Building Carbon Emissions Research Articles

Energy investigation in buildings applying a solar adsorption chiller coupled with biofuel heaters and solar heating/cooling systems in different climates

Green building is a concept in sustainable development which aims to reduce energy consumption and carbon emission of buildings. This study attempts to design and introduce a novel green HVAC system which reduces both energy consumption and carbon production of buildings. This system uses solar absorption chillers to provide the cooling demands of buildings. In addition, biofuel (biogas) fired boilers are also added to provide the heating demands. By using the novel approach of the study, such HVAC system can be coupled with other renewable technologies like photovoltaics and develop green buildings. In order to design, model and simulate the operation of this system, famous energy modeling tools are compared with each other and Design Builder software is chosen as the appropriate one. The paper also introduces a plan for establishment of such systems which is applicable to any locations. The results of modeling this green HVAC system for 10 various climates indicated considerable advantages of this system over traditional systems. The green HVAC system can save the maximum energy of 50 MWh per year in a four stories building. The maximum amount of 31 tons carbon can also be saved if the said building is equipped with this novel HVAC system. The results also made it clear that the solar cooling system is a great choice for providing cooling needs in hot climates. By coupling this kind of cooling system with biofuel heaters, the sustainable HVAC system can develop green buildings even in cold or cool climates. Adding green electricity producers like photovoltaic panels to this system and the building was successful. The maximum produced green energy in buildings was calculated as the amount of 17.9 MWh/yr.

Internet of Things (IoT)-Integrated Embodied Carbon Assessment and Monitoring of Prefabricated Buildings

Buildings contribute significantly to carbon emissions over their life cycle. Recently, embodied carbon (EC) accounts for an increasing share of life cycle carbon emissions of new buildings as their energy efficiency is improving. However, traditional methods of data collection and communication from diverse carbon sources such as manual recording lead to low-efficient and prone-to-error estimation of embodied carbon emissions. Therefore, this paper aims to propose an Internet of Things (IoT)-integrated embodied carbon assessment and monitoring system (ECAMS) for prefabricated buildings. This system involves three layers, i.e., of data collection, data communication, and data analysis. To provide a theoretical foundation, the EC assessment model is firstly modified to distribute carbon emissions into twelve construction statuses and at five levels of analysis of prefabricated buildings. IoT sensors including radio frequency identification (RFID), acceleration transducer, and global positioning system (GPS) are employed for automated real-time data collection. IoT data will be communicated with building information modelling (BIM) and carbon assessment software through application programming interfaces (APIs). Laboratory tests were conducted to demonstrate the feasibility of sensor-based data collection and communication. The proposed system facilitates more efficient and accurate estimations of prefabricated buildings’ embodied carbon, which should help practitioners to explore effective carbon reduction strategies.

BIM-based approach for the integrated assessment of life cycle carbon emission intensity and life cycle costs

The construction industry is well known for generating a large amount of carbon emissions and this, together with huge costs of buildings during their life cycle, seriously affects its environmental and economic sustainability. That reducing the former tends to increase the latter is a highly significant problem exacerbating the situation. Finding a solution with the lowest carbon emission at a certain cost is an urgent problem to be solved. Research to date aimed at remedying the situation, however, has been largely focused on either treating the two issues independently or partially conjoined, with little progress in their integrated assessment. In response, this study presents a method for integrating the life cycle carbon emissions (LCCE) and life cycle costs (LCC) of buildings to assess their life cycle carbon emission intensity (CEI) based on BIM technology. Through a public building in China as a case study, the feasibility of the method is verified, the key stages of building carbon emissions are analyzed using CEI, and the conversion of high carbon emission materials to low carbon emission materials is explored. The proposed methodology and framework provide solutions and ideas for achieving optimal costs commensurate with low carbon emissions throughout the building life cycle, facilitating the assessment of carbon emissions at the decision-making and design stages, achieving the optimization of building carbon emissions and costs, and enhancing building life cycle sustainability.

Total CO2 emissions associated with buildings in 266 Chinese cities: characteristics and influencing factors

Rapid urbanization and increasing demand for buildings have brought tremendous pressure to reduce carbon emissions for cities. Quantifying the building emissions and exploring the socioeconomic characteristics of CO2 emissions can promote urban low-carbon development. However, existing studies have not yet provided a complete and comprehensive database of city-level CO2 emissions associated with buildings in China. Here, we calculated the building-related CO2 emissions for 266 Chinese cities in 2010 and 2015. Results show that total carbon emissions of buildings increased by 26%. We further divided cities into four groups: megalopolis, metropolises, large cities and medium and small cities. The high-emission concentrated in large cities, whereas medium and small cities showed higher emission intensity and lower per capita emissions. We show that CO2 emissions of buildings were significantly correlated with tertiary industrial outputs. The results provide scientific support for policy evaluation in a multilevel governance context for reducing CO2 emissions of buildings.

Regional differences and influencing factors of the carbon emission efficiency from public buildings in China

The rapid development of the tertiary industry has made the energy consumption of public buildings grow too fast during the operation stage, which has become a key area of energy conservation and emission reduction in China’s construction industry. This study uses the Minimum Distance to Strong effective Frontier function (MinDS) and Malmquist-Luenberger (ML) index analysis methods to measure the public building carbon emission efficiency (PBCEE) of 30 provincial-level units in China’s eight economic regions from 2010 to 2019, and analyze regional differences and evolution. Then, the influencing factors of PBCEE in different regions were analyzed using the fixed-effect panel data model. The results show that: 1) China’s PBCEE is generally low, with an average efficiency value of only 0.74, and there are great differences among regions, showing the spatial characteristics of “high in the east and low in the west.” 2) Relying on the positive impact of technological progress, the PBCEE in the eight regions increased year by year, with an annual growth rate of 1.82%. 3) The influence results and degrees of various factors on PBCEE are different in different economic zones, but increasing the proportion of electricity consumption has a certain positive effect on improving PBCEE. The same influencing factor has obvious threshold characteristics for PBCEE in different regions, so the government needs to consider the actual situation of the region when formulating carbon emission reduction policies for public buildings.

Building Information Modeling (BIM) Driven Carbon Emission Reduction Research: A 14-Year Bibliometric Analysis.

Governments across the world are taking actions to address the high carbon emissions associated with the construction industry, and to achieve the long-term goals of the Paris Agreement towards carbon neutrality. Although the ideal of the carbon-emission reduction in building projects is well acknowledged and generally accepted, it is proving more difficult to implement. The application of building information modeling (BIM) brings about new possibilities for reductions in carbon emissions within the context of sustainable buildings. At present, the studies on BIM associated with carbon emissions have concentrated on the design stage, with the topics focusing on resource efficiency (namely, building energy and carbon-emission calculators). However, the effect of BIM in reducing carbon emissions across the lifecycle phases of buildings is not well researched. Therefore, this paper aims to examine the relationship between BIM, carbon emissions, and sustainable buildings by reviewing and assessing the current state of the research hotspots, trends, and gaps in the field of BIM and carbon emissions, providing a reference for understanding the current body of knowledge, and helping to stimulate future research. This paper adopts the macroquantitative and microqualitative research methods of bibliometric analysis. The results show that, in green-building construction, building lifecycle assessments, sustainable materials, the building energy efficiency and design, and environmental-protection strategies are the five most popular research directions of BIM in the field of carbon emissions in sustainable buildings. Interestingly, China has shown a good practice of using BIM for carbon-emission reduction. Furthermore, the findings suggest that the current research in the field is focused on the design and construction stages, which indicates that the operational and demolition stages have greater potential for future research. The results also indicate the need for policy and technological drivers for the rapid development of BIM-driven carbon-emission reduction.

Sustainable development of green buildings in China and evaluation framework of decarbonization technology of AAC production

AbstractThis paper discusses the path to achieve carbon neutrality in the construction field, which is divided into five parts: (1) China's carbon peaking and carbon neutrality goals and consensus on the development of the construction industry; (2) The sustainable green building development trend and relevant policies in China; (3) The green building evaluation system and common software in China; (4) Building carbon emission evaluation model and the decarbonizing tools for the whole life cycle of construction projects; (5) Carbon emission reduction technology evaluation framework of autoclaved aerated concrete production in China. Through the discussion of this paper, we will jointly provide intellectual and technical support for the exploration and implementation of building carbon neutralization path.

Make your home carbon-free. An open access planning tool to calculate energy-related carbon emissions in districts and dwellings

Reducing the carbon emissions of buildings and whole districts is one of the main objectives of sustainable development goals. Both policymakers and end-users need reliable information to take actions that lead to achieving those goals. For this, an innovative open access planning tool has been developed to assess the effect of energy consumption on the overall carbon emissions of districts, buildings, and house units. When it comes to the end-users, it would help them know the actual environmental impact of their homes and make environmentally and financially sound decisions before investing in new equipment. The tool is meant to be an online planning service for dwelling end-users and policymakers alike; thus responding to a still unresolved demand. For this, firstly, the study focuses on the obtention of a complete catalogue of open-source conversion factors, which convert the different energy sources to carbon dioxide emissions per unit of energy. Secondly, it presents two case studies to illustrate the use of the tool. The first case study explains how an end-user could estimate the energy savings that may result from changing his domestic energy habits, equipment, and sources. The second case study uses actual data from a district in Valencia (Spain) to show how renewable sources would affect the carbon footprint of an apartment block. Both study cases show greenhouse gas emissions savings by replacing the existing equipment with more efficient ones such as heat pumps or renewable energy-based power systems like photovoltaic panels. This study concludes that providing smart tools is pivotal to planning nearly Zero-Energy Districts (nZED).

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.

Evaluation of Urban Green Building Design Schemes to Achieve Sustainability Based on the Projection Pursuit Model Optimized by the Atomic Orbital Search

The popularization and use of green buildings are of great significance for reducing the carbon emissions of buildings and achieving sustainable development. Scientific evaluation of the green building design scheme is the key factor in ensuring the popularization and use of green buildings. To overcome the shortage of a systematic evaluation index system and comprehensive evaluation method, an evaluation index system of green building design schemes and an evaluation method based on the projection pursuit model were developed. First, according to the needs of green building development, an evaluation index system of green building design schemes was systematically constructed from the five aspects of the economy, the resource utilization index, environmental impacts, technical management, and social impacts. The calculation methods of all secondary indexes are provided in detail. Then, a novel evaluation method based on the projection pursuit model optimized by the atomic orbital search was constructed. This method searches for key influencing factors and determines the evaluation grade from the evaluation data structure, and realizes the scientific and objective evaluations of green building design schemes. Finally, the Nanchang Hengda Project was selected to conduct a detailed empirical study. The research results show that the incremental net present value of the investment, the energy consumption of the air conditioning system, and the ratio of the window area to the indoor area are the most important secondary indexes. Moreover, the environmental impact index was found to be the most important primary index. Via comparisons with different optimization algorithms and evaluation methods, the superiority of the proposed model is proven.

China’s commercial building carbon emissions toward 2060: An integrated dynamic emission assessment model

Carbon-mitigation in the commercial building sector is critical to carbon peaking and carbon neutral commitment. However, there has been little scientific focus on long-term evolutionary trajectories and peak path in this sector. This study innovatively develops an integrated dynamic emission assessment model (IDEAM) by combining the system dynamics model with the bottom-up end-use energy model. Moreover, the IDEAM is combined with the scenario analysis approach to model the dynamic evolution of Chinese commercial building carbon emissions toward 2060. The results show that commercial building carbon emissions will peak at 1.28 Gigatons (Gt) of CO2 in 2037 under the baseline scenario and will advance toward 2029 with an emissions peak of 0.98 Gt CO2 under the low-carbon scenario. Cooling and lighting are the two end-uses that contribute most to the growth of carbon emissions at over 70%. These two end-uses indicate different carbon-abatement potentials across climate zones. Sensitivity analysis reveals that promoting technological progress, increasing the share of clean energy, and improving low-carbon awareness are major ways to facilitate the early realization of carbon peaks and carbon neutrality. This study provides a deeper understanding of possible emission pathways and could assist policy-makers in devising scientific carbon mitigation plans.

Comprehensive Carbon Emission and Economic Analysis on Nearly Zero-Energy Buildings in Different Regions of China

Considering the comprehensive effect of building carbon emissions, cost savings is of great significance in nearly-zero-energy buildings (NZEBs). Previous research mostly focused on studying the impact of technical measures in pilot projects. The characteristics of different cities or climate zones have only been considered in a few studies, and the selection of cities is often limited. At times, only one city is considered in each climate zone. Therefore, this study selected 15 cities to better cover climate zone characteristics according to the variation in weather and solar radiation conditions. A pilot NZEB project was chosen as the research subject, in which the energy consumption was monitored and compared across different categories using simulated values by EnergyPlus software. Various NZEB technologies were considered, such as the high-performance building envelope, the fresh air heat recovery unit (FAHRU), demand-controlled ventilation (DCV), a high-efficiency HVAC and lighting system, daylighting, and photovoltaic (PV). The simulated carbon emission intensities in severe cold, cold, and hot summer and cold winter (HSCW) climate zones were 21.97 kgCO2/m2, 19.60 kgCO2/m2, and 15.40 kgCO2/m2, respectively. The combined use of various NZEB technologies resulted in incremental costs of 998.86 CNY/m2, 870.61 CNY/m2, and 656.58 CNY/m2. The results indicated that the HSCW region had the best carbon emission reduction potential and cost-effectiveness when adopting NZEB strategies. Although the incremental cost of passive strategies produced by the envelope system is higher than active strategies produced by the HVAC system and lighting system, the effect of reducing the building’s heating load is a primary and urgent concern. The findings may provide a reference for similar buildings in different climate zones worldwide.

Spatial correlation network structure of China's building carbon emissions and its driving factors: A social network analysis method

Clarifying the spatial association network of provincial building carbon emissions and its influential drivers is profoundly significant for transregional collaborative emission reduction and regionally-coordinated development. This study adopts the social network analysis method to investigate the network structure characteristics of carbon emissions in the building sector based on China's provincial-level evidence from 2000 to 2018. Then, the quadratic assignment procedure is further utilized to examine the driving factors. The results demonstrate that building carbon emissions in China take the form of a network structure. From 2000 to 2018, the relevance and stability of the spatial associations gradually strengthened. Shanghai, Jiangsu, Tianjin, Beijing and Zhejiang are in the center of the spatial association network and play a vital part in the network. The network of carbon emissions in the building sector can be classified into four plates: the main inflow plate, main outflow plate, bidirectional spillover plate and agent plate. Geographical adjacency, economic development level, energy intensity and industrial structure are significantly correlated with building carbon emissions. The urbanization level has no significant influence on the spatial correlations of building carbon emissions. This study is conducive to formulating energy conservation policies and promoting transregional collaborative emission reductions.

A dynamic calculation model of the carbon footprint in the life cycle of hospital building: a case study in China

PurposeThe calculation of buildings’ carbon footprint (CFP) is an important basis for formulating energy-saving and emission-reduction plans for building. As an important building type, there is currently no model that considers the time factor to accurately calculate the CFP of hospital building throughout their life cycle. This paper aims to establish a CFP calculation model that covers the life cycle of hospital building and considers time factor.Design/methodology/approachOn the basis of field and literature research, the basic framework is built using dynamic life cycle assessment (DLCA), and the gray prediction model is used to predict the future value. Finally, a CFP model covering the whole life cycle has been constructed and applied to a hospital building in China.FindingsThe results applied to the case show that the CO2 emission in the operation stage of the hospital building is much higher than that in other stages, and the total CO2 emission in the dynamic and static analysis operation stage accounts for 83.66% and 79.03%, respectively; the difference of annual average emission of CO2 reached 28.33%. The research results show that DLCA is more accurate than traditional static life cycle assessment (LCA) when measuring long-term objects such as carbon emissions in the whole life cycle of hospital building.Originality/valueThis research established a carbon emission calculation model that covers the life cycle of hospital building and considered time factor, which enriches the research on carbon emission of hospital building, a special and extensive public building, and dynamically quantifies the resource consumption of hospital building in the life cycle. This paper provided a certain reference for the green design, energy saving, emission reduction and efficient use of hospital building, obviously, the limitation is that this model is only applicable to hospital building.

Spatiotemporal characteristics and influencing factors of carbon emissions from civil buildings: Evidence from urban China.

Carbon emissions from civil buildings refer to the carbon emissions generated during the operation of civil buildings. With the continuous development of the urban economy and the improvement of people’s living standards, this part of carbon emission puts tremendous pressure on China to achieve the goal of carbon peaking and carbon neutral. In the context of rapid urbanization, studying the spatiotemporal characteristics and influencing factors of the carbon emissions from civil buildings have strong practical significance for China to achieve the "dual carbon" goal. Based on the emission data from 104 prefecture-level cities in China, we examine the spatiotemporal characteristics of the civil building carbon emissions from the perspectives of temporal evolution trend, spatial distribution and its dynamic evolution, spatial difference and its decomposition, and spatial autocorrelation characteristics. Finally, we reveal the influencing factors of the carbon emissions from civil buildings using static panel data models and spatial dynamic panel data models. The results of the study show that: (1) During the sample period, the carbon emissions from civil buildings have increased year by year. The civil building carbon emissions have become an important source of China’s overall carbon emissions. Realizing energy saving and emission reduction in the operational stage of civil buildings is crucial to realizing China’s "dual carbon" goal. (2) According to the estimated results, there is a significant inverted U-shaped non-linear relationship between urbanization and civil building carbon emissions. Most Chinese cities are located in the upward part of the inverted U-shaped curve at present. Thus, the traditional economic growth model characterized by high energy consumption and high emission during rapid urbanization should be abandoned to reduce the carbon emissions from civil buildings. (3) Technological progress and fixed asset investment can effectively reduce the carbon emissions from civil buildings. At the same time, the level of marketization and social consumption expenditure positively affect the carbon emissions from civil buildings. It is necessary to improve the relevant market mechanisms, policy subsidies, and other means to encourage the application of green energy-saving technologies in civil buildings. Also, it is needed to guide the urban residents’ consumption structure and lifestyle in a low-carbon direction, to reduce the energy consumption and carbon emissions during the operation of civil buildings.

Building Carbon Emission Scenario Prediction Using STIRPAT and GA-BP Neural Network Model

As a major province of energy consumption and carbon emission, Jiangsu Province is also a major province of the construction industry, which is a key region and potential area for carbon emission reduction in China. The research and prediction of carbon emission in the construction industry is of great significance for the development of low-carbon policies in the construction industry of other cities. The purpose of this paper is to study the influencing factors of the whole life cycle carbon emissions of buildings in Jiangsu Province, and to predict the carbon emissions of buildings in Jiangsu Province based on the main influencing factors. This paper uses the energy balance sheet splitting method, STIRPAT model, gray correlation method and GA-BP neural network model to study and predict the carbon emissions of construction industry in Jiangsu Province. The research results show that the resident population, urbanization rate, steel production, average distance of road transportation, and labor productivity of construction enterprises have a catalytic effect on construction carbon emissions; GDP per capita and added value of tertiary industry have a suppressive effect; construction carbon emissions reached the historical peak in 2012; the prediction results show that the future construction carbon emissions in Jiangsu province generally show a decreasing trend. The research results of this paper provide a possibility to refine the study of construction carbon emission, and also provide a basis and guidance for subsequent research on construction carbon emission.

Daily Carbon Assessment Framework: Towards Near Real-Time Building Carbon Emission Benchmarking for Operative and Design Insights

The energy consumption and its related carbon emission of non-domestic complex buildings in an urban context are complicated due to their wide variety of functions and services. A detailed assessment of the carbon emission of such buildings can contribute to decision making for in-operation building management and schematic designs of future proposals. Concurrently, advances in smart meter data analytics and sensor-enabled operational data streams offer the opportunity to investigate this problem at a finer temporal resolution. This research developed a daily carbon emission benchmarking system of a mixed-use building in a UK university. The research period was set at an annual range from 1 January 2019 to 31 December 2019 and was segmented by strategic periods in line with the operation schedule of the building. The daily benchmark revealed the fluctuation of the building’s energy consumption and associated carbon emissions. Based on this, a digital twin framework was developed to identify the possible time periods when the building is less carbon efficient and potential building characters that can lead to increased carbon emission in the operational stage compared with what originally expected at the design stage. We discuss how these insights can offer actionable knowledge for user groups such as asset managers and architects.

Bridging the knowledge gap between energy-saving intentions and behaviours of young people in residential buildings

The energy-efficient behaviour of occupants in residential buildings is critical to reducing the carbon emissions of buildings. However, it is not clear how to motivate occupants, especially the younger generation, who are considered to be more environmentally concerned than previous generations, to engage in energy-saving behaviour and increase their energy-saving intentions. This study integrates “Personal moral norms” (PMNs) into the theory of planned behaviour (TPB) and proposes a new theoretical framework aimed at understanding important internal factors that influence energy-saving behaviour and behavioural intentions in student dormitories. Using partial least squares structural equation modelling, this paper conducts an empirical analysis of 448 questionnaires collected in universities. The results showed that (1) attitudes, subjective norms, perceived behavioural control, and PMNs were significantly positively correlated with energy-saving intentions. (2) The results verify the validity of the extended TPB model. (3) Students’ energy-saving intentions have indirect mediating effects on attitudes, subjective norms, perceived behavioural control, PMNs, and energy-saving behaviours. (4) The effects of subjective norms and perceived behavioural control on energy-saving intentions were significantly different by gender. This study suggests directions for research on the energy-saving behaviour of the young generation.

Forecast of Energy Consumption and Carbon Emissions in China’s Building Sector to 2060

The goal of reaching the peak of carbon in the construction industry is urgent. However, the research on the feasibility of realizing this goal and the implementation of relevant policies in China is relatively superficial. In view of the historical data of energy consumption and building CO2 emission from 1995 to 2019, this paper establishes a BP neural network model for predicting building CO2 emissions. Moreover, the influencing factors, such as population, GDP, and total construction output, are introduced as the parameters in the model. Through the scenario analysis method explores the practical path to accomplish the peak of building CO2 emissions. When using traditional prediction methods to predict building carbon emissions, the long prediction cycle will increase the possibility of significant errors. Therefore, this paper constructs the calculation model of building carbon emission and forecasts the future carbon emission value through the BP neural network to avoid the error caused by the nonlinear relationship between influencing factors and predicted value. It will effectively predict the feasibility of the carbon peak and the carbon-neutral target set by government, and provide a useful predictive tool for adjusting the new energy structure and formulating related emission reduction policies.

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.