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.

A good skylight environment in urban residential areas is an important component of a healthy city, and has always been highly valued. With the rapid development of new-type urbanization, the density of buildings continues to increase, and megacities have entered the stage of stock transformation. An effective method for evaluating the skylight environment of large-scale urban residential areas is urgently needed. However, there is still a lack of empirical research methods and cases of large-scale residential skylight environment. In this regard, this article takes the megacity Beijing as the research object, and proposes an efficient analysis method of residential skylight environment that integrates multiple real-world data at city scale. In terms of data, it collects and integrates 3D data of urban-scale building space and residential boundary data; in terms of algorithm, Sky View Factor (SVF) is used as the evaluation index of residential skylight environment, and an efficient analysis method of urban-scale skylight environment based on cloud parallel simulation is realized. Through analysis, it is found that: (1) the average SVF value of Beijing residential area is 61%, which means that its skylight quality is in general level; (2) the skylight environment of Beijing residential area is distributed in a circle, and there are 4 types of skylight environment quality residential areas; (3) The skylight environment of Beijing residential area is relatively weakly related to the distance from the residential area to the city center and the average height of the residential buildings, and is closely related to the plot volume ratio, the residential building density and the shading from surrounding buildings. The highlight of this study lies in the empirical research on the skylight environment of mega-city residential areas that incorporates multiple real data for the first time, which can promote the study of skylight environment on a city scale and provide a reference for the updating of Beijing's residential daylight environment.

Curved shapes are increasingly used in buildings recently, which not only enriches the appearance of buildings, but also provides new possibilities of improving building performance by shape design. However, existing research relating building performance with building shapes focuses mostly on regular shapes; the effects of curved shapes on building performance should be better addressed. This paper aims to implement design optimization for curved shapes and to explore the performance improvement that they can contribute. Specifically, the improvements in daylight efficiency of office buildings by optimized curved facades are investigated. A typical office building with a curved facade is parametrically modeled in Rhinoceros, simulated in daylight by DIVA, and optimized by Galapagos to maximize its area-weighted average UDI. 20 optimizations are conducted, with 3 levels of geometrical complexity, 3 locations and 4 orientations. The results prove that optimized curved facades can significantly improve the daylight efficiency of office buildings, with improvements in area-weighted average UDI as high as 0.4376. Larger improvements can be achieved by curved facades with higher geometrical complexity, while the growth trend slows as the complexity increases. The improvements are also influenced by locations and orientations. Moreover, optimization for the best daylight efficiency can be a feasible method for finding novel curved shapes for architecture design. It is also found that the mechanism of the improvements is that the optimized curved facades reduce the time of daylight oversupply, although the side effect is the occurrence of uneven daylight distribution.

How to control the growth of building energy consumption and achieve the goal of energy saving and emission reduction while ensuring people’s growing demand for indoor comfort is of great practical significance in the new era. The rapid and accurate prediction of the building energy consumption at the early design stage can provide a quantitative basis for the energy-saving design. ANN (artificial neural network) model is the most widely used artificial intelligence model in the field of building performance optimization due to its high speed, high accuracy, and capability of handling nonlinear relationships between variables. In this paper, an ANN-based fast building energy consumption prediction method for complex architectural form for the early design stage was proposed. Under this method, the authors proposed an idea of architectural form decomposition, to eliminate the complexity of building shape at the early design stage, thus transforming the energy consumption prediction problem of one complex architectural form into several energy consumption prediction problems of multiple simple blocks: the method of characterization decomposition (MCD) and the method of spatial homogenization decomposition (MSHD). The ANN model was introduced to realize energy consumption prediction, which fully utilized the two advantages: high speed and good response to complicated relationships. Accuracy verification shows that the relative deviation of cooling and heating energy consumption is within ±10% using the MCD method. The relative deviation of total energy consumption is within 10% using the MSHD method.

This paper presents a graph- and feature-based building space recognition algorithm for a boundary representation format (B-rep) geometric model, which can identify the building element type and space. The flow of the algorithm is described in detail, including the construction of a building geometric topology relation graph (BTG), the recognition of building element type, and the extraction of building space based on graph and local feature. The algorithm can be applied to the design of a building scheme; it can quickly identify and transform the geometric model into the input model required by the performance simulation software. This is a key step in realizing a performance-oriented design in the early design stage. We implemented this algorithm using SketchUp for testing its performance. Through the case study, it is proved that the algorithm can recognize the model and extract all the building spaces accurately. There is linear correlation between the recognition time and number of faces. Moreover, at the time of analysis, a model composed of 500 spaces and 3001 faces did not exceed 1.69 s, which meets the requirements of most applications well. Compared to previous works, this algorithm performs well in both recognition accuracy and time efficiency simultaneously, and can better serve the actual demand of automatic real-time building performance feedback in the early design stage. Finally, the future work regarding performance-oriented design based on model recognition is proposed.

Citizens could enjoy a healthy and comfortable living environment if outdoor thermal comfort and sufficient natural ventilation are available in their dwellings. In this paper, numerical studies were performed with the Simulation Platform for Outdoor Thermal Environment (SPOTE) to investigate: (1) the thermal environment and pedestrian thermal comfort of the occupants in the open space with different patterns of the building and green space; (2) the wind pressures on the building facades and the natural ventilation rate of these buildings. The conclusions are summarized as follows: (1) it has been observed that the long facades of building and green space, which are parallel to the prevailing wind direction, can accelerate horizontal vortex airflow at the edges where such airflow could strengthen the convective exchange efficiency of hot air in low altitude and cold air in high altitude, and can obtain thermal comfort and sufficient natural ventilation at the pedestrian level; (2) after a series of simulations and comparisons, the configuration in which buildings are grouped in staggered layout with a centralized green space can provide better ventilation conditions and suitable air movement as a result of attenuated revised standard effective temperature (SET^*). This configuration is regarded as the optimum pattern of the building and green space.

Vegetation has positive effects on the outdoor pedestrian comfort and thermal environment. Studied with He Qing Yuan district in Beijing, an investigation of the optimal landscape design of trees in residential buildings at northern China is carried out for good sunshine and comfortable wind environment. Firstly, in consideration of the legal planning requirement for basic sunshine hour control in winter for house, geometrical models for trees and buildings are built and analyzed by AutoCAD and Sketch-Up software to determine reasonable tree location between buildings, suitable heights and crown shapes. Secondly, aimed at comfortable wind environment inside the residential district, optimal arrangement of trees has been studied with numerical simulation by SPOTE (simulation platform for outdoor thermal environment), where the air velocity lower than 5 m/s is introduced as the aims of scheme optimization. With geometrical analysis and numerical simulation and comparison, the tree types and layout in green space between buildings were optimized.