On Designing Data Models for Energy Feature Stores

TL;DR

This paper introduces a taxonomy for designing data models tailored for energy applications, demonstrating how richer models improve machine learning performance and benchmarking feature management solutions.

Contribution

Proposes a novel taxonomy for energy data models and evaluates their impact on feature engineering and model performance.

Findings

Richer data models enhance forecasting accuracy.

Feature engineering significantly improves model performance.

Benchmarking shows open-source feature store effectiveness.

Abstract

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.