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Study on Building Energy Efficiency Related to Simulation Models

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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 (20 November 2024) | Viewed by 18128

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Special Issue Editors

Dr. Pengyuan Shen

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Guest Editor

Department of Architecture, Harbin Institute of Technology, Shenzhen 518055, China
Interests: building simulation; district energy system; thermal comfort; data mining; climate change impacts; urban microclimate

Dr. Yun Kyu Yi

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Guest Editor

School of Architecture, University of Illinois Urbana-Champaign, Champaign, IL 61820, USA
Interests: computational building modeling and simulation; building performance evaluation; indoor occupant’s behavior

Dr. Huilong Wang

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Guest Editor

Department of Construction Management and Real Estate, Shenzhen University, Shenzhen 518060, China
Interests: building simulation; building optimal control; demand response; HVAC; model predictive control
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Special Issue Information

Dear Colleagues,

Since the building sector accounts for a significant share of energy-related carbon emissions globally, research on improvements in building energy efficiency has received increasing interest in the past decades. Buildings, as a complex system per se, and the problems associated with their energy use have properties of nonlinearity, multicollinearity, and stochasticity, which entail cross-disciplinary knowledge and effort. The question of how to improve building energy efficiency without compromising the physical comfort for occupancy has yet to be fully addressed. Simulation techniques emerge as powerful methods that enable the exploration and resolution of these complicated problems related with energy efficiency in buildings, in contrast with traditional methods. Today, various simulation models—whether they are white box, black box, or grey box models—have been developed to understand the behavior and to promote the energy performance of building forms, building envelopes, HVAC systems, lighting systems, renewable energy systems, demand management, occupancy behavior, etc. Rapid advancements in simulation techniques shed light on methods for seeking buildings with better performance, higher energy efficiency, and buildings that induce less environmental impacts.

The goal of this Special Issue is to call for contributions that research opportunities in building energy saving and efficiency improvement by adopting various simulation techniques, including either analytical, empirical, or numerical models. I cordially invite authors to submit papers for consideration and publication in the Special Issue, “Study on Building Energy Efficiency Related to Simulation Models”. Topics may include but are not limited to the following:

Energy-efficient building design based on performance simulation;
High-efficiency building energy system and its modeling;
Modeling and simulation of district energy systems;
Onsite building renewable-energy system modeling and simulation;
Simulation-based optimization problems related to building energy efficiency;
Model predictive control for building system;
Demand response in building and its modeling;
Modeling and simulation of building occupancy behavior;
Modeling of energy-efficient building envelope;
Building retrofit assessments and optimizations based on simulations;
Novel simulation method or tool for building energy performance evaluation;
Implementation of simulation techniques for energy saving with respect to historical or vernacular buildings;
Development of data driven proxy model and its comparison to simulation models;
Simplified simulation methods and tools for rapid building performance evaluation;
Development of urban-level building simulation technique;
Simulation-based study on urban- or street-level building energy use and heat island;
Use of computational fluid dynamics to achieve energy efficiency in buildings;
Integrating or coupling other domains with energy simulation or modeling;
Modeling and simulation on human behavior or individual responses;
Modeling or simulating of new materials and systems.

Dr. Pengyuan Shen
Dr. Yunkyu Yi
Dr. Huilong Wang
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 simulation
energy efficiency
renewable energy system
demand response
occupancy behavior
performance-driven design
district energy system
performance optimization
HVAC system
computational fluid dynamics

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Published Papers (8 papers)

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Research

26 pages, 16877 KiB

Open AccessArticle

Influence of Balcony Thermal Bridges on Energy Efficiency of Dwellings in a Warm Semi-Arid Dry Mediterranean Climate

by Carlos Pérez-Carramiñana, Aurelio de la Morena-Marqués, Ángel Benigno González-Avilés, Nuria Castilla and Antonio Galiano-Garrigós

Buildings 2024, 14(3), 703; https://doi.org/10.3390/buildings14030703 - 6 Mar 2024

Cited by 2 | Viewed by 962

Abstract

Thermal bridges significantly influence the energy performance of buildings. However, their impact varies depending on the type of thermal bridge, climate conditions, construction methodologies, and geometric characteristics of the building. On the Spanish Mediterranean coast, buildings with large balconies are predominant. Nevertheless, the Spanish energy efficiency regulations do not adequately specify the thermal bridges at the junctions of balconies with facades, leading to a lack of consideration for their influence in the majority of architectural projects. The objective of this study is to qualitatively and quantitatively assess the impact of such thermal bridges on the energy efficiency of buildings in a dry Mediterranean climate (BShs) within a warm semi-arid climate (BSh). As a case study, the influence of this thermal bridge is analyzed in two residential buildings located on the Mediterranean coast of southeastern Spain. The study also examines the modification of various construction parameters of this thermal bridge and determines the optimal design parameters to reduce its thermal transmittance. The results demonstrate that the energy needs caused by thermal bridges account for approximately 40% of the total annual energy needs of the studied residential buildings. Balcony thermal bridges account for 25% to 40% of the energy needs caused by all thermal bridges. The lack of differentiation in Spanish standards between balcony–facade and facade–slab edge junctions causes an imprecision in calculations equivalent to 12% of the total annual energy needs of dwellings. The novelty of this research lies in highlighting that current regulations and calculation programs need improvement to better characterize balcony thermal bridges and enhance the accuracy of building energy efficiency calculations. Full article

(This article belongs to the Special Issue Study on Building Energy Efficiency Related to Simulation Models)

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33 pages, 12872 KiB

Open AccessArticle

The ANN Architecture Analysis: A Case Study on Daylight, Visual, and Outdoor Thermal Metrics of Residential Buildings in China

by Shanshan Wang, Yun Kyu Yi and Nianxiong Liu

Buildings 2023, 13(11), 2795; https://doi.org/10.3390/buildings13112795 - 7 Nov 2023

Cited by 27 | Viewed by 1558

Abstract

Selecting an appropriate ANN model is crucial for speeding up the process of building performance simulation during the design phase of residential building layouts, particularly when evaluating three or more green performance metrics simultaneously. In this study, daylight, visual, and outdoor thermal metrics were selected as main green performance. To find the suitable ANN model, sensitivity analysis was used to obtain a set of proper parameters applied to the ANN structure. To train the ANN model with a higher predicting accuracy, this paper tested four different scenarios of ANN parameter setups to find some general guidelines about how to set up an ANN model to predict DF, sunlight hours, QuVue and UTCI. The results showed that an ANN model with a combined output variable demonstrated better average prediction accuracy than ANN models with a separated output variable. Having two times the number of training samplings compared to the number of input variables can lead to a high accuracy of prediction. The ideal number of neurons in the hidden layer was approximately 1.5 times the number of input variables. These findings of how to improve the ANN model may provide guidance for modeling an ANN for building performance. Full article

(This article belongs to the Special Issue Study on Building Energy Efficiency Related to Simulation Models)

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21 pages, 3330 KiB

Open AccessArticle

Development, Calibration, and Validation of a Simulation Model for Indoor Temperature Prediction and HVAC System Fault Detection

by Darko Palaić, Ivan Štajduhar, Sandi Ljubic and Igor Wolf

Buildings 2023, 13(6), 1388; https://doi.org/10.3390/buildings13061388 - 26 May 2023

Cited by 8 | Viewed by 2068

Abstract

An effective approach to increasing energy efficiency in buildings without compromising thermal comfort is to optimize heating, ventilation, and air conditioning (HVAC) systems through the use of advanced building-management system features, such as fault detection and diagnosis. Such functions are usually developed based on simulation models that must be calibrated and validated to achieve an appropriate level of accuracy and reliability. The objective of this study was to develop and calibrate a room-level simulation model of a hotel building and its HVAC system using TRNSYS 18 software and real data collected from the smart room system installed in the building. The calibration process was performed with 100 rooms using 5-min samples of room temperatures in selected 1-month periods during the summer and winter seasons by minimizing the root mean squared error (RMSE) in the average of the observed rooms using a genetic algorithm. The calibrated model was able to predict room temperatures with an RMSE of 0.79 ± 0.14 °C and a coefficient of variation in the root mean squared error (cvRMSE) of 3.58 ± 0.7%, which is well below the limits prescribed by international guidelines. The model was then applied to detect faults in the operation of fan coil units in the rooms based on the residual analysis and defined if–then rules. The results obtained show that the model can track the trends of temperature changes in real conditions and successfully detect major anomalies in a system. Full article

(This article belongs to the Special Issue Study on Building Energy Efficiency Related to Simulation Models)

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21 pages, 9254 KiB

Open AccessArticle

Research on Multiple Energy-Saving Strategies for Existing Coach Stations: A Case of the Xi’an Area, China

by Xueping Li, Luo Qin and Jingjing Li

Buildings 2023, 13(5), 1215; https://doi.org/10.3390/buildings13051215 - 4 May 2023

Cited by 1 | Viewed by 1450

Abstract

In the context of China’s dual-carbon goals, energy efficiency in public buildings has become a focal point of public concern. As large-scale public transportation buildings, the indoor thermal comfort and the current state of energy consumption of coach stations are increasingly being emphasized. This research used existing coach stations in the Xi’an region as the object; through on-site investigations and field tests of indoor thermal environments in winter and summer seasons, it was found that the coach stations had energy waste and high energy consumption; the enclosure structures had poor thermal performance; and the stations lacked effective energy-saving measures. Energy-saving transformation strategies were proposed from two aspects: enclosure structures and renewable energy utilization. Using DeST-C for energy consumption, the external walls, roofs, insulation materials, and glass materials were simulated, and nine different combinations of energy-saving schemes were simulated using orthogonal experiments. The optimal scheme was selected based on the comprehensive energy-saving rate and economic analysis results, which included using 80 mm XPS external insulation for the external walls, low-e hollow glass for the windows (low transmittance type), and an 80 mm PUR board for the roof insulation. The energy-saving rate of this scheme was 26.84%. The use of rooftop solar photovoltaic power generation and fresh air heat recovery devices can effectively reduce building energy consumption, and the investment payback period is less than 5 years. The research applications have practical significance for improving the indoor environment of existing coach stations and saving energy consumption. Full article

(This article belongs to the Special Issue Study on Building Energy Efficiency Related to Simulation Models)

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21 pages, 1994 KiB

Open AccessArticle

An Electricity Consumption Disaggregation Method for HVAC Terminal Units in Sub-Metered Buildings Based on CART Algorithm

by Xinyu Yang, Ying Ji, Jiefan Gu and Menghan Niu

Buildings 2023, 13(4), 967; https://doi.org/10.3390/buildings13040967 - 6 Apr 2023

Cited by 2 | Viewed by 2016

Abstract

Obtaining reliable and detailed energy consumption information about building service (BS) systems is an essential prerequisite for identifying energy-saving potential and improving energy efficiency of a building. Therefore, in recent years, energy sub-metering systems have been widely implemented in public buildings in China. A majority of electrical systems and equipment can be directly metered. However, in actual sub-metering systems, the terminal units of heating, ventilation and air conditioning (HVAC) systems, such as fan coils, air handling units and so on, are often mixed with the lighting-plug circuit. This mismatch between theoretical sub-metering systems and actual electricity supply circuits constitutes a lot of challenges in BS system management and control optimization. This study proposed an indirect method to disaggregate the energy consumption of HVAC terminal units from mixed sub-metering data based on the CART algorithm. This method was demonstrated in two buildings in Shanghai. The case study results show that the weighted mean absolute percentage errors (WMAPE) are within 5% and 15% during working hours in the cooling and heating seasons, respectively. Full article

(This article belongs to the Special Issue Study on Building Energy Efficiency Related to Simulation Models)

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24 pages, 15168 KiB

Open AccessArticle

Effect of Block Morphology on Building Energy Consumption of Office Blocks: A Case of Wuhan, China

by Shen Xu, Gaomei Li, Hailong Zhang, Mengju Xie, Thushini Mendis and Hu Du

Buildings 2023, 13(3), 768; https://doi.org/10.3390/buildings13030768 - 15 Mar 2023

Cited by 8 | Viewed by 3061

Abstract

Block morphology refers to critical parameters influencing building energy performance on the block scale. However, analysis of the combined effect of block morphological parameters on building energy consumption with real blocks is lacking. In this paper, the aim is to evaluate the combined effect of office block morphology on building energy consumption in the context of the Hot-summer and Cold-winter zone in China. First, a workflow for the energy assessment of office buildings with the coupled block morphology on the block scale was proposed with evaluation tools. Seventy office blocks in Wuhan were taken as examples and then classified based on building layout typology and building height. Afterwards, the morphological parameters and building energy use intensity (EUI) for different blocks were calculated. Then, the combined effect of block morphology on the buildings’ energy consumption was evaluated and the model on predicting the building energy consumption of office blocks was proposed. Finally, based on the results, low-energy design strategies were projected for office blocks. The results illustrated that the effect of block morphology on building cooling, heating, and lighting is EUI 28.83%, 28.56%, and 23.23%, respectively. Building shape factor (BSF), floor area ratio (FAR), average building height of block (BH), and average building depth of block (BD) are effective block morphological parameters. The key morphological parameters which combined affect the building energy consumption of office blocks are BSF and FAR; BSF has 1.24 times the effect on building energy consumption than FAR. The workflow built in this paper can be applied to other cities around the world for promoting sustainable cities. Full article

(This article belongs to the Special Issue Study on Building Energy Efficiency Related to Simulation Models)

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24 pages, 3410 KiB

Open AccessArticle

Machine Learning Approach to Predict Building Thermal Load Considering Feature Variable Dimensions: An Office Building Case Study

by Yongbao Chen, Yunyang Ye, Jingnan Liu, Lixin Zhang, Weilin Li and Soheil Mohtaram

Buildings 2023, 13(2), 312; https://doi.org/10.3390/buildings13020312 - 20 Jan 2023

Cited by 13 | Viewed by 3202

Abstract

An accurate and fast building load prediction model is critically important for guiding building energy system design, optimizing operational parameters, and balancing a power grid between energy supply and demand. A physics-based simulation tool is traditionally used to provide the building load demand; however, it is constrained by its complex model development process and requirement for engineering judgments. Machine learning algorithms (i.e., data-driven models) based on big data can bridge this gap. In this study, we used the massive energy data generated by a physics-based tool (EnergyPlus) to develop three data-driven models (i.e., LightGBM, random forest (RF), and long-short term memory (LSTM)) and compared their prediction performances. The physics-based models were developed using office prototype building models as baselines, and ranges were provided for selected key input parameters. Three different input feature dimensions (i.e., six-, nine-, and fifteen-input feature selections) were investigated, aiming to meet different demands for practical applications. We found that LightGBM significantly outperforms the RF and LSTM algorithms, not only with respect to prediction accuracy but also in regard to computation cost. The best prediction results show that the coefficient of variation of the root mean squared error (CVRMSE), squared correction coefficient (R²), and computation time are 5.25%, 0.9959, and 7.0 s for LightGBM, respectively, evidently better than the values for the algorithms based on RF (18.54%, 0.9482, and 44.6 s) and LSTM (22.06%, 0.9267, and 758.8 s). The findings demonstrate that a data-driven model is able to avoid the process of establishing a complicated physics-based model for predicting a building’s thermal load, with similar accuracy to that of a physics-based simulation tool. Full article

(This article belongs to the Special Issue Study on Building Energy Efficiency Related to Simulation Models)

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19 pages, 4172 KiB

Open AccessArticle

Energy Saving and Thermal Comfort Performance of Passive Retrofitting Measures for Traditional Rammed Earth House in Lingnan, China

by Shihao Li, Meilin Wang, Pengyuan Shen, Xue Cui, Linqian Bu, Ruji Wei, Longzhu Zhang and Chengjia Wu

Buildings 2022, 12(10), 1716; https://doi.org/10.3390/buildings12101716 - 17 Oct 2022

Cited by 6 | Viewed by 2438

Abstract

The traditional rammed earth houses sharing similar patterns in the Lingnan region, south China, and distributed in rectangular arrays, are gradually losing their vitality and becoming uninhabited under modern living conditions. This research examined a typical pattern called the “Four-point gold” house and analyzed the suitability of different retrofitting technologies by field measurements and building simulation. To optimize energy consumption, indoor thermal comfort, and the corresponding economic performance of the retrofitting measures for the prototypical house, five measures, including wall insulation, reflective roof coating, carpet, sunshade, and natural ventilation, are proposed after considering the status quo of the building envelope. It is found that the best performance in energy-saving, dynamic investment payback period, and annual indoor thermal comfort are 2192.27 kWh/a, 9.17 years, and 1766 h, respectively. Different parameters are included to be clustered by K means clustering technique, and the comprehensively optimized scheme consists of a regime of 30 mm XPS 30 mm, ZS-221 white coating, carpet, 0.5 m sunshade width, and turning off windows (doors). The proposed retrofitting strategy can be promoted to a wide range of traditional rammed earth houses in the Lingnan region in China and holds a conspicuous energy-saving potential for the suburban and rural residential sectors in the region. Full article

(This article belongs to the Special Issue Study on Building Energy Efficiency Related to Simulation Models)

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