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The digital transformation of the energy infrastructure enables new, datadriven, applications often supported by machine learning models. However,domain specific data transformations, pre-processing and management in moderndata driven pipelines is yet to be addressed.  In this paper we perform a first time study on generic data models that areable to support designing feature management solutions that are the mostimportant component in developing ML-based energy applications. We firstpropose a taxonomy for designing data models suitable for energy applications,explain how this model can support the design of features and their subsequentmanagement by specialized feature stores. Using a short-term forecastingdataset, we show the benefits of designing richer data models and engineeringthe features on the performance of the resulting models. Finally, we benchmarkthree complementary feature management solutions, including an open-sourcefeature store suitable for time series.

On Designing Data Models for Energy Feature Stores