This manuscript presents an early-design methodology that identifies actionable design goals that maximize the effectiveness of natural ventilation in hybrid buildings. These design goals are presented using a compactness factor, recommended thermal mass area and target air change rates per hour (ACH) to maintain thermal comfort preferences of occupants. This approach will enable the consultant to explore viable design options that satisfy the identified design goals, such as optimum opening size based on the target ACH when wind and buoyancy ventilation forces are available, or incorporating fanassisted ventilation when natural ventilation is not sufficient. The method implements a single-node transient, analytical model using non-geometric mathematical representations of building parameters defined in a python routine.
This dataset contains key characteristics about the data described in the Data Descriptor The Scales Project, a cross-national dataset on the interpretation of thermal perception scales. Contents: 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format
Versioning Note:Version 2 was generated when the metadata format was updated from JSON to JSON-LD. This was an automatic process that changed only the format, not the contents, of the metadata.
This paper presents an urban analysis work flow using a Rhinoceros/Grasshopper massing tool. The tool utilizes terrain elevation models as part of the design process to subdivide sites and generate urban form to be explored parametrically. It can then be linked to various performance assessment methods. As a proof of concept, the study uses a walkability calculator for three urban form alternatives, and applies genetic algorithms to optimize generated designs through allocation of land-use. Results show a great diversity that converges to near optimal solutions. A discussion is drawn about the effort and time spent to model such iterations versus it’s automation using this work flow, and conclusions show the potentials, limitations and directions for future research work.
Measuring window to wall ratios (WWRs) is key to assessing building performance as façade apertures control the admission of light, wind and heat. However, this data is not always publicly available. This paper details a methodology for automatically extracting and rectifying street-view facade imagery while utilizing a Machine Learning model to detect WWRs with architectural generalization in mind. Although several models of detection have emerged to categorize façade features, some lack robustness when presented with greater design diversity. Hence, the training and validation process of the Convolutional Neural Network (CNN) model utilized is centered around three main data categories; environmental conditions, design diversity and context. The results show that the proposed workflow sufficiently represents the WWRs of buildings in an area in Lisbon under varied design conditions. We find that the distribution of prediction accuracy, tested on 864 facades, shows that 72% of buildings are detected within the 10% error range.
The solar envelope is a three-dimensional volume on a building site which will not shade adjacent neighbors during a specified minimum of hours each day. The solar envelope was developed as a tool to give buildings in an urban setting the mutual opportunity to employ passive and active solar-design strategies. Parametric computer-aided-design environments significantly ease the construction and visualization of solar envelopes across whole neighborhoods, facilitating its wider use as a prescriptive zoning tool. This study investigates the implications of a solar-envelope zoning approach for the most common building type in the United States with respect to energy use and developable density. The results indicate that solar zoning for this building type has a limited, and sometimes negative effect on energy use as well as a larger negative impact on developable density.
As global temperatures may increase due to climate change, so would energy use for building heating and cooling. Additionally, energy prices fluctuate in relation to climate change socio-economic impacts and related policies, which in turn further influence future building operational costs. A new method is presented to consider climate change and future energy price scenarios for institutional building owners to compare investment options for various energy conservation measures related to heating and cooling. Heating and cooling energy use are predicted for office buildings in Boston (Massachusetts) and Phoenix (Arizona) using a range of different climate change and energy price scenarios. The results allow building owners (1) to understand building performance under a spectrum of possible futures, and (2) to determine the associated risk ranges present when choosing between design solutions. For the example buildings, it is shown that a financial payback of 15 years is possible for an advanced retrofitting strategy in Phoenix; this compares favourably with the 18-year payback calculated while ignoring climate change. Cumulative energy costs over 70 years and peak loads by 2050 are predicted to more than double for the Phoenix baseline model, whereas the advanced design all but eliminates that cost increase. Dès lors que les températures mondiales augmenteraient en raison du changement climatique, il en irait de même de la consommation dénergie pour le chauffage et le refroidissement des immeubles. En outre, les prix de lénergie fluctuent en fonction des incidences socioéconomiques du changement climatique et des politiques connexes, qui à leur tour influent davantage encore sur les coûts dexploitation futurs des immeubles. Une nouvelle méthode est présentée pour prendre en compte les scénarios relatifs au changement climatique et aux prix futurs de lénergie afin que les propriétaires institutionnels dimmeubles puissent comparer les options dinvestissement en fonction des différentes mesures déconomie dénergie relatives au chauffage et au refroidissement. Des prévisions sont établies concernant la consommation dénergie pour le chauffage et le refroidissement dans des immeubles de bureaux situés à Boston (Massachusetts) et à Phoenix (Arizona) en utilisant un éventail de scénarios différents en matière de changement climatique et de prix de lénergie. Les résultats permettent aux propriétaires dimmeubles (1) de comprendre les performances de leurs immeubles en fonction dun éventail de futurs possibles, et (2) de déterminer les plages de risques associés présentes lorsquils ont à choisir entre les différentes solutions de conception. Sagissant des immeubles pris en exemple, il est montré quun retour sur investissement financier de 15 ans est possible avec une stratégie de rénovation avancée à Phoenix; cela soutient favorablement la comparaison avec le retour sur investissement de 18 ans calculé en faisant abstraction du changement climatique. Il est prévu que les coûts énergétiques cumulés sur 70 ans et les pointes de consommation dici à 2050 feront plus que doubler pour le modèle de base de Phoenix, alors que la conception avancée éliminera pratiquement cette hausse des coûts. Mots clés: économie du bâtiment, performances des bâtiments, rénovations dimmeubles, changement climatique, modélisation énergétique, options réelles, incertitude
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