Building Simulation最新文献

The air infiltration rate of buildings strongly influences indoor environment and energy consumption. In this study, several traditional methods for determining the air infiltration rate were compared, and their accuracy in different scenarios was examined. Additionally, a method combining computational flow dynamics (CFD) with the Swami and Chandra (S-C) model was developed to predict the influence of the surrounding environment on the air infiltration rate. Two buildings in Dalian, China, were selected: one with a simple surrounding environment and the other with a complex surrounding environment; their air infiltration rates were measured. The test results were used to validate the accuracy of the air infiltration rate solution models in different urban environments. For the building with a simple environment, the difference between the simulation and experimental results was 0.86%–22.52%. For the building with a complex environment, this difference ranged from 17.42% to 159.28%. We found that most traditional models provide accurate results for buildings with simple surrounding and that the simulation results widely vary for buildings with complex surrounding. The results of the method of combining CFD with the S-C model were more accurate, and the relative error between the simulation and test results was 10.61%. The results indicate that the environment around the building should be fully considered when calculating the air infiltration rate. The results of this study can guide the application of methods of determining air infiltration rate.

Bifacial PV modules capture solar radiation from both sides, enhancing power generation by utilizing reflected sunlight. However, there are difficulties in obtaining ground albedo data due to its dynamic variations. To address this issue, this study established an experimental testing system on a rooftop and developed a model to analyze dynamic albedo variations, utilizing specific data from the environment. The results showed that the all-day dynamic variations in ground albedo ranged from 0.15 to 0.22 with an average of 0.16. Furthermore, this study evaluates the annual performance of a bifacial PV system in Beijing by considering the experimental conditions, utilizing bifacial modules with a front-side efficiency of 21.23% and a bifaciality factor of 0.8, and analyzing the dynamic all-day albedo data obtained from the numerical module. The results indicate that the annual radiation on the rear side of bifacial PV modules is 278.90 kWh/m², which accounts for only 15.50% of the front-side radiation. However, when using the commonly default albedo value of 0.2, the rear-side radiation is 333.01 kWh/m², resulting in an overestimation of 19.40%. Under dynamic albedo conditions, the bifacial system is predicted to generate an annual power output of 412.55 kWh/m², representing a significant increase of approximately 12.37% compared to an idealized monofacial PV system with equivalent front-side efficiency. Over a 25-year lifespan, the bifacial PV system is estimated to reduce carbon emissions by 8393.91 kgCO₂/m², providing an additional reduction of 924.31 kgCO₂/m² compared to the idealized monofacial PV system. These findings offer valuable insights to promote the application of bifacial PV modules.

Indoor environment quality (IEQ) is one of the most concerned building performances during the operation stage. The non-uniform spatial distribution of various IEQ parameters in large-scale public buildings has been demonstrated to be an essential factor affecting occupant comfort and building energy consumption. Currently, IEQ sensors have been widely employed in buildings to monitor thermal, visual, acoustic and air quality. However, there is a lack of effective methods for exploring the typical spatial distribution of indoor environmental quality parameters, which is crucial for assessing and controlling non-uniform indoor environments. In this study, a novel clustering method for extracting IEQ spatial distribution patterns is proposed. Firstly, representation vectors reflecting IEQ distributions in the concerned space are generated based on the low-rank sparse representation. Secondly, a multi-step clustering method, which addressed the problems of the “curse of dimensionality”, is designed to obtain typical IEQ distribution patterns of the entire indoor space. The proposed method was applied to the analysis of indoor thermal environment in Beijing Daxing international airport terminal. As a result, four typical temperature spatial distribution patterns of the terminal were extracted from a four-month monitoring, which had been validated for their good representativeness. These typical patterns revealed typical environmental issues in the terminal, such as long-term localized overheating and temperature increases due to a sudden influx of people. The extracted typical IEQ spatial distribution patterns could assist building operators in effectively assessing the uneven distribution of IEQ space under current environmental conditions, facilitating targeted environmental improvements, optimization of thermal comfort levels, and application of energy-saving measures.

Abstract

Since indoor clothing insulation is a key element in thermal comfort models, the aim of the present study is proposing an approach for predicting it, which could assist the occupants of a building in terms of recommendations regarding their ensemble. For that, a systematic analysis of input variables is exposed, and 13 regression and 12 classification machine learning algorithms were developed and compared. The results are based on data from 3352 questionnaires and 21 input variables from a field study in mixed-mode office buildings in Spain. Outdoor temperature at 6 a.m., indoor air temperature, indoor relative humidity, comfort temperature and gender were the most relevant features for predicting clothing insulation. When comparing machine learning algorithms, decision tree-based algorithms with Boosting techniques achieved the best performance. The proposed model provides an efficient method for forecasting the clothing insulation level and its application would entail optimising thermal comfort and energy efficiency.

Immersed in the rich tapestry of traditional culture, Gugak, the traditional Korean music stands as a captivating embodiment of artistic expression. This study embarked on a comprehensive evaluation of a Gugak hall, employing acoustic measurements, computer simulations, and subjective perception surveys. The evaluation focused on the reverberance, clarity, spatial impression, and preference, unravelling the secrets that shape the immersive Gugak experience. Through intricate computer simulations and auralization, the experience of Gugak performances was meticulously brought to life, allowing exploration under diverse conditions by adjusting stage volume ratios from −20% to +20% and modifying the interior materials, including the walls, ceiling, and lateral reflectors. Although Gugak halls exhibited relatively low values of reverberation time (RT), early decay time (EDT), and binaural quality index (BQI) the dominant factor influencing the acoustic environment was the effect of sound strength (G). Musical clarity (C80) value did not show an inverse proportionality to the reverberation time. Furthermore, genre differences between traditional Korean and Western classical music did not significantly affect listeners’ perception and satisfaction with regards to reverberance, clarity, and spatial impression. As a result, Gugak halls can adhere to the same acoustic design criteria as Western orchestra halls, since this study found that people perceived them the same way. In this study, sound strength was found to be strongly correlated with perception indicators. It was possible to enhance listeners’ perception and preference regarding the acoustic environment through material and structural changes to the sidewalls and ceiling. These changes improved the reinforcement of low frequencies and simultaneously enhanced the relative effect of side reflections. Additionally, enhancing the reflection and spatial characteristics of the materials effectively improved listener preference. Based on these findings, an optimal design solution was proposed.

Changing climate intensifies heat stress, resulting in a greater risk of workplace productivity decline in timber office buildings with low internal thermal mass. The impact of climate change induced heat exposure on indoor workplace productivity in timber office buildings has not been extensively researched. Therefore, further investigation to reduce the work capacity decline towards the end of the century is needed. Here, heat exposure in a net zero-carbon timber building near Brussels, Belgium, was evaluated using a reproducible comparative approach with different internal thermal mass levels. The analysis indicated that strategies with increased thermal mass were more effective in limiting the effects of heat exposure on workplace productivity. The medium and high thermal mass strategies reduced workplace productivity loss to 0.1% in the current, 0.3% and 0.2% in the midfuture, and 4.9% and 3.9% for future scenarios. In comparison, baseline with low thermal mass yielded a decline of 2.3%, 3.3%, and 8.2%. The variation in maximum and minimum wet-bulb globe temperatures were also lower for medium and high thermal mass strategies than for low thermal mass baseline. The study findings lead to the formulation of design guidelines, identification of research gaps, and recommendations for future work.

Energy demand fluctuations due to low probability high impact (LPHI) micro-climatic events such as urban heat island effect (UHI) and heatwaves, pose significant challenges for urban infrastructure, particularly within urban built-clusters. Mapping short term load forecasting (STLF) of buildings in urban micro-climatic setting (UMS) is obscured by the complex interplay of surrounding morphology, micro-climate and inter-building energy dynamics. Conventional urban building energy modelling (UBEM) approaches to provide quantitative insights about building energy consumption often neglect the synergistic impacts of micro-climate and urban morphology in short temporal scale. Reduced order modelling, unavailability of rich urban datasets such as building key performance indicators for building archetypes-characterization, limit the inter-building energy dynamics consideration into UBEMs. In addition, mismatch of resolutions of spatio–temporal datasets (meso to micro scale transition), LPHI events extent prediction around UMS as well as its accurate quantitative inclusion in UBEM input organization step pose another degree of limitations. This review aims to direct attention towards an integrated-UBEM (i-UBEM) framework to capture the building load fluctuation over multi-scale spatio–temporal scenario. It highlights usage of emerging data-driven hybrid approaches, after systematically analysing developments and limitations of recent physical, data-driven artificial intelligence and machine learning (AI-ML) based modelling approaches. It also discusses the potential integration of google earth engine (GEE)-cloud computing platform in UBEM input organization step to (i) map the land surface temperature (LST) data (quantitative attribute implying LPHI event occurrence), (ii) manage and pre-process high-resolution spatio–temporal UBEM input-datasets. Further the potential of digital twin, central structed data models to integrate along UBEM workflow to reduce uncertainties related to building archetype characterizations is explored. It has also found that a trade-off between high-fidelity baseline simulation models and computationally efficient platform support or co-simulation platform integration is essential to capture LPHI induced inter-building energy dynamics.

A poorly calibrated model undermines confidence in the effectiveness of building energy simulation, impeding the widespread application of advanced energy conservation measures (ECMs). Striking a balance between information-gathering efforts and achieving sufficient model credibility is crucial but often obscured by ambiguities. To address this gap, we model and calibrate a test bed with different levels of information (LOI). Beginning with an initial model based on building geometry (LOI 1), we progressively introduce additional information, including nameplate information (LOI 2), envelope conductivity (LOI 3), zone infiltration rate (LOI 4), AHU fan power (LOI 5), and HVAC data (LOI 6). The models are evaluated for accuracy, consistency, and the robustness of their predictions. Our results indicate that adding more information for calibration leads to improved data fit. However, this improvement is not uniform across all observed outputs due to identifiability issues. Furthermore, for energy-saving analysis, adding more information can significantly affect the projected energy savings by up to two times. Nevertheless, for ECM ranking, models that did not meet ASHRAE 14 accuracy thresholds can yield correct retrofit decisions. These findings underscore equifinality in modeling complex building systems. Clearly, predictive accuracy is not synonymous with model credibility. Therefore, to balance efforts in information-gathering and model reliability, it is crucial to (1) determine the minimum level of information required for calibration compatible with its intended purpose and (2) calibrate models with information closely linked to all outputs of interest, particularly when simultaneous accuracy for multiple outputs is necessary.

Severe urban heat, a prevalent climate change consequence, endangers city residents globally. Vegetation-based mitigation strategies are commonly employed to address this issue. However, the Middle East and North Africa are under investigated in terms of heat mitigation, despite being one of the regions most vulnerable to climate change. This study assesses the feasibility and climatic implications of wide-scale implementation of green infrastructure (GI) for heat mitigation in Riyadh, Saudi Arabia—a representative desert city characterized by low vegetation coverage, severe summer heat, and drought. Weather research forecasting model (WRF) is used to simulate GI cooling measures in Riyadh’s summer condition, including measures of increasing vegetation coverage up to 60%, considering irrigation and vegetation types (tall/short). In Riyadh, without irrigation, increasing GI fails to cool the city and can even lead to warming (0.1 to 0.3 °C). Despite irrigation, Riyadh’s overall GI cooling effect is 50% lower than GI cooling expectations based on literature meta-analyses, in terms of average peak hour temperature reduction. The study highlights that increased irrigation substantially raises the rate of direct soil evaporation, reducing the proportion of irrigation water used for transpiration and thus diminishing efficiency. Concurrently, water resource management must be tailored to these specific considerations.

This study develops an approach consisting of a stacking model integrated with a multi-objective optimisation algorithm aimed at predicting and optimising the ecological performance of buildings. The integrated model consists of five base models and a meta-model, which significantly improves the prediction performance. Specifically, the R² value was improved by 9.19% and the error metrics MAE, MSE, MAPE, and CVRMSE were reduced by 69.47%, 79.88%, 67.32%, and 57.02%, respectively, compared to the single prediction model. According to the research on interpretable machine learning, adding the SHAP value gives us a deeper understanding of the impact of each architectural design parameter on the performance. In the multi-objective optimisation part, we used the NSGA-III algorithm to successfully improve the energy efficiency, daylight utilisation and thermal comfort of the building. Specifically, the optimal design solution reduces the energy use intensity by 31.6 kWh/m², improves the useful daylight index by 39%, and modulated the thermal comfort index, resulting in a decrement of 0.69 °C for the summer season and an enhancement of 0.64 °C for the winter season, respectively. Overall, this study provides building designers and decision makers with a tool to make better design decisions at an early stage to achieve a better combination of energy efficiency, daylight utilisation and thermal comfort optimisation in an integrated manner, providing an important support for achieving sustainable building design.