No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Approach for simulation-based scenario analyses of district energy systems
... However, simplification always results in lower accuracy of the model. Nevertheless different applications already proofed the applicability of the model on urban scale (Fuchs et al. 2015; Schiefelbein et al. 2015). A fourth and optional step in our methodology is the export of the enriched data set into a valid CityGML file, for further use in other software tools. ...
... Sparse data is a typical problem for dynamic simulation on urban scale. Therefore previous work showed the possibility to use archetype buildings defined by a limited number of elementary building parameters to estimate enclosure area and usage zones inside the building (Fuchs et al. 2015; Hillebrand et al. 2014; Schiefelbein et al. 2015). With the statistical data enrichment, TEASER adds physical properties, like wall construction layer by layer to the internal data model (Loga et al. 2005). ...
... A parallel effort to use the Modelica- based framework developed for open Integrated District Energy Assessment by Simulation (OpenIDEAS) by KU Leuven and 3E. This uses building load profiles to optimize district energy using Modelica libraries ( Fuchs et al. 2015, Wetter et al. 2015), integrating physics- based modules of systems in a larger context such as district heating/cooling or shared energy infrastructures ( Baetens et al. 2012, Baetens et al. 2015). CityBES is an open web platform for city building energy efficiency. ...
Many buildings already exist in an urban context, in close proximity to many other buildings of similar or larger size and in micro-climates (e.g., urban canyons and heat islands) created by this environment. As the world's population increasingly flows into cities, many more buildings will fit this description. Cities are collecting new data on basic building features, mutual shading, and microclimate and synthesizing it in GIS platforms and in 3D city models like CityGML. This new data in the urban context impacts building energy performance. Taking advantage of this information to improve urban building energy modeling requires simulation engines to incorporate it into calculations and do so in a scalable way that achieves acceptable computing performance and accuracy for urban scale modeling applications. This paper presents several new features recently implemented in EnergyPlus version 8.8 to improve its accuracy in the urban context and expand its applicability to urban scale building energy modeling. These features include (1) import and export of exterior shading schedules, (2) calculation of long-wave radiant exchange between buildings, and (3) use of urban microclimate conditions. We discuss these features along with simulation examples to demonstrate their use and impact on urban scale building energy modeling.
... Another approach by KU Leuven and 3E uses the Modelica-based framework developed for open Integrated District Energy Assessment by Simulation (OpenIDEAS). This approach employs building load profiles to optimize district energy, leveraging Modelica libraries (Fuchs et al. 2015;Wetter et al. 2015b) and integrating physics-based modules of systems in a larger context such as district heating/cooling or shared energy infrastructures (Baetens et al. 2012(Baetens et al. , 2015. ...
Buildings consume more than one-third of the world’s primary energy. Reducing energy use and greenhouse-gas emissions in the buildings sector through energy conservation and efficiency improvements constitutes a key strategy for achieving global energy and environmental goals. Building performance simulation has been increasingly used as a tool for designing, operating and retrofitting buildings to save energy and utility costs. However, opportunities remain for researchers, software developers, practitioners and policymakers to maximize the value of building performance simulation in the design and operation of low energy buildings and communities that leverage interdisciplinary approaches to integrate humans, buildings, and the power grid at a large scale. This paper presents ten challenges that highlight some of the most important issues in building performance simulation, covering the full building life cycle and a wide range of modeling scales. The formulation and discussion of each challenge aims to provide insights into the state-of-the-art and future research opportunities for each topic, and to inspire new questions from young researchers in this field.
ResearchGate has not been able to resolve any references for this publication.