Building Performance Optimization Research Articles

Research on the Optimization of Green Building Performance Based on BIM Technology

With the acceleration of urbanization, the construction industry has developed rapidly worldwide but has also brought serious environmental problems. Traditional architectural design methods often only focus on the function and beauty of the building while ignoring its impact on the environment. In addition, the lack of effective design and construction management methods also led to high resource and energy consumption. To overcome this challenge, the concept of green building came into being. Green buildings emphasize reducing the negative impact of buildings on the environment and improving resource utilization efficiency throughout the entire life cycle. BIM technology provides strong support for achieving this goal. Based on this, starting from the role of BIM technology in green building performance optimization, this article analyzes the optimization of green building performance solutions based on BIM technology in detail to promote the sustainable development of buildings.

Defining weather scenarios for simulation-based assessment of thermal resilience of buildings under current and future climates: A case study in Brazil

In response to increasingly severe weather conditions, optimization of building performance and investment provides an opportunity to consider co-benefits of thermal resilience during energy efficiency retrofits. This work aims to assess thermal resilience of buildings using building performance simulation to evaluate the indoor overheating risk under nine weather scenarios, considering historical (2010s), mid-term future (2050s), and long-term future (2090s) typical meteorological years, and heat wave years. Such an analysis is based on resilience profiles that combine six integrated indicators. A case study with a district of 92 buildings in Brazil was conducted, and a combination of strategies to improve thermal resilience was identified. Results reflect the necessity of planning for resilience in the context of climate change. This is because strategies recommended under current conditions might not be ideal in the future. Therefore, an adaptable design should be prioritized. Cooling energy consumption could increase by 48 % by the 2050s, while excessive overheating issues could reach 37 % of the buildings. Simple passive strategies can significantly reduce the heat stress. A comprehensive thermal resilience analysis should ultimately be accompanied by a thorough reflection on the stakeholders’ objectives, available resources, and planning horizon, as well as the risks assumed for not being resilient.

Multi-objective optimization of residential building energy consumption, daylighting, and thermal comfort based on BO-XGBoost-NSGA-II

The energy consumption, daylighting, and thermal comfort of buildings directly affect the three key goals of residents. However, there is little research on the optimization of energy consumption, daylighting, and thermal comfort in residential buildings in China. Therefore, this study proposes an optimization framework that combines Bayesian optimization with extreme gradient boosting trees (BO-XGBoost) and non-dominated genetic algorithm-II (NSGA-II) to study the multi-objective optimization of residential building performance. This paper first uses Grasshopper to simulate and obtain a dataset through Latin hypercube sampling (LHS). BO-XGBoost is used to establish the regression relationship between building envelope design parameters and residential building performance. Then, the obtained regression model is used as the fitness function of NSGA-II to get the Pareto optimal solution set. Finally, the ideal point method is used to obtain the optimal combination of building envelope structure parameters for residential buildings. Taking a residential building in a hot summer and cold winter area as an example, the effectiveness of this method is verified. The results show that (1) BO-XGBoost has excellent predictive performance, with R2 values of 0.997, 0.960, and 0.994 for energy consumption, thermal comfort, and daylighting, respectively. (2) The proposed BO-XGBoost-NSGA-II can effectively achieve multi-objective optimization. Compared with the initial scheme of the case building, energy consumption is reduced by 44.1%, thermal comfort index is reduced by 10.9%, and daylighting performance is improved by 1.7%. Therefore, the proposed method can effectively optimize the performance goals of residential buildings and provide practical ideas for similar problems.

Optimizing daylight in west-facing facades for LEED V4.1 compliance using metaheuristic approach

This study introduces an optimized design approach for west-facing room façades to improve daylighting while adhering to LEED v4.1 sustainability criteria. Employing parametric modeling, metaheuristic optimization, and validated daylight simulations, the research highlights the African Vulture Optimization Algorithm's success in achieving 100% LEED compliance and superior performance over random models in daylight sufficiency and glare reduction. Light-colored materials and transparent glazing emerged as beneficial for LEED points. Despite computational limitations and the need for empirical validation, this method offers architects versatile and sustainable design solutions. Comparative analysis reveals the algorithm's strong performance, although opportunities exist for refinement. Future research directions include contrasting this algorithm with other optimization methods, focusing on empirical backing, assessing environmental and human-centric impacts, adapting to varied building types and conditions, and examining diverse geographical and material factors. This work advances daylight-integrated façade design, suggesting a more comprehensive framework for building performance optimization.

Improving building resilience in the face of future climate uncertainty: A comprehensive framework for enhancing building life cycle performance

This research introduces an innovative resilient design framework, addressing gaps in building performance optimization by considering a holistic life cycle perspective and factoring in climate projection uncertainties from the 2020s to 2090s. The study concludes that in the case study, future climate scenarios in different Shared Socio-Economic Paths significantly impact building life cycle performance, with wall U-value, windows U-value, and wall density identified as major factors affecting building performance under future climate uncertainty. The ensemble learning model achieves high fidelity in predicting life cycle carbon emissions (LCCE), life cycle cost (LCC), and indoor discomfort hours (IDH) through input feature screening and hyperparameter optimization. Comparing optimization algorithms, Two-Archive Evolutionary Algorithm for Constrained multi-objective optimization (C-TAEA) demonstrates better convergence degree and optimization effect. Among the tested multi-criteria decision making methods, the VIKOR method selects the best building resilient design scheme from the Pareto data set, effectively reducing LCCE, LCC, and IDH, and leading to an overall improvement in building life cycle performance. Applying this framework, the finalized scheme for an office building in cold region optimizes 44.0% of LCCE, 8.2% of LCC, and 4.3% of IDH. This framework develops a new approach to improve building life cycle performance under future climate uncertainty and supports informed decision-making for resilient building design. To promote carbon neutrality in construction, it is urged that future studies incorporate additional sources of uncertainty, such as occupant behavior, into the proposed framework.

A human-machine collaboration frame in daylighting optimization of semi-outdoor space design by using phased synergistic method: A case study

The semi-outdoor space (SOS) of a building is a valuable activity space and a microclimate transition zone for occupants. Current parametric and optimization methods for SOS design limit the flexibility and complexity of the design process and lack human-machine interaction, which can lead to design results that do not meet specific requirements and diverge significantly from designer expectations. Therefore, this paper proposes a new dynamic and in-depth human-machine collaborative design method to solve multi-level, complex semi-outdoor space design problems and provide preferable daylighting performance. A phased synergistic method (PSM) is proposed based on hierarchical iterations of design variables. The new method allows for integrating existing design tools and expands their optimization scope to accommodate early design stages with ambiguous requirements. A case study building in Guangzhou, China, is used to showcase the design process and daylighting performance evaluation results. The case study uses the intuitive design method (IDM) and the non-phased method (NPM) results for performance and usability comparison. The study shows that the new method can find spatial combinations with better daylighting performance. It also illustrates the positive effect of adequate human-machine interaction in enhancing building performance and form optimization.

EXPLORING SCIENTIFIC DOMAIN OF OCCUPANCY ANALYSIS, POE, AND DT IN AECO INDUSTRY: A SCIENTOMETRIC APPROACH

Building occupancy patterns strongly influence organizational effectiveness during the operation and maintenance (O&M) phase. Actual occupancy is highly variable over time and differs from the static occupancy levels considered during design phases. Consequently, poor levels of space management, use, and cleanness are detected during the O&M phase, affecting user well-being and satisfaction. Occupancy monitoring and analysis are needed to achieve effective and efficient facility management (FM) and user well-being. The article explores the scientific domains of occupancy detection and analysis, and related main approaches, i.e., post-occupancy evaluations (POEs) and digital twins (DTs) in the architecture, engineering, construction and operation (AECO) industry. A scientometric approach through science mapping and data visualization is applied to analyze 386 bibliographic records from Scopus database. The temporal trend analysis and conceptual structure of the scientific domain are investigated, drawing a picture of the body of knowledge. The performed analysis uncovered the temporal distribution of the publications and the relationships among the topics. DT is a recent topic in the early stages of investigation, while POEs are mature approaches related to sustainability, energy efficiency, productivity, building performance optimization, and user satisfaction. Occupancy detection and analysis are related to FM but less investigated topics in recent years. The research aims to uncover possible research gaps and open challenges in the field for further research aiming to support the optimization of occupancy and space management in the O&M phase, increasing workplace adaptability to changing conditions and needs and user satisfaction and well-being over time.

Global sensitivity analysis based on multi-objective optimization of rural tourism building performance

Morphological design has significant impacts on the performance of rural tourism buildings (RTBs). Multi-objective optimization has been practiced for three prototypes of RTBs in Tianjin, China to explore the RTB design with better energy load, indoor daylighting and thermal comfort performance. However, making decisions from the solution set is still a challenging task for decision-makers. So post-optimization analysis is conducted in this study, and multi-objective optimization solution set of the three RTB types is investigated through global sensitivity analysis (GSA) on the platform of R studio. To begin with, correlation coefficients of the parameters are analyzed by Spearman and partial correlation coefficients methods, and strong correlation relationships are discovered among design parameters of RTBs. Afterwards, Genizi and random forest-based cforest methods are adopted for GSA towards different performance of RTBs, and comprehensive analysis is implemented for the results. Finally, it's found that the depth of frequently used space has significant impacts on annual, cooling and heating load, and daylighting of RTBs. Besides, the height of skylight is discovered to be key important parameter for both cooling and daylighting, and the width as well as the window to wall ratio of the heating space in winter have important influence on winter thermal comfort performance. Overall, the framework and methodology for GSA in post-optimization phase is put forward. The influential mechanism of morphological design parameters on energy load, daylighting and thermal comfort performance of RTBs is explored on basis of the multi-objective optimization solution set. This study not only supplies scientific suggestions for decision-making of RTBs in Tianjin, China, but also offers reference for RTB design and practice in similar regions of the world.

Building energy simulation and its application for building performance optimization: A review of methods, tools, and case studies

Building energy simulation and its application for building performance optimization: A review of methods, tools, and case studies

Surrogate Models for Efficient Multi-Objective Optimization of Building Performance

Nowadays, the large set of available simulation tools brings numerous benefits to urban and architectural practices. However, simulations often take a considerable amount of time to yield significant results, particularly when performing many simulations and with large models, as is typical in complex urban and architectural endeavors. Additionally, multiple objective optimizations with metaheuristic algorithms have been widely used to solve building optimization problems. However, most of these optimization processes exponentially increase the computational time to correctly produce outputs and require extensive knowledge to interpret results. Thus, building optimization with time-consuming simulation tools is often rendered unfeasible and requires a specific methodology to overcome these barriers. This work integrates a baseline multi-objective optimization process with a widely used, validated building energy simulation tool. The goal is to minimize the energy use and cost of the construction of a residential building complex. Afterward, machine learning and optimization techniques are used to create a surrogate model capable of accurately predicting the simulation results. Finally, different metaheuristics with their tuned hyperparameters are compared. Results show significant improvements in optimization results with a decrease of up to 22% in the total cost while having similar performance results and execution times up to 100 times faster.

Dual-objective building retrofit optimization under competing priorities using Artificial Neural Network

Building retrofit has received renewed interests in recent years, driven by energy-savings and indoor environmental quality goals. Digital technologies such as building performance simulation and optimization algorithms have been used to identify optimal retrofit schemes, yet the existing approaches are limited by the slow running speed of physics-based models and sub-optimal results. This study describes a novel framework, the Building Performance Optimization using Artificial Neural Network (BPO-ANN), which can automatically identify optimal building retrofit schemes. A robust Artificial Neural Network model was developed and validated as a surrogate to rapidly assess building performances, which was then connected to a genetic algorithm in search of Pareto optimal. The impact of key design attributes on building performances have been assessed using sensitivity analysis. The BPO-ANN framework has been tested in a high-performing campus building in Northern China under two competing objectives: building energy demand and occupant thermal comfort. It can automatically identify optimal design schemes, which were expected to achieve an energy-savings of 4% and reduce the annual thermal discomfort percentage by 4%. Sensitivity analysis suggested that window-to-wall ratio and HVAC setpoint have contributed the most to the performances of the campus building, followed by the roof U-value and wall U-value. The study has contributed methodologically to simulation-based optimization method, with novelties in the use of neural network algorithms to accelerate the otherwise time-consuming physics-based simulation models. It has also contributed a robust procedure in the tuning of hyperparameters in neural network models, with marked improvements in model prediction and computational efficiency.

Building Performance Optimization throughout the Design–Decision Process with a Holistic Approach

Building performance optimization throughout the design–decision process has not been well defined, especially in holistic approaches that require multilevel integration. For this, it is essential to ensure coexistence between different analysis programs such as three-dimensional (3D), energy, and computational fluid dynamics (CFD) models that can be applied on a single platform such as building information modeling (BIM). This research aims, first, to define and document a holistic design approach to building performance optimization concerning energy usage by employing the BIM platform and, second, to underline interoperability and data exchange between current technology and commercially available software from different vendors. Therefore, the novelty of this study is to serve a systematic, multiobjective, and multicriterion optimization of building performance with a holistic approach in the BIM platform. The holistic design approach is for a multidisciplinary design process including three stages (conceptual, schematic, and detailed) that cover different optimization criteria (building envelope design, occupants’ comfort conditions, and building’ energy consumption). The proposed method aims to generate, evaluate, develop, and utilize a 3D model during the different design stages concerning the model’s level of details and included information, which underlines the usage of 3D model-related software and sharing data among the design stages. A BIM-based method is developed through the case study of a hypothetical, two-story building (domestic dwelling) by identifying the activities of each design stage. With the implementation of the proposed method, the result shows a predicted energy saving of up to 75%, with a detailed energy analysis placing the building energy performance in the “A” class (annual energy consumption 17,865 kW · h). At last, based on the authors’ experience during the study, it is time- and effort-saving to have interoperability between BIM tools.

Based on ANN and many-objective optimization to improve the performance and economy of village houses in Chinese cold regions

The number of studies considering building performance optimization (BPO) in the building design phase is steadily growing, but many of the existing studies do not consider the applicability of many-objective optimization algorithms when increasing the objective dimensions. This article first compares the NSGA-II, IDBEA, MSOPS-II, and NSGA-III algorithms. Then, the algorithm most suitable for many-objective optimization is combined with Artificial natural network(ANN) and TOPSIS-AHP to complete the optimization of four dimensions of building energy consumption (EC), useful daylight illuminance (UDI), comfort time ratio (CTR) and energy-saving envelope cost (ESEC) for village houses in cold regions of China. The results show that the NSGA-III algorithm performs well in terms of convergence speed, convergence, diversity, and uniformity when solving many-objective problems compared to the other three algorithms. Finally, four optimization strategies were selected using the TOPSIS-AHP method.

An interactive multi-criteria decision-making method for building performance design

Building performance design is essential to support sustainable urban development. It involves multiple conflicting performance, such as energy consumption and daylighting. These potential conflicts present a challenging issue for architects to get trade-off solutions. This study proposed a multi-criteria decision-making method based on sensitivity analysis and analytic hierarchy process for building performance optimization, which provided architects with a real-time interactive way to handle the building’s sustainable design. After inputting the initial scheme and the expected performance goals, architects can modify the scheme according to the variable modification strategy recommended by the method and combine it with their preferences to improve the scheme’s performance. Application of the proposed method to a case study of office building design in the China context was achieved. The results showed that the optimal solutions obtained with the help of the proposed method had better performance than those obtained without the method. Architects have also increased their design efficiency by 211.5%. The proposed method promotes the applicability of decision-making methods in building sustainable design and also provides a satisfactory method to support the improvement of building performance and also sustainability for modern cities.

Multi-objective optimization of building envelope in different climate zones in China based on BP-NSGA-Ⅱ under the future climate

Global warming has an impact on building performance, and it is very important to explore the optimization of building performance under future climate change conditions. The study generates 2050s typical meteorological year (TMY) data of different cities (Harbin, Beijing, Shanghai, Shenzhen) representing the future climate. Taking energy consumption, thermal comfort, and initial investment cost as the objective function, the Back Propagation (BP) neural network and non-dominated sorting genetic algorithm (NSGA-Ⅱ) were used to optimize the key parameters of the building envelope of representative cities in different climate regions of China and to obtain the Pareto curve. The final solution is obtained by the weighted sum method (WSM). The results show that, except for the type of windows, the optimal configuration of the building envelope in each city is different. Compared with the results of reference buildings, the final results of each city reduces energy consumption by 14.5~24.0 % and improves thermal comfort by 23.8~34 % when the initial investment cost increases by 27.0~35.3 %. The method proposed in this paper has reference significance for the optimization of building envelope in different climatic regions of China under the future climate.

An Integrated User Interface of Assessment and Optimization for Architectural Façade Shading Designs in Taiwan

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.

Data-driven prediction and optimization of residential building performance in Singapore considering the impact of climate change

Research on the performance of tropical residential buildings requires careful consideration of the effects of the changing weather conditions. Based on machine learning and multi-objective genetic optimization algorithms, this study proposes an integrated framework for predicting and optimizing the performance of residential buildings in Singapore. The framework was used for two Singapore residential building types, the point block building and the slab block building, to examine their optimal performance in terms of daylight performance, energy efficiency, and thermal comfort under future climate conditions in the short term (2021–2040), middle term (2041–2060), and long term (2061–2100). The results show that the XGBoost algorithm and transfer learning method perform well in building performance prediction, specifically in the source domain (point block buildings) with R2 = 0.95 and in the target domain (slab block building) with R2 = 0.87. The general trend in the optimal design parameters for residential buildings is to enhance the thermal insulation of building walls and envelopes to better cope with future warming climate conditions, while providing adequate indoor lighting. This study contributes to the field of climate-resilient residential buildings in Singapore. This is closely linked to the Singapore government's quest for sustainable development and to enhance the well-being of its residents.

A multi-objective optimization strategy for building carbon emission from the whole life cycle perspective

This research proposed an integrated strategy for building performance optimization from the whole life cycle perspective to explore the optimal building scheme. After the feature elimination, the ensemble learning model (ELM) was trained to obtain a high-precision model for predicting life cycle carbon emissions (LCCE), life cycle costs (LCC), and indoor discomfort hours (IDH). Then, the optimal optimization algorithm was selected among three different optimization algorithms. Finally, the best building scheme was chosen according to the newly proposed solution. The results showed that the ELM could achieve high prediction efficiency by combining input feature evaluation and screening, multi-sampling methods, and hyperparameter optimization. The R2 value of ELM can reach 0.980, while the Two-Archive Evolutionary Algorithm for Constrained multi-objective optimization (C-TAEA) was the optimal optimization algorithm. The best equilibrium solution proposed in this study solved the problem of different optimization ranges of different objectives and maximized the optimization value. Finally, the best equilibrium scheme reduced the LCCE by 34.7%, the LCC by 13.9%, and the IDH by 26.6%. Therefore, this strategy can efficiently optimize building objectives and produce a more balanced and optimal building scheme, thus making it widely applicable in building performance optimization.

Application of LightGBM Algorithm in the Initial Design of a Library in the Cold Area of China Based on Comprehensive Performance

The proper application of machine learning and genetic algorithms in the early stage of library design can obtain better all-around building performance. The all-around performance of the library, such as indoor temperature, solar radiation, indoor lighting, etc., must be fully considered in the initial design stage. Aiming at building performance optimization and based on the method of “generative design”, this paper constructs the library’s comprehensive performance evaluation workflow and rapid prediction combined with the LightGBM algorithm. A library in a cold region of China is taken as the research object to verify its application. In this study, 5000 scheme samples generated in the iterative genetic optimization process were taken as data sets. The LightGBM algorithm was used to classify and predict design schemes, with a precision of 0.78, recall rate of 0.93, and F1-Score of 0.851. This method can help architects to fully exploit the optimization potential of the building’s all-around performance in the initial stage of library design and ensure the timely interaction and feedback between design decisions and performance evaluation.

An integrated framework for multi-objective optimization of building performance: Carbon emissions, thermal comfort, and global cost

In this paper, we proposed an integrated optimization framework to explore minimum building carbon emissions (BCE), indoor discomfort hours (IDH), and global cost (GC) of building, as well as a new formula for selecting the best scheme in the Pareto front set. Such framework improves the efficiency of the optimization process, the accuracy of the results, and the rationality of the best scheme. The entire optimization process can be divided into four steps. First, the input parameters were randomly generated using three sampling methods and then simulated to build the database. Then, the contribution rates of the selected parameters to the outputs were comprehensively evaluated and combined with multi-sensitivity analysis methods to screen important parameters. Next, we trained and validated the Back Propagation Neural Network (BPNN) model, in which different methods were used for hyperparametric optimization. Third, based on the comparison of various optimization methods, the Non-dominated Sorting Genetic Algorithm-III (NSGA-III) was selected and combined with BPNN to solve the proposed multi-objective optimization problem. Then, finally, we applied the proposed optimal balance formula to select the scheme that considered all aspects of objectives in the Pareto front set. The results demonstrated that the best sampling method and hyperparameter combination can result in an R 2 of BPNN that reaches 0.992. The simulation results are in good agreement with the optimization results. Compared with the case building, the optimal balance schemes of BCE, IDH, and GC were reduced by 53.25%, 42.95%, and 22.33%, respectively. Therefore, we demonstrated that this method is feasible and effective for improving building design in more practical and complex situations and can be widely popularized in the building performance optimization field. • This integration method can predict and optimize complicated solutions efficiently. • Several sampling and sensitivity analysis methods were applied in data evaluation. • BPNN models were trained to replace simulation process. • NSGA-III was the best algorithms in building performance optimization. • A new optimal solution was proposed for the optimal balance scheme.