  Data-driven modeling is an approach in energy systems modeling that has been
gaining popularity. In data-driven modeling, machine learning methods such as
linear regression, neural networks or decision-tree based methods are being
applied. While these methods do not require domain knowledge, they are
sensitive to data quality. Therefore, improving data quality in a dataset is
beneficial for creating machine learning-based models. The improvement of data
quality can be implemented through preprocessing methods. A selected type of
preprocessing is feature engineering, which focuses on evaluating and improving
the quality of certain features inside the dataset. Feature engineering methods
include methods such as feature creation, feature expansion, or feature
selection. In this work, a Python framework containing different feature
engineering methods is presented. This framework contains different methods for
feature creation, expansion and selection; in addition, methods for
transforming or filtering data are implemented. The implementation of the
framework is based on the Python library scikit-learn. The framework is
demonstrated on a case study of a use case from energy demand prediction. A
data-driven model is created including selected feature engineering methods.
The results show an improvement in prediction accuracy through the engineered
features.
