  The digital transformation of the energy infrastructure enables new, data
driven, applications often supported by machine learning models. However,
domain specific data transformations, pre-processing and management in modern
data driven pipelines is yet to be addressed.
  In this paper we perform a first time study on generic data models that are
able to support designing feature management solutions that are the most
important component in developing ML-based energy applications. We first
propose a taxonomy for designing data models suitable for energy applications,
explain how this model can support the design of features and their subsequent
management by specialized feature stores. Using a short-term forecasting
dataset, we show the benefits of designing richer data models and engineering
the features on the performance of the resulting models. Finally, we benchmark
three complementary feature management solutions, including an open-source
feature store suitable for time series.
