wei, Chengcheng Song, Zhiyi Ren and Zhang Deng

Buildings 2023, 13(6), 1427; https://0-doi-org.brum.beds.ac.uk/10.3390/buildings13061427 - 31 May 2023

Cited by 3 | Viewed by 1233

Abstract

Building performance simulation can be used for retrofit analysis. However, it is time-consuming to create building energy models for existing buildings. This paper presented and implemented a rapid building energy modeling method for existing buildings by using prototype models and automatic model calibration for retrofit analysis with uncertainty. A shopping mall building located in Changsha, China, was selected as a case study to demonstrate the rapid modeling method. First, a toolkit named AutoBPS-Param was developed to generate building energy models with parameterized geometry data. A baseline EnergyPlus model was generated based on the building’s basic information, including vintage, climate zone, total floor area, and percentage of each function type. Next, Monte Carlo sampling was applied to generate 1000 combinations for fourteen parameters. One thousand EnergyPlus models were created by modifying the baseline model with each parameter combination. Moreover, the 1000 simulation results were compared with the measured monthly electricity and natural gas usage data to find 29 calibrated solutions. Finally, the 29 calibrated energy models were used to evaluate the energy-saving potential of three energy conservation measures with uncertainty. The retrofit analysis results indicated that the electrical energy saving percentage of chiller replacement ranged from 1.57% to 13.51%, with an average of 8.27%. The energy-saving rate of lighting system replacement ranged from 1.92% to 11.66%, with an average of 6.43%. The energy-saving rate of window replacement ranges from 0.31% to 1.81%, with an average of 0.55%. The results showed that AutoBPS-Param could rapidly create building energy models for existing buildings and can be used for retrofit analysis after model calibration. Full article

(This article belongs to the Special Issue Building Performance Simulation)

► Show Figures

Figure 1

20 pages, 4651 KiB

Open AccessArticle

Reinforcement Learning with Dual Safety Policies for Energy Savings in Building Energy Systems

by Xingbin Lin, Deyu Yuan and Xifei Li

Buildings 2023, 13(3), 580; https://0-doi-org.brum.beds.ac.uk/10.3390/buildings13030580 - 21 Feb 2023

Cited by 1 | Viewed by 1140

Abstract

Reinforcement learning (RL) is being gradually applied in the control of heating, ventilation and air-conditioning (HVAC) systems to learn the optimal control sequences for energy savings. However, due to the “trial and error” issue, the output sequences of RL may cause potential operational safety issues when RL is applied in real systems. To solve those problems, an RL algorithm with dual safety policies for energy savings in HVAC systems is proposed. In the proposed dual safety policies, the implicit safety policy is a part of the RL model, which integrates safety into the optimization target of RL, by adding penalties in reward for actions that exceed the safety constraints. In explicit safety policy, an online safety classifier is built to filter the actions outputted by RL; thus, only those actions that are classified as safe and have the highest benefits will be finally selected. In this way, the safety of controlled HVAC systems running with proposed RL algorithms can be effectively satisfied while reducing the energy consumptions. To verify the proposed algorithm, we implemented the control algorithm in a real existing commercial building. After a certain period of self-studying, the energy consumption of HVAC had been reduced by more than 15.02% compared to the proportional–integral–derivative (PID) control. Meanwhile, compared to the independent application of the RL algorithm without safety policy, the proportion of indoor temperature not meeting the demand is reduced by 25.06%. Full article

(This article belongs to the Special Issue Building Performance Simulation)

► Show Figures

Figure 1

21 pages, 5820 KiB

Open AccessArticle

A Fast Method for Calculating the Impact of Occupancy on Commercial Building Energy Consumption

by Jiefan Gu, Peng Xu and Ying Ji

Buildings 2023, 13(2), 567; https://0-doi-org.brum.beds.ac.uk/10.3390/buildings13020567 - 19 Feb 2023

Cited by 2 | Viewed by 2048

Abstract

Occupancy, which refers to the occupant count in this paper, is one of the main factors affecting the energy consumption of commercial buildings. It is important for both building managers and energy simulation engineers to understand how an entire building’s energy consumption varies with different occupancy levels in the process of building automation systems or in assessments of building performance with benchmarking lines. Because commercial buildings usually have large scales, complex layouts and a large number of people, it is a challenge to simulate the relationships between an entire building’s energy consumption and occupancy. This study proposes a fast method for calculating the influence of occupancy on the energy consumption of commercial buildings with different building layouts and existing occupancies. Other occupant behaviors, such as the opening of windows and adjustment of shading devices, are comprehensively reflected in two basic building parameters: the balance point temperature and the total heat transmission coefficient of the building. This new method can be easily used to analyze how building energy varies with occupancy without a physical building’s energy model. An office building in Shanghai is taken as a case study to validate the proposed method. The results show that the coefficient of determination R² between the calculated value and actual value is 0.86, 0.8 and 0.71 for lighting, cooling and heating energy, respectively, which is suitable in engineering applications. Full article

(This article belongs to the Special Issue Building Performance Simulation)

► Show Figures

Figure 1

19 pages, 3787 KiB

Open AccessArticle

Bayesian Optimization Framework for HVAC System Control

by Xingbin Lin, Qi Guo, Deyu Yuan and Min Gao

Buildings 2023, 13(2), 314; https://0-doi-org.brum.beds.ac.uk/10.3390/buildings13020314 - 20 Jan 2023

Cited by 2 | Viewed by 1792

Abstract

The use of machine-learning algorithms in optimizing the energy efficiency of HVAC systems has been widely studied in recent years. Previous research has focused mainly on data-driven model predictive controls and reinforcement learning. Both approaches require a large amount of online interactive data; therefore, they are not efficient and stable enough for large-scale practical applications. In this paper, a Bayesian optimization framework for HVAC control has been proposed to achieve near-optimal control performance while also maintaining high efficiency and stability, which would allow it to be implemented in a large number of projects to obtain large-scale benefits. The proposed framework includes the following: (1) a method for modeling HVAC control problems as contexture Bayesian optimization problems and a technology for automatically constructing Bayesian optimization samples, which are based on time series raw trending data; (2) a Gaussian process regression surrogate model for the objective function of optimization; (3) a Bayesian optimization control loop, optimized for the characteristics of HVAC system controls, including an additional exploration trick based on noise estimation and a mechanism to ensure constraint satisfaction. The performance of the proposed framework was evaluated by using a simulation system, which was calibrated by using trending data from a real data center. The results of our study showed that the proposed approach achieved more than a 10% increase in energy-efficiency savings within a few weeks of optimization time compared with the original building automation control. Full article

(This article belongs to the Special Issue Building Performance Simulation)

► Show Figures

Figure 1

18 pages, 3176 KiB

Open AccessArticle

A New Explication of Minimum Variable Sets (MVS) for Building Energy Prediction Based on Building Performance Database

by Mingya Zhu, Yiqun Pan, Yan Lyu, Zhizhong Huang and Pengcheng Li

Buildings 2022, 12(11), 1907; https://0-doi-org.brum.beds.ac.uk/10.3390/buildings12111907 - 7 Nov 2022

Cited by 1 | Viewed by 1324

Abstract

Building energy simulation plays a significant role in buildings, with applications such as building performance evaluation, retrofit decisions and the optimization of building operations. However, the wide range of model inputs has limited much research into empirically customized case studies due to the insufficient availability of data inputs or the lack of systematic feature selection of key inputs. To address this gap, this study proposes the concept of minimum variable sets (MVSs) for building energy-prediction models to improve the general applicability of building energy prediction using forward simulation. An MVS, in this paper, refers to a variable set that contains the most indispensable energy-related variables/features for annual building energy prediction. This study developed MVSs for office buildings by applying feature engineering algorithms to a Building Performance Database (BPD), which was established by integrating the design of experiments (DoE) method with high-dimensional data-space metrics, as well as parallel simulation. Supervised feature dimension reduction methods and multiple statistical criteria were adopted to choose different numbers of indispensable variables from the primary 16 building variables. The hierarchical MVSs that consist of the selected variables are characterized by the most influential features for building energy prediction, with certain requirements for prediction accuracy. To further improve the feasibility of MVSs, this study utilized two separate office buildings located in Shanghai and California as validation cases and provided comparable prediction accuracies across different sizes of MVS. The results showed that the MVS that has 12 variables has higher prediction accuracy than that which has 9 variables, followed by that which has 7 variables. Finally, the quantitatively hierarchical correlations between different sizes of MVS with different prediction accuracies for annual building energy could provide potential support for reasonable decision-making regarding building energy model variables, especially when comprehensive consideration is needed of the limited cost and data availability, and the acceptable accuracy of building energy. Full article

(This article belongs to the Special Issue Building Performance Simulation)

► Show Figures

Figure 1

20 pages, 4473 KiB

Open AccessArticle

A Comprehensive Study on Integrating Clustering with Regression for Short-Term Forecasting of Building Energy Consumption: Case Study of a Green Building

by Zhikun Ding, Zhan Wang, Ting Hu and Huilong Wang

Buildings 2022, 12(10), 1701; https://0-doi-org.brum.beds.ac.uk/10.3390/buildings12101701 - 16 Oct 2022

Cited by 5 | Viewed by 1688

Abstract

Integrating clustering with regression has gained great popularity due to its excellent performance for building energy prediction tasks. However, there is a lack of studies on finding suitable regression models for integrating clustering and the combination of clustering and regression models that can achieve the best performance. Moreover, there is also a lack of studies on the optimal cluster number in the task of short-term forecasting of building energy consumption. In this paper, a comprehensive study is conducted on the integration of clustering and regression, which includes three types of clustering algorithms (K-means, K-medians, and Hierarchical clustering) and four types of representative regression models (Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Regression (SVR), Artificial Neural Network (ANN), and extreme gradient boosting (XGBoost)). A novel performance evaluation index (PI) dedicated to comparing the performance of two prediction models is proposed, which can comprehensively consider different performance indexes. A larger PI means a larger performance improvement. The results indicate that by integrating clustering, the largest PI for SVR, LASSO, XGBoost, and ANN is 2.41, 1.97, 1.57, and 1.12, respectively. On the other hand, the performance of regression models integrated with clustering algorithms from high to low is XGBoost, SVR, ANN, and LASSO. The results also show that the optimal cluster number determined by clustering evaluation metrics may not be the optimal number for the ensemble model (integration of clustering and regression model). Full article

(This article belongs to the Special Issue Building Performance Simulation)

► Show Figures

Figure 1

26 pages, 12941 KiB

Open AccessArticle

Development and Test of a New Fast Estimate Tool for Cooling and Heating Load Prediction of District Energy Systems at Planning Stage

by Yan Lyu, Yiqun Pan and Zhizhong Huang

Buildings 2022, 12(10), 1671; https://0-doi-org.brum.beds.ac.uk/10.3390/buildings12101671 - 12 Oct 2022

Cited by 3 | Viewed by 1202

Abstract

During the design and planning stage of a district energy system, the prediction of the cooling and heating loads is an important step. The accurate estimate of the load pattern can provide a basis for the configuration and optimization of the system. To meet the demand in practical application, this paper proposes a fast load prediction method for district energy systems based on a presimulated forward modelling database and KNN (K-nearest neighbor) algorithm and develops it into a practical tool. Owing to the absence of some design parameters at the planning stage, scenario analysis is also used to determine some input conditions for load prediction. In this paper, the scenarios cover three types of building: office, shopping mall and hotel. To test the performance of this new method, we randomly selected 15 virtual buildings (5 buildings for each type) with different design parameters and took their detailed BPS (building performance simulation) model as a benchmark to assess the prediction results of the new method. The index “ratio of the hours with effective prediction” is defined as the ratio of the hours whose relative error of hourly load prediction is less than 15% to the hours whose load is not 0 in the whole year, and the test result shows that this index is not less than 0.9 (90%) for the predicted cooling load of all 45 test cases and the predicted heating load of 25 of the 45 cases. As a research achievement with practical value, this paper accomplishes the programming work of the tool and makes it into a software. The application of this software in the actual project of district energy system is also presented. The performance of the new load prediction tool was compared with the traditional approach commonly used in engineering—the load estimation based on reference building models—and the result shows that the fast load estimate tool can provide the same level of prediction accuracy as traditional simulation methods. Full article

(This article belongs to the Special Issue Building Performance Simulation)

► Show Figures

Figure 1

26 pages, 130028 KiB

Open AccessArticle

Parametric Design and Spatial Optimization of East–West-Oriented Teaching Spaces in Shanghai

by Hongzhi Mo, Yuxin Zhou and Yiming Song

Buildings 2022, 12(9), 1333; https://0-doi-org.brum.beds.ac.uk/10.3390/buildings12091333 - 30 Aug 2022

Cited by 1 | Viewed by 1722

Abstract

The goal of the current study was to determine the appropriate spatial shapes for classroom occupants while saving energy. The research used parametric design and Genetic Algorithm (GA) to achieve this. Four recognized performance indicators, Energy Use Intensity (EUI), Useful Daylight Illuminance (UDI), Daylight Factor (DF), and Daylight Autonomy (DA), were used as the evaluation indexes for the research. The tests took place in six east–west-oriented classrooms at Shanghai University, China. The methodology was based on four steps: (1) parametric 3D modeling by Rhino and Grasshopper; (2) using building performance simulation tools; (3) running algorithm optimization; (4) outputting the useful results. The results proved that the methodology worked successfully in reducing energy consumption: optimized classrooms could be reduced by 7.5~14.5%, and classrooms with east directions were generally 4.8~8.3% more efficient than west-facing ones. The indoor lighting environment was also significantly improved, being slightly better than north–south-oriented classrooms in terms of the UDI index (60~75%) and inferior (but still high) in terms of the DF (4.0~7.0%) and DA (60~80%) indexes. The conclusion can help save design time in the early design process of teaching spaces. Full article

(This article belongs to the Special Issue Building Performance Simulation)

► Show Figures

Figure 1

24 pages, 10854 KiB

Open AccessArticle

An Air Conditioning Design Strategy of the Stepped Hall Based on Building Performance Simulation

by Ruijun Chen, Yu-Tung Liu and Yaw-Shyan Tsay

Buildings 2022, 12(8), 1269; https://0-doi-org.brum.beds.ac.uk/10.3390/buildings12081269 - 19 Aug 2022

Cited by 2 | Viewed by 2854

Abstract

This study proposed an improved air conditioning design strategy based on building performance simulation for a stepped hall. The air velocity and air change rate of the case were measured on-site, which were compared with the simulation data to verify the reliability of the building simulation model. Then, the fully mixing ventilation scheme and the design schemes proposed in this study were simulated. Finally, the building simulation results were summarized to confirm the applicability of the air conditioning design strategy. The building performance results showed that the air distribution performance index (ADPI) value was 76.95% in the original case. Nevertheless, the effective draft temperature (EDT) in the middle seat area exceeded the standard value, indicating that a local cold shock would occur. Moreover, its scale for ventilation efficiency (SVE6) in the residential area was 2.54. However, the SVE6s in the other schemes were between 0.89 and 0.92. It means that the proposed schemes only needed to take one-third of the time to exhaust air. These three indicators’ visualization results can analyze the advantages and disadvantages of each scheme. Therefore, the improved building performance simulation strategy could inspect the design effect and give suggestions quickly for air conditioning design. Full article

(This article belongs to the Special Issue Building Performance Simulation)

► Show Figures

Figure 1

21 pages, 9506 KiB

Open AccessArticle

Evaluation of Expanded Metal Mesh Applied on Building Facades with Regard to Daylight and Energy Consumption: A Case Study of an Office Building in Taiwan

by Yaw-Shyan Tsay, Chih-Hung Yang and Chiu-Yu Yeh

Buildings 2022, 12(8), 1187; https://0-doi-org.brum.beds.ac.uk/10.3390/buildings12081187 - 8 Aug 2022

Cited by 5 | Viewed by 2610

Abstract

Recently, expanded metal mesh has been used on the facades of many buildings in Taiwan. Therefore, in this study, we evaluated the impact of expanded metal mesh on natural lighting and energy consumption in office buildings. First, the compatibility of EnergyPlus and DIVA simulation software with expanded metal mesh was verified using field measurements. The results show a high correlation between simulation and measurement, except for some periods of direct sunlight. Then, we evaluated the effects of window-to-wall ratio (WWR), glass, and expanded metal mesh on energy consumption and lighting. The results show that WWR has a significant influence on both lighting and energy consumption. The greater the WWR, the greater the energy saving potential of the expanded metal mesh and glass. If the SHGC of the glass is lower, the potential of the expanded metal mesh to save air conditioning energy consumption is smaller, and, as a result, the expanded metal mesh may increase the total energy consumption. Of the 36 simulation cases performed, three cases met the LEED lighting standard. The case with minimum energy consumption is achieved when SHGC = 50%, using laminated clear glass and expanded metal mesh with a 21% perforated ratio. Full article

(This article belongs to the Special Issue Building Performance Simulation)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Building Performance Simulation

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (11 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI