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# AHU use case | ||
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## Overview | ||
This is a demonstration, in which the Scikit models are used to control an | ||
air handling unit (AHU). | ||
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The task is to control the supply air temperature, while the outdoor | ||
temperature fluctuates. | ||
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### Provide the Scikit models | ||
The models are Multi-Layer Perceptron (MLP) models created with Scikit-learn. | ||
You can use the frozen models stored in | ||
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pyDMPC\pyDMPC\SpecialStudies\FrozenModels\AHU | ||
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and copy them and the corresponding scalers (required to scale the inputs for | ||
the MLP) to the working directory. If you would like to create the models from Modelica models, you can use this [script](../pyDMPC/Utilities/mlp_train_modelica.py). This script requires an FMU (see settings below) of the | ||
heater and cooler models in | ||
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ModelicaModels.SubsystemModels.DetailedModels.HeaterML and | ||
ModelicaModels.SubsystemModels.DetailedModels.CoolerML | ||
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### Controlled system (AHU) | ||
The central AHU model is stored in this path in the ModelicaModels package of | ||
pyDMPC | ||
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ModelicaModels.ControlledSystems.ControlledSystemBoundaries | ||
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Use Dymola to export the model to a functional mockup unit (FMU) according to | ||
the following settings: | ||
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![FMU](../pyDMPC/Resources/Images/FMU.png) | ||
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The FMU filename and the file's location has to be included into the Init.py. | ||
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### Settings | ||
All settings are given in the Init.py according as described in the | ||
[Settings](Settings.md). |
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