Dataset Information

Multi-scale modelling to evaluate building energy consumption at the neighbourhood scale.

ABSTRACT: A new methodology is proposed to couple a meteorological model with a building energy use model. The aim of such a coupling is to improve the boundary conditions of both models with no significant increase in computational time. In the present case, the Canopy Interface Model (CIM) is coupled with CitySim. CitySim provides the geometrical characteristics to CIM, which then calculates a high resolution profile of the meteorological variables. These are in turn used by CitySim to calculate the energy flows in an urban district. We have conducted a series of experiments on the EPFL campus in Lausanne, Switzerland, to show the effectiveness of the coupling strategy. First, measured data from the campus for the year 2015 are used to force CIM and to evaluate its aptitude to reproduce high resolution vertical profiles. Second, we compare the use of local climatic data and data from a meteorological station located outside the urban area, in an evaluation of energy use. In both experiments, we demonstrate the importance of using in building energy software, meteorological variables that account for the urban microclimate. Furthermore, we also show that some building and urban forms are more sensitive to the local environment.

SUBMITTER: Mauree D

PROVIDER: S-EPMC5589157 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Multi-scale modelling to evaluate building energy consumption at the neighbourhood scale.

Mauree Dasaraden D Coccolo Silvia S Kaempf Jérôme J Scartezzini Jean-Louis JL

PloS one 20170907 9

A new methodology is proposed to couple a meteorological model with a building energy use model. The aim of such a coupling is to improve the boundary conditions of both models with no significant increase in computational time. In the present case, the Canopy Interface Model (CIM) is coupled with CitySim. CitySim provides the geometrical characteristics to CIM, which then calculates a high resolution profile of the meteorological variables. These are in turn used by CitySim to calculate the ene ...[more]

PMID: 28880883

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Project description:BACKGROUND:The association between the residential fast food environment and diet has gained growing attention. However, why the food environment affects food consumption is under-examined. This study aimed to investigate neighbourhood social norms with respect to fast food consumption as a potential mediating pathway between residential fast food outlet exposure and residents' fast food consumption. METHODS:A correlational study was conducted in which a nationwide sample of 1038 respondents living across The Netherlands completed a survey. Respondents reported their fast food consumption (amount/week) as well as perceived descriptive and injunctive norms regarding fast food consumption in their neighbourhood. Fast food outlet exposure was measured by the average count of fast food outlets within a 400?m walking distance buffer around the zip-codes of the respondents, using a retail outlet database. Regression models were used to assess associations between residential fast food outlet exposure, fast food consumption, and social norm perceptions, and a bootstrapping procedure was used to test the indirect -mediation- effect. Separate analyses were performed for descriptive norms and injunctive norms. RESULTS:There was no overall or direct association between residential fast food outlet exposure and residents' fast food consumption. However, fast food outlet exposure was positively associated with neighbourhood social norms (descriptive and injunctive) regarding fast food consumption, which in turn were positively associated with the odds of consuming fast food. Moreover, results of the bootstrapped analysis provided evidence of indirect effects of fast food outlet exposure on fast food consumption, via descriptive norms and injunctive norms. CONCLUSIONS:In neighbourhoods with more fast food outlets, residents were more likely to perceive fast food consumption in the neighbourhood as more common and appropriate. In turn, stronger neighbourhood social norms were associated with higher fast food consumption. Acknowledging the correlational design, this study is the first that implies that neighbourhood social norms may be a mediating pathway in the relation between the residential fast food environment and fast food consumption. Future research may examine the role of neighbourhood social norms in other contexts and explore how the changing food environment may shift our consumption norms.

Project description:Commercial buildings account for one third of the total electricity consumption in the United States and a significant amount of this energy is wasted. Therefore, there is a need for "virtual" energy audits, to identify energy inefficiencies and their associated savings opportunities using methods that can be non-intrusive and automated for application to large populations of buildings. Here we demonstrate virtual energy audits applied to large populations of buildings' time-series smart-meter data using a systematic approach and a fully automated Building Energy Analytics (BEA) Pipeline that unifies, cleans, stores and analyzes building energy datasets in a non-relational data warehouse for efficient insights and results. This BEA pipeline is based on a custom compute job scheduler for a high performance computing cluster to enable parallel processing of Slurm jobs. Within the analytics pipeline, we introduced a data qualification tool that enhances data quality by fixing common errors, while also detecting abnormalities in a building's daily operation using hierarchical clustering. We analyze the HVAC scheduling of a population of 816 buildings, using this analytics pipeline, as part of a cross-sectional study. With our approach, this sample of 816 buildings is improved in data quality and is efficiently analyzed in 34 minutes, which is 85 times faster than the time taken by a sequential processing. The analytical results for the HVAC operational hours of these buildings show that among 10 building use types, food sales buildings with 17.75 hours of daily HVAC cooling operation are decent targets for HVAC savings. Overall, this analytics pipeline enables the identification of statistically significant results from population based studies of large numbers of building energy time-series datasets with robust results. These types of BEA studies can explore numerous factors impacting building energy efficiency and virtual building energy audits. This approach enables a new generation of data-driven buildings energy analysis at scale.

Dataset Information

Multi-scale modelling to evaluate building energy consumption at the neighbourhood scale.

Publications

Multi-scale modelling to evaluate building energy consumption at the neighbourhood scale.

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