Building Simulation最新文献

Urban block form significantly impacts energy and environmental performance. Therefore, optimizing urban block design in the early stages contributes to enhancing urban energy efficiency and environmental sustainability. However, widely used multi-objective optimization methods based on performance simulation face the challenges of high computational loads and low efficiency. This study introduces a framework using machine learning, especially the XGBoost model, to accelerate multi-objective optimization of energy-efficient urban block forms. A residential block in Nanjing serves as the case study. The framework commences with a parametric block form model driven by design variables, focusing on minimizing building energy consumption (EUI), maximizing photovoltaic energy generation (PVE) and outdoor sunlight hours (SH). Data generated through Latin Hypercube Sampling and performance simulations inform the model training. Through training and hyperparameter tuning, XGBoost’s predictive accuracy was validated against artificial neural network (ANN), support vector machine (SVM), and random forest (RF) models. Subsequently, XGBoost replaced traditional performance simulations, conducting multi-objective optimization via the NSGA-II algorithm. Results showcase the framework’s significant acceleration of the optimization process, improving computational efficiency by over 420 times and producing 185 Pareto optimal solutions with improved performance metrics. SHAP analysis highlighted shape factor (SF), building density (BD), and building orientation (BO) as key morphological parameters influencing EUI, PVE, and SH. This study presents an efficient approach to energy-efficient urban block design, contributing valuable insights for sustainable urban development.

Computational performance-driven design optimization (CPDDO) informs early building design decisions, enhancing projects’ responsiveness to local climates. This paper reviews recent CPDDO studies, identifies prevalent gaps, and proposes a refined optimization framework. The framework stands out by: (1) integrating view quality alongside energy, daylight, and thermal comfort considerations, with a vector-simulation-based metric considering content, access and clarity; (2) incorporating users’ adaptive behavior patterns in simulations; and (3) employing a hybrid weighting method to accommodate diverse project demands and support robust design decisions. This study applies the framework to optimize the shape and facade variables of a medium-sized office building in Guangzhou, Chongqing, Qingdao, Lanzhou, and Changchun, representing hot, warm, mixed, cool, and cold climates, respectively. Results highlight that geometry features (aspect ratio, orientation, window-to-wall ratio (WWR), and shading devices), as well as window and blinds constructions significantly impact energy, daylight, thermal comfort and view quality. Different climatic conditions, objective priorities, and facade orientations necessitate tailored design variables. Furthermore, certain findings challenge conventional recommendations; for instance, buildings in colder climates benefit from increased WWR, due to enhanced potential to harness solar radiation and improved view access, while high-performance envelope thermal settings mitigate heat transfer. These findings underscore the need for detailed, targeted research in early-stage design. The developed CPDDO framework proves effective and user-friendly, offering new possibilities for optimizing building performance, thus holds the potential to foster green, comfortable, and sustainable architecture in various practical applications.

Energy simulation is a valuable tool for evaluating and improving the thermal performance and energy efficiency of buildings during the design phase. Common evaluation methods are thermal load (TL), degree-hour (DH), and design days (DD). The choice of method and its settings may vary depending on regional factors and researchers’ preferences, leading to diverse and often incompatible metrics and results. Therefore, this study aims to investigate the influence of these evaluation methods on the assessment of buildings’ performance and, consequently, on design choices. For this purpose, this study compared the results of the 3 evaluation methods and different settings for 3 different wall systems, 4 ranges of comfort temperature, and 2 residential models located in the 8 Brazilian bioclimatic zones. As result, the best and worst wall systems varied depending on the evaluation method and the threshold / setpoint temperature range considered. Warmer regions showed greater variability in the results. We noticed that it is not possible to compare and interpret results from different evaluation methods, and that the variation of only 1 °C in the setpoint temperatures can lead to entirely different practices being considered the best for a given building model. In conclusion, the most suitable evaluation method is the one that best portrays the operation and dynamic reality of the building to be designed, and building regulations and standards can occasionally lead to unrealistic assessments.

U-type medium-deep borehole heat exchanger (U-MDBHE) is a sustainable building heating technology. Current studies assess the long-term thermal performance of U-MDBHE using typical meteorological year weather data. The conclusions indicate a discernible deterioration in the thermal performance of U-MDBHE attributed to heat extraction attenuation. The thermal performance deterioration leads to the oversize of U-MDBHE and hinders the widespread application of U-MDBHE. This study introduces a novel idea that the long-term thermal performance of U-MDBHE should be evaluated considering climate change (CC) and verifies that the favorable effects of CC on the thermal performance of U-MDBHE can effectively mitigate the adverse effect of heat extraction attenuation. The favorable effects of CC include reducing the heating demand (due to the reduced building heating load (BHL) caused by CC) and improving the heating supply capacity (due to the enhanced outlet temperature caused by CC). In addition, the reduced BHL under CC enhances the inlet temperature of U-MDBHE, thereby improving its operation safety. CC mitigates the heat extraction attenuation of U-MDBHE, with the strongest effect in the ascending well, followed by the descending well, and then the butted well. Case studies using experimentally validated simulations on the 30-year operation of U-MDBHE demonstrate that by mitigating the adverse effect of the heat extraction attenuation, CC reduces the accumulated energy consumption by 14.31%–26.59% and improves the operation safety by up to 100% in Harbin (severe cold region) and Beijing (cold region). This study significantly contributes to improving the long-term thermal performance of U-MDBHE.

Accurate and rapid predictions of residential building performance are crucial for both new building designs and existing building renovations. This study develops an integrated prediction model using a stacking ensemble learning algorithm to predict daylighting, thermal comfort, and energy consumption in residential buildings. The model incorporates multimodal residential building information as inputs, including image-based floorplans and vector-based building parameters. A comparative analysis is presented to evaluate the prediction performance of the proposed stacking ensemble learning algorithm against three base models: Resnet-50, Inception-V4, and Vision Transformer (ViT-32). The results indicated that the stacking ensemble learning algorithm outperforms the base models, reducing the mean absolute percentage error (MAPE) by 0.17%–1.94% and the coefficient of variation root mean square error (CV-RMSE) by 0.37%–2.06% for daylighting metrics; the MAPE by 0.63%–4.46% and the CV-RMSE by 0.62%–5.13% for thermal comfort metrics; the MAPE by 1.42%–6.43% and the CV-RMSE by 0.27%–5.09% for energy consumption metrics of the testing dataset. Further prediction error analyses also indicate that the stacking ensemble learning algorithm consistently yields smaller prediction errors across all performance metrics compared to the three base models. In addition, this study compares the stacking ensemble learning algorithm to traditional machine learning models in terms of prediction accuracy, robustness, and generalization ability, highlighting the advantages of the stacking ensemble learning algorithm with image-based inputs. The proposed stacking ensemble learning algorithm demonstrates superior accuracy, stability, and generalizability, offering valuable and practical design support for building design and renovation processes.

Selective visual attention determines what pedestrians notice and ignore in urban environment. If consistency exists between different individuals’ visual attention, designers can modify design by underlining mechanisms to better meet user needs. However, the mechanism of pedestrians’ visual attention remains poorly understood, and it is challenging to forecast which position will attract pedestrians more in urban environment. To address this gap, we employed 360° video and immersive virtual reality to simulate walking scenarios and record eye movement in 138 participants. Our findings reveal a remarkable consistency in fixation distribution across individuals, exceeding both chance and orientation bias. One driver of this consistency emerges as a strategy of information maximization, with participants tending to fixate areas of higher local entropy. Additionally, we built the first eye movement dataset for panorama videos of diverse urban walking scenes, and developed a predictive model to forecast pedestrians’ visual attention by supervised deep learning. The predictive model aids designers in better understanding how pedestrians will visually interact with the urban environment during the design phase. The dataset and code of predictive model are available at https://github.com/LiamXie/UrbanVisualAttention

The Chinese solar greenhouse (CSG) is a prevalent feature in agricultural practices within China. Nevertheless, the regulation of natural ventilation within this architectural structure remains suboptimal. Consequently, the development of a natural ventilation model becomes imperative for the effective management of the greenhouse environment. Of particular significance within these models is the consideration of the discharge coefficient as a pivotal parameter. Conducting a multi-case investigation into the variable-dependent discharge coefficient is crucial for both practical application and model advancement. This research delved into the impact of various factors, including the upper-lower vents area ratio (A_up/A_low), vent-greenhouse area ratio (A_low/A_greenhouse), lower vent position height (h/H), the incident angle of the external wind, and altitude, on the discharge coefficient (C_d) of CSG. A CFD model was developed for a scaled CSG with validation conducted through field experiments and wind tunnel tests. Results indicated a 61.6% reduction in C_d on average corresponding to an 80% decrease in A_up/A_low. C_d levels remained consistent following the attainment of an A_up/A_low ratio of 1.0. Besides, there was an average increase of 52.5% in C_d levels for every 0.09 decline in h/H, attributed to the blocking effect of the cover. Moreover, the ventilation rate and the pressure coefficient difference were utilized to construct a model of C_d pertaining to greenhouse design and ventilation operation, exhibiting a notable accuracy level of R² = 0.95. Furthermore, the blocking effect of higher h/H was relieved as the incident angle θ decreased under the windward conditions. The increase in A_up/A_low and the decrease in A_low/A_greenhouse were identified as crucial factors contributing to the growth of C_d under leeward conditions. Ultimately, the high-altitude environment led to a rise in C_d levels in contrast to the low-altitude region. The increasing rate of C_d correlated positively with A_low/A_greenhouse and h/H initially, but exhibited a decline once A_low/A_greenhouse reached 0.036, remaining stable thereafter once h/H reached 0.18. In summary, a comprehensive examination of the discharge coefficient of CSG was undertaken, addressing a significant knowledge deficiency and laying the groundwork for advancements in the natural ventilation model and the intelligent control system for CSG.

Adverse impacts of exposure to formaldehyde on human health significantly increases attention in monitoring formaldehyde concentrations in the air. Conventional formaldehyde detection methods typically rely on large and costly instruments and requires high skills of expertise, preventing it from being widely accessible to civilians. This study introduced a novel approach utilizing smartphone-based colorimetric analysis. Changes of green channel signals of digital images by a smartphone successfully capture variation of purple color of 4-amino-3-hydrazino-5-mercapto-1,2,4-triazol solution, which is proportional to formaldehyde concentrations. It is because that green and purple are complimentary color pairs. A calibration curve was established between green channel signals and formaldehyde concentrations, with a correlation coefficient of 0.98. Detection limit of the smartphone-based method is 0.008 mg/m³. Measurement errors decrease as formaldehyde concentrations increase, with median relative errors of 34%, 17%, and 6% for concentration ranges of 0–0.06 mg/m³, 0.06–0.12 mg/m³, and 0.12–0.35 mg/m³, respectively. This method replaced scientific instrumentation with ordinary items, greatly reducing cost and operation bars. It would provide an opportunity to realize onsite measurements for formaldehyde by occupants themselves and increase awareness of air quality for better health protection.

Building integrated photovoltaic (BIPV) windows impact building performance by balancing daylighting availability, visual comfort, solar power generation, and building energy consumption. Optimizing this balance is crucial for improving overall building energy efficiency and indoor environment quality. This study introduces a novel curved photovoltaic window design aimed at increasing daylight transmittance while maintaining the same photovoltaic area as a flat PV window. The annual daylighting availability, visual comfort and building energy performance of three types of flat/curved PV windows (180°, 120°, 0°-flat) in a reference office room was comparatively studied across five different climate zones in China (Xiamen, Harbin, Nanjing, Kunming, and Beijing). The PV model was validated by the experimental data. The results showed that the room with curved PV windows had significantly higher daylighting availability compared to flat windows, with the growth rates of the spatial useful daylight illuminance ranging of 3.94%–4.78% and 5.56%–5.94%, respectively, for the curved PV windows at central angles of 120° and 180° across different climate zones. The 120° curved PV windows achieved the lowest net energy used intensity (Net_EUI), suggesting the advantages of curved PV windows and proposed the existence of an optimal curvature for achieving the lowest Net_EUI.

Occupant-centric control (OCC) is intelligent control of building systems based on the real comfort needs of occupants. This paper provides a comprehensive review of how real-world data on energy-related occupant behavior (OB) can be integrated and applied in OCC systems. The aim is to accurately portray the real occupant needs and improve energy efficiency without sacrificing occupant comfort. This paper first introduces two types of OB: detailed occupancy states and energy-interaction behaviors, including methods to monitor, establish, and predict these OB. Then, OCC is divided into real-time control and model-based predictive control, and each of these four scenarios is discussed. It extensively reviews OCC methods for different equipment in four cases, covering control strategies, control scales, comfort enhancement scenarios, and energy-saving potential for each category. It is summarized that despite extensive research on OB, there are still significant challenges in integrating this research into OCC. A major issue is the lack of a bridge connecting monitoring acquired information and controls. In addition, the article reviews the current state of OCC platform development. The future direction should be combined with advanced Internet of Things (IoT) technologies, WiFi, and other communication technologies to obtain information about people’s behavior and real needs in order to create truly energy efficient and comfortable smart environments. The article also discusses how enhancing the real-time feedback capability of the OCC system can help improve the overall control system capability and the importance of testing through experimentation.