Abstract
Full integration of building energy modelling into the design and retrofit process has long been a goal of building scientists and practitioners. However, significant barriers still exist. Among them are the lack of available: (1) configurable technology stacks for performing both small- and large-scale analyses, (2) different classes of algorithms compatible with common design workflows, and (3) analysis tools for effectively visualizing large-scale simulation results. This article discusses the OpenStudio® Analysis Framework: a scalable analysis framework for building energy modelling that was developed to overcome the three barriers listed above. The framework is open-source and scalable to facilitate wider adoption and has a clearly defined application programming interface upon which other applications can be built. It runs on high-performance computing systems, within cloud infrastructure, and on laptops, and uses a common workflow to enable different classes of algorithms. Lessons learned from previous development efforts are also discussed.
Highlights
As detailed in Hong, Langevin, and Sun (2018), the full integration of building energy modelling (BEM) into the building design and retrofit process has been a long-term goal of many building scientists and practitioners
We introduce the OpenStudio Analysis Framework (OSAF), which is the culmination of many of these previous attempts to create a workflow with the intention of addressing the three adoption barriers, depicted in Figure 1, in a single framework
EnergyPlus runtime was significantly longer than other modelling tools at the time, such as Department of Energy (DOE)-2, but the increased runtime was deemed acceptable because EnergyPlus was designed to handle more complex building interactions across the various building domains (Crawley et al 2008)
Summary
As detailed in Hong, Langevin, and Sun (2018), the full integration of building energy modelling (BEM) into the building design and retrofit process has been a long-term goal of many building scientists and practitioners. To conduct a thorough analysis in a realistic timeframe, practitioners need access to various algorithms to perform sensitivity analyses, uncertainty quantification, design optimization, and model calibration. These algorithms should be configurable and effortlessly interchangeable without having to completely reformulate the problem. Creating computing resources to perform even the simplest of analyses is a major barrier for typical BEM practitioners Compounding this is the fact that BEM projects have evolved over the last decade from being relatively simple batch run simulations to more advanced analyses requiring iterative optimization and calibration algorithms.
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