The Journal of Open Source Software · 2022 · 13 citations · 11 references
AixCaliBuHA enables an automated calibration of dynamic building and HVAC (heating, ventilation and air conditioning) simulation models. Currently, the package supports the calibration of Functional Mock-up Units (FMUs) based on the Functional Mock-up Interface (FMI) standard (Modelica Association Project, 2021) as well as Modelica models through the python_interface of the Software Dymola (Dassault Systems, 2021). As the former enables a software-independent simulation, our framework is applicable to any time-variant simulation software that supports the FMI standard. Using these interfaces, we enable the automated calibration of state-of-the-art building performance simulation libraries such as the Buildings Figure At the core of AixCaliBuHA lays the definition of data types, that link the python data types to the underlying optimization problem and are used for all subsequent steps.
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SciPy 1.0: fundamental algorithms for scientific computing in Python
Nature Methods · 2020 · 35.1K citations · Full text
Dlib-ml: A Machine Learning Toolkit
Davis E. King · Journal of Machine Learning Research · 2009 · 2.9K citations
Pymoo: Multi-Objective Optimization in Python
Julian Blank, Kalyanmoy Deb · IEEE Access · 2020 · 2K citations · Full text
Artificial Intelligence, Model Optimization, Computational Science +15
Michael Wetter, Wangda Zuo, Thierry Stephane Nouidui et al. · Journal of Building Performance Simulation · 2013 · 659 citations · Full text
Buildings Library, Engineering, Architectural Engineering +20