Building Simulation最新文献

Performance analysis during the early design stage can significantly reduce building energy consumption. However, it is difficult to transform computer-aided design (CAD) models into building energy models (BEM) to optimize building performance. The model structures for CAD and BEM are divergent. In this study, geometry transformation methods was implemented in BES tools for the early design stage, including auto space generation (ASG) method based on closed contour recognition (CCR) and space boundary topology calculation method. The program is developed based on modeling tools SketchUp to support the CAD format (like *.stl, *.dwg, *.ifc, etc.). It transforms face-based geometric information into a zone-based tree structure model that meets the geometric requirements of a single-zone BES combined with the other thermal parameter inputs of the elements. In addition, this study provided a space topology calculation method based on a single-zone BEM output. The program was developed based on the SketchUp modeling tool to support additional CAD formats (such as *.stl, *.dwg, *.ifc), which can then be imported and transformed into *.obj. Compared to current methods mostly focused on BIM-BEM transformation, this method can ensure more modeling flexibility. The method was integrated into a performance analysis tool termed MOOSAS and compared with the current version of the transformation program. They were tested on a dataset comprising 36 conceptual models without partitions and six real cases with detailed partitions. It ensures a transformation rate of two times in any bad model condition and costs only 1/5 of the time required to calculate each room compared to the previous version.

Building-Integrated photovoltaics (BIPV) have emerged as a promising sustainable energy solution, relying on accurate energy production predictions and effective decarbonization strategies for efficient deployment. This paper presents a novel approach that combines photogrammetry and deep learning techniques to address the problem of BIPV decarbonization. The method is called BIM-AITIZATION referring to the integration of BIM data, AI techniques, and automation principles. It integrates photogrammetric data into practical BIM parameters. In addition, it enhances the precision and reliability of PV energy prediction by using artificial intelligence strategies. The primary aim of this approach is to offer advanced, data-driven energy forecasts and BIPV decarbonization while fully automating the underlying process. To achieve this, the first step is to capture point cloud data of the building through photogrammetric acquisition. This data undergoes preprocessing to identify and remove unwanted points, followed by plan segmentation to extract the plan facade. After that, a meteorological dataset is assembled, incorporating various attributes that influence energy production, including solar irradiance parameters as well as BIM parameters. Finally, machine and deep learning techniques are used for accurate photovoltaic energy predictions and the automation of the entire process. Extensive experiments are conducted, including multiple tests aimed at assessing the performance of diverse machine learning models. The objective is to identify the most suitable model for our specific application. Furthermore, a comparative analysis is undertaken, comparing the performance of the proposed model against that of various established BIPV software tools. The outcomes reveal that the proposed approach surpasses existing software solutions in both accuracy and precision. To extend its applicability, the approach is evaluated using a building case study, demonstrating its ability to generalize effectively to new building data.

The global concern over indoor air pollution in public vehicles has grown significantly. With a focus on enhancing passengers’ comfort and health, this study endeavors to investigate the distribution characteristics of formaldehyde within a high-speed train cabin by employing a computational fluid dynamics (CFD) model which is experimentally validated in a real cabin scenario. The research focuses on analyzing the impact of air supply modes, temperature, relative humidity, and fresh air change rate on the distribution and concentration of formaldehyde. The results demonstrate that the difference in average formaldehyde concentration between the two air supply modes is below 1.3%, but the top air supply mode leads to a higher accumulation of formaldehyde near the sidewalls, while the bottom air supply mode promotes a more uniform distribution of formaldehyde. Furthermore, the temperature, relative humidity, and fresh air change rate are the primary factors affecting formaldehyde concentration levels, but they have modest effects on formaldehyde’s distribution pattern within the cabin. As the temperature and relative humidity increase, the changes in formaldehyde concentrations in response to variations in these factors become more evident. Importantly, the formaldehyde concentration may surpass the standard limit of 0.10 mg/m³ if the fresh air change rate falls below 212 m³/h. This research provides a systematic approach and referenceable results for exploring formaldehyde pollution in high-speed train cabins.

Climate-responsive building design holds immense potential for enhancing comfort, energy efficiency, and environmental sustainability. However, many social, cultural, and economic obstacles might prevent the wide adoption of designing climate-adapted buildings. One of these obstacles can be removed by enabling practitioners to easily access, visualize and analyze local climate data. The CBE Clima Tool (Clima) is a free and open-source web application that offers easy access to publicly available weather files and has been created for building energy simulation and design. It provides a series of interactive visualizations of the variables contained in the EnergyPlus Weather Files and several derived ones like the UTCI or the adaptive comfort indices. It is aimed at students, educators, and practitioners in the architecture and engineering fields. Since its inception, Clima’s user base has exhibited robust growth, attracting over 25,000 unique users annually from across 70 countries. Our tool is poised to revolutionize climate-adaptive building design, transcending geographical boundaries and fostering innovation in the architecture and engineering fields.

Non-intrusive load monitoring (NILM) is a technique which extracts individual appliance consumption and operation state change information from the aggregate power consumption made by a single residential or commercial unit. NILM plays a pivotal role in modernizing building energy management by disaggregating total energy consumption into individual appliance-level insights. This enables informed decision-making, energy optimization, and cost reduction. However, NILM encounters substantial challenges like signal noise, data availability, and data privacy concerns, necessitating advanced algorithms and robust methodologies to ensure accurate and secure energy disaggregation in real-world scenarios. Deep learning techniques have recently shown some promising results in NILM research, but training these neural networks requires significant labeled data. Obtaining initial sets of labeled data for the research by installing smart meters at the end of consumers’ appliances is laborious and expensive and exposes users to severe privacy risks. It is also important to mention that most NILM research uses empirical observations instead of proper mathematical approaches to obtain the threshold value for determining appliance operation states (On/Off) from their respective energy consumption value. This paper proposes a novel semi-supervised multilabel deep learning technique based on temporal convolutional network (TCN) and long short-term memory (LSTM) for classifying appliance operation states from labeled and unlabeled data. The two thresholding techniques, namely Middle-Point Thresholding and Variance-Sensitive Thresholding, which are needed to derive the threshold values for determining appliance operation states, are also compared thoroughly. The superiority of the proposed model, along with finding the appliance states through the Middle-Point Thresholding method, is demonstrated through 15% improved overall improved F1_micro score and almost 26% improved Hamming loss, F1 and Specificity score for the performance of individual appliance when compared to the benchmarking techniques that also used semi-supervised learning approach.

It has been found in recent years that using setpoint temperatures based on adaptive thermal comfort models is a successful method of energy conservation. Recent studies using adaptive setpoint temperatures incorporate international models from ASHRAE Standard 55 and EN16798-1. This study, however, has instead considered a regional Brazilian adaptive comfort model. This study investigates the energy demand arising from the use of a local Brazilian comfort model in order to assess the energy implications from the use of the worldwide ASHRAE Standard 55 adaptive model and various fixed setpoint temperatures. All of Brazil’s climate zones, full air-conditioning, mixed-mode building operating modes, present-day climate change scenarios, and future scenarios—specifically Representative Concentration Pathways (RCP) 2.6, 4.5, and 8.5 for the years 2050 and 2100—have all been taken into account in building energy simulations. The use of adaptive setpoint temperatures based on the Brazilian local model considering mixed-mode has been found to significantly reduce energy consumption when compared to static setpoint temperatures (average energy-saving values ranging from 52% to 58%) and the ASHRAE 55 adaptive model (average values ranging from 15% to 21%). Considering climate change and the mixed-mode Brazilian model, the overall energy demand for the three groups of climatic zones (annual average outdoor temperatures ≤ 21 °C, > 21 and ≤ 25 °C and > 25 °C) ranged between 2% decrease and 5% increase, 4% and 27% increase, and 13% and 45% increase, respectively. It is concluded as a consequence that setting setpoint temperatures based on the Brazilian local adaptive comfort model is a very efficient energy-saving method.

The energy consumption of heating, ventilation, and air conditioning (HVAC) systems holds a significant position in building energy usage, accounting for about 65% of the total energy consumption. Moreover, with the advancement of building automation, the energy consumption of ventilation systems continues to grow. This study focuses on improving the performance of spherical tuyeres in HVAC systems. It primarily utilizes neural networks and multi-island genetic algorithms (MIGA) for multi-parameter optimization. By employing methods such as structural parameterization, accurate and fast computational fluid dynamics (CFD) simulations, a minimized sample space, and a rational optimization strategy, the time cycle of the optimization process is shortened. Additionally, a new comprehensive evaluation index is proposed in this research to describe the performance of spherical tuyeres, which can be used to more accurately assess spherical tuyeres with different structures. The results show that by establishing a neural network prediction model and combining it with the multi-island genetic algorithm, a novel spherical tuyere design was successfully achieved. The optimized novel spherical tuyeres achieved a 27.05% reduction in the spherical tuyeres effective index (STEI) compared to the traditional spherical tuyeres. Moreover, the resistance decreased by 15.68%, and the jet length increased by 7.57%. The experimental results demonstrate that our proposed optimization method exhibits high accuracy, good generalization capability, and excellent agreement at different Reynolds numbers.

The cabin air pressure remains lower than the horizontal atmospheric pressure when the airplane is in flight. Air pressure is one of the parameters that must be taken into consideration while studying the thermal environment of an airplane cabin. There are still no reference values for aircraft cabins despite the fact that numerous studies on low pressure heat transfer have demonstrated the connection between convective heat transfer coefficient (CHTC) and air pressure. In this paper, a correction method for CHTC under low pressure conditions was established by using the dummy heat dissipation in the low-pressure cabin experiment. On this basis, a thermal environment simulation model was developed, then was applied to the simulation of a seven-row aircraft cabin containing 42 passengers, and the CHTC and heat loss of dummy surface in the cabin were obtained. Finally, the results of PMV calculated by using heat dissipation and air parameters at sampling points were compared. The results show that the modified CHTC can accurately reflect the cabin thermal environment under low pressure conditions, and the correction of CHTC can be realized by adjusting the turbulent Prandtl number, which is nonlinear correlated with the pressure. The simulation results of the thermal environment in the seven-row cabin show that the CHTC changes by about 42% before and after modification. The air pressure decreases during take-off, which reduces the average CHTC of the crew surface from 5.09 W/(m²·K) to 4.56 W/(m²·K), but the air temperature rises by about 0.2 °C as a whole. The deviation of PMV results calculated by using simulated heat loss data and using air parameters of measuring points in space is up to 0.5, but the latter is representative for calculating the thermal comfort level of the whole cabin.