The directory contains main steps for wind power forecasting applications and is a part of a Master thesis at KU Leuven, 2022. The dataset of interest is La Haute Borne wind farm in France. The repository details all preprocessing and forecasting steps - data analysis, imputation, feature engineering and modeling. The notebooks are used to compare various forecasting methodologies - Persistence, ARIMA, LSTM or ensembles (SVR or Decision trees).
- solver - directory containing all required functions used in the notebooks
- notebooks - directory containing all workflow for wind power forecasting
- script - directory with external files important for forecasting
notebooks
|-- 1_data_analysis.ipynb
|-- 2_outliers_removal.ipynb
|-- 3_feature_engineering.ipynb
|-- 4_imputation.ipynb
|-- 5_ARIMA_forecasting.ipynb
|-- 5_COND-LSTM_forecasting.ipynb
|-- 5_LSTM_forecasting.ipynb
|-- 5_Persistence_forecasting.ipynb
|-- 5_ensembles_forecasting.ipynb
script
|-- get_hyperparameters_LSTM.py
|-- get_hyperparameters_ensemble.py
solver
|-- arima.py
|-- ensembles.py
|-- lstm.py
|-- persistence.py
|-- processing.py
wind_power37.yml
.gitignore
LICENSE
README.md
setup.py
Use the conda environment file wind_power37.yml to install the required wind_power37 environment and its modules.
conda env create -f wind_power37.yml
Activate wind_power37
conda environment:
conda activate wind_power37
From the root directory, create solver package which can be accessed from all the notebooks:
conda develop .