Examples

This folder contains several example files which help with the understanding of ebcpy.

Getting started

While these examples should run in any IDE, we advise using PyCharm. Before being able to run these examples, be sure to:

1. Create a clean environment of python 3.7 or 3.8. In Anaconda run: conda create -n py38_ebcpy python=3.8

2. Activate the environment in your terminal. In Anaconda run: activate py38_ebcpy

3. Clone the repository by running git clone https://github.com/RWTH-EBC/ebcpy

4. Clone the AixLib in order to use the models: git clone https://github.com/RWTH-EBC/AixLib Also check if you’re on development using cd AixLib && git status && cd ..

5. Install the library using pip install ebcpy. In order to execute everything, install the full version using pip install ebcpy[full]

What can I learn in the examples?

e1_time_series_data_example.py

1. Learn how to use TimeSeriesData

2. Understand why we use TimeSeriesData

3. Get to know the different processing functions

e2_fmu_example.py

1. Learn how to use the FMU_API

2. Understand model variables

3. Learn how to change variables to store (result_names)

4. Learn how to change parameters of a simulation

5. Learn how to change inputs of a simulation

6. Learn how to run simulations in parallel

e3_dymola_example.py

1. Learn how to use the DymolaAPI

2. Learn the different result options of the simulation

3. Learn how to convert inputs into the Dymola format

e4_optimization_example.py

1. Learn how to create a custom Optimizer class

2. Learn the different optimizer frameworks

3. See the difference in optimization when using newton-based methods and evolutionary algorithms. The difference is, that newton based methods (like L-BFGS-B) are vastly faster in both convex and concave problems, but they are not guaranteed to find the global minimum and can get stock in local optima. Evolutionary algorithms (like the genetic algorithm) are substantially slower, but they can overcome local optima, as shown in the concave examples.