Metadata-Version: 2.1
Name: aixcalibuha
Version: 0.2.2
Summary: Framework used for sensitivity-analysisand calibration for models of HVAC components.
Home-page: https://github.com/RWTH-EBC/AixCaliBuHA
Author: RWTH Aachen University, E.ON Energy Research Center, Institute of Energy Efficient Buildings and Indoor Climate
Author-email: fabian.wuellhorst@eonerc.rwth-aachen.de
License: MIT
Download-URL: https://github.com/RWTH-EBC/AixCaliBuHA/archive/refs/tags/0.2.2.tar.gz
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        # AixCaliBuHA
        
        **Aix** (from French Aix-la-Chapelle) 
        **Cali**bration for **Bu**ilding and **H**V**A**C Systems
        
        This framework attempts to make the process of calibrating models used in Building
        and HVAC Systems easier.
        
        # Key features
        - Performing a **Sensitivity Analysis** to discover tuner parameters for the calibration
        - **Calibration** of a given model based on the tuner parameters, the calibration classes and specified goals to evaluate the objective function of the underlying optimization
        
        # Installation
        
        To install, simply run
        ```
        pip install aixcalibuha
        ```
        
        If you encounter an error with the installation of `scikit-learn`, first install `scikit-learn` separatly and then install `ebcpy`:
        
        ```
        pip install scikit-learn
        pip install aixcalibuha
        ```
        
        If this still does not work, we refer to the troubleshooting section of `scikit-learn`: https://scikit-learn.org/stable/install.html#troubleshooting. Also check [issue 23](https://github.com/RWTH-EBC/ebcpy/issues/23) for updates.
        
        In order to help development, install it as an egg:
        
        ```
        git clone --recurse-submodules https://github.com/RWTH-EBC/AixCaliBuHA
        pip install -e AixCaliBuHA
        ```
        
        # How to get started?
        We differ this section into two parts. How to get started with the theory of calibration and how to get started with using this repo.
        
        ## How can I calibrate my model?
        While we aim at automating most parts of a calibration process, you still have to specify the inputs and the methods you want to use.
        We therefore recommend to:
        1. Analyze the physical system and theoretical model you want to calibrate
        2. Identify inputs and outputs of the system and model
        3. Get to know your tuner parameters and how they affect your model
        4. Plan your experiments and perform them
        5. Learn about the methods provided for calibration (statistical measures (RMSE, etc.), optimization, ...)
        6. **Always be critical** about the results of the process. If the model approach or the experiment is faulty, the calibration will perform accordingly. 
        
        ## How to start with AixCaliBuHa?
        We have three services in place to help you with the setup of `AixCaliBuHa`. For the basics on using this repo, we recommend the Jupyter Notebook.
        If you want to setup your calibration models (in Modelica) and quickly start your first calibration, we provide a guided setup.
        
        ### Jupyter Notebook
        We recommend running our jupyter-notebook to be guided through a **helpful tutorial**.  
        For this, run the following code:
        ```
        # If jupyter is not already installed:
        pip install jupyter
        # Go into your ebcpy-folder (cd \path_to_\AixCaliBuHA) or change the to the absolute path of the tutorial.ipynb and run:
        jupyter notebook AixCaliBuHA\examples\tutorial.ipynb
        ```
        
        ### Examples
        Clone this repo and look at the examples\README.md file.
        Here you will find several examples to execute.
        
        # Documentation
        Visit hour official [Documentation](https://ebc.pages.rwth-aachen.de/EBC_all/github_ci/AixCaliBuHA/master/docs).
        
        # Problems?
        Please [raise an issue here](https://github.com/RWTH-EBC/AixCaliBuHA/issues/new).
        
Keywords: calibration,building,energy,black-box optimization,sensitivity_analysis
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: MIT License
Classifier: Topic :: Scientific/Engineering
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/markdown
