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Advanced Building Performance Analysis
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Advanced Building Performance 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: closed (20 October 2022) | Viewed by 15409

Special Issue Editor

Prof. Dr. Natasa Nord

E-Mail Website
Guest Editor
Department of Energy and Process Engineering, Faculty of Engineering, Norwegian University of Science and Technology(NTNU), NO-7491 Trondheim, Norway
Interests: building energy planning; energy efficiency in buildings; district heating; building energy supply

Special Issue Information

Dear Colleagues,

The Guest Editors are inviting submissions to a Special Issue of Buildings on the subject area of “Advanced Building Performance Analysis”. Intelligent and advanced use of Building Energy Measurement System (BEMS) or any other monitoring and sensing system in buildings for energy efficiency and improvement in thermal comfort has been highly recognized, but still not fully utilized due to different reasons. Digitalization of the building service systems as well as the development of artificial intelligence are giving new opportunities for improved energy efficiency and thermal comfort in buildings. Different methods are available to analyze and handle building data, like regression methods, probabilistic methods, neutral networks, and data mining techniques. They may be used for both prediction, and fault detection and diagnosis. The rapid development in machine learning and artificial intelligence has provided advanced data analytics to tackle prediction and data analysis problems in a more flexible, convenient, and efficient way.

This Special Issue will deal with implementation of the existing and new methods to analyze and handle building data for improved energy efficiency, better thermal comfort, operation optimization, and better control, prediction, fault detection and diagnosis, etc. Topics of interest for publication include, but are not limited to:

  • Digitalization of building service systems
  • Existing and new statistical methods to handle building data
  • Advanced building analyses for prediction of building energy performance
  • Advanced building data analyses for improved operation and control of buildings
  • New statistical methods for single building analyses and prediction
  • New statistical methods for building group analyses and prediction
  • Digitalization for better demand side management

Prof. Dr. Natasa Nord
Guest Editor

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 semimonthly 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

  • digitalization
  • artificial intelligence and data mining methods
  • building service systems
  • building performance prediction
  • building performance optimization
  • fault detection and diagnosis
  • energy planning

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

16 pages, 5731 KiB  
Article
An Integrated User Interface of Assessment and Optimization for Architectural Façade Shading Designs in Taiwan
by Yaw-Shyan Tsay, Min-Shiun Wu and Chuan-Hsuan Lin
Buildings 2022, 12(12), 2116; https://doi.org/10.3390/buildings12122116 - 2 Dec 2022
Cited by 3 | Viewed by 2183
Abstract
In response to sustainable development goals, the architectural industry aims to decrease the high proportion of emissions and energy use in the construction sector. Therefore, the design method of building performance optimization (BPO) has been advocated in recent studies as a method for [...] Read more.
In response to sustainable development goals, the architectural industry aims to decrease the high proportion of emissions and energy use in the construction sector. Therefore, the design method of building performance optimization (BPO) has been advocated in recent studies as a method for accomplishing high-performance building design. However, BPO remains difficult to implement in practice due to the lack of a definite process and supporting tools for architects/designers in the early design process. The purpose of this paper is to propose a BPO framework and integrated design decision support (DDS) interface to provide a visual and science-based analysis and assist designers working with high-performance building façade designs. The framework and DDS tool are then tested by designers through a practice design of the headquarters façade. All the designers started and implemented the facade optimization design in a short training session, although they reported that the developed support tools still needed to be improved in terms of also integrating optimization tools. The characteristics of the user interface help considerably with comparing and making decisions in optimal solutions. The results emphasize the importance of developing design support tools for practical adoption from practical designers’ perspectives. Full article
(This article belongs to the Special Issue Advanced Building Performance Analysis)
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15 pages, 4080 KiB  
Article
Evaluation and Prediction of Pavement Deflection Parameters Based on Machine Learning Methods
by Xueqin Chen, Qiao Dong and Shi Dong
Buildings 2022, 12(11), 1928; https://doi.org/10.3390/buildings12111928 - 9 Nov 2022
Cited by 4 | Viewed by 3176
Abstract
The deflection measurements made using Falling Weight Deflectometers (FWDs) are widely used in the back-calculation of pavement layer moduli. Pavement structural characteristics, changes in temperature, and other related factors exert a significant effect on the deflection measurements. Therefore, three machine learning methods—Classification and [...] Read more.
The deflection measurements made using Falling Weight Deflectometers (FWDs) are widely used in the back-calculation of pavement layer moduli. Pavement structural characteristics, changes in temperature, and other related factors exert a significant effect on the deflection measurements. Therefore, three machine learning methods—Classification and Regression Tree (CART), Random Forest (RF), and Gradient Boosting Decision Tree (GBDT)—were used to evaluate the importance of influencing factors including FWD test conditions, pavement structural parameters, climatic factors, traffic level, rehabilitation level, and service age, on the FWD measurements of deflection basin in this study. The results indicated that structural number was an important feature for all FWD measurements but its importance on lg(D0–D20) and lg(D0–D30) was smaller than other FWD measurements. The relative feature importance of the asphalt layer, base, and subbase on lg(D0–D20) and lg(D0–D30) was asphalt layer > subbase > base; their relative importance on lg(D20–D60), lg(D30–D60), and lg(D30–D90) was asphalt layer > base > subbase; and their relative importance on lg(D90−D120) and lg(D60–D120) was base > subbase > asphalt layer. Among the FWD test condition variables, drop load was the most significant factor influencing deflection measurements. The second-layer temperature was also important for lg(D0–D20), lg(D0–D30), and lg(D0–D45). The importance of precipitation was greater than the freeze index. The prediction results shown that the accuracy of GBDT was as high as 99%. Besides, GBDT outperformed RF, and RF outperformed CART. The analyses between FWD deflection parameters and influencing factors, especially the structural characteristics of the pavement, provide theoretical evidence for the evaluation of pavement layer strength on the basis of FWD data. Full article
(This article belongs to the Special Issue Advanced Building Performance Analysis)
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38 pages, 10280 KiB  
Article
Research on Optimization of Climate Responsive Indoor Space Design in Residential Buildings
by Zhixing Li, Yukai Zou, Mimi Tian and Yuxi Ying
Buildings 2022, 12(1), 59; https://doi.org/10.3390/buildings12010059 - 7 Jan 2022
Cited by 14 | Viewed by 3950
Abstract
This paper first analyzes the climate characteristics of five typical cities in China, including Harbin, Beijing, Shanghai, Shenzhen and Kunming. Then, based on Grasshopper, Ladybug and Honeybee analysis software, according to the indoor layout of typical residential buildings, this research extracts design parameters [...] Read more.
This paper first analyzes the climate characteristics of five typical cities in China, including Harbin, Beijing, Shanghai, Shenzhen and Kunming. Then, based on Grasshopper, Ladybug and Honeybee analysis software, according to the indoor layout of typical residential buildings, this research extracts design parameters such as the depth and width of different rooms and their window-to-wall ratios etc., to establish a climate responsive optimization design process with indoor lighting environment comfort, with heating and cooling demand as the objective functions. Meanwhile, based on Monte Carlo simulation data, ANN (Artificial Neural Network) is used to establish a prediction model to analyze the sensitivity of interior design parameters under different typical cities’ climatic conditions. The study results show that the recommended values for the total width and total depth of indoor units under the climatic conditions of each city are both approximately 14.97 m and 7.88 m. Among them, under the climatic conditions of Harbin and Shenzhen, the design parameters of residential interiors can take the recommended value of UDI optimal or nZEB optimal. While the recommended values of window-to-wall ratios for the north bedroom, master bedroom and living room in Shanghai residential interiors are 0.26, 0.32 and 0.33, respectively. The recommended value of the window-to-wall ratio of the master bedroom in Kunming residences is 0.36, and that of the remaining rooms is between 0.15 and 0.18. The recommended values of window-to-wall ratios for the master bedroom and living room in Beijing residences are 0.41 and 0.59, respectively, and that for the remaining rooms are 0.15. The multi-objective optimization process based on parametric performance simulation used in the study can effectively assist architects in making energy-saving design decisions in the preliminary stage, allowing architects to have a case to follow in the actual design operation process. Full article
(This article belongs to the Special Issue Advanced Building Performance Analysis)
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20 pages, 2640 KiB  
Article
Design Optimization of a Passive Building with Green Roof through Machine Learning and Group Intelligent Algorithm
by Yaolin Lin, Luqi Zhao, Xiaohong Liu, Wei Yang, Xiaoli Hao and Lin Tian
Buildings 2021, 11(5), 192; https://doi.org/10.3390/buildings11050192 - 2 May 2021
Cited by 30 | Viewed by 4740
Abstract
This paper proposed an optimization method to minimize the building energy consumption and visual discomfort for a passive building in Shanghai, China. A total of 35 design parameters relating to building form, envelope properties, thermostat settings, and green roof configurations were considered. First, [...] Read more.
This paper proposed an optimization method to minimize the building energy consumption and visual discomfort for a passive building in Shanghai, China. A total of 35 design parameters relating to building form, envelope properties, thermostat settings, and green roof configurations were considered. First, the Latin hypercube sampling method (LHSM) was used to generate a set of design samples, and the energy consumption and visual discomfort of the samples were obtained through computer simulation and calculation. Second, four machine learning prediction models, including stepwise linear regression (SLR), back-propagation neural networks (BPNN), support vector machine (SVM), and random forest (RF) models, were developed. It was found that the BPNN model performed the best, with average absolute relative errors of 3.27% and 1.25% for energy consumption and visual comfort, respectively. Third, six optimization algorithms were selected to couple with the BPNN models to find the optimal design solutions. The multi-objective ant lion optimization (MOALO) algorithm was found to be the best algorithm. Finally, optimization with different groups of design variables was conducted by using the MOALO algorithm with the associated outcomes being analyzed. Compared with the reference building, the optimal solutions helped reduce energy consumption up to 34.8% and improved visual discomfort up to 100%. Full article
(This article belongs to the Special Issue Advanced Building Performance Analysis)
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