# On Designing Data Models for Energy Feature Stores

arXiv: https://arxiv.org/abs/2205.04267 · HTML: https://mlsanity.com/paper/2205.04267/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 44 references that are in the map.
- First posted on arXiv 9 May 2022.

## Builds on (most cited first, 4 of 4)

- [Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges](https://mlsanity.com/paper/1802.04117/) (2018)
- [Resource-aware Time Series Imaging Classification for Wireless Link Layer Anomalies](https://mlsanity.com/paper/2104.00972/) (2021)
- [A comparison of non-intrusive load monitoring methods for commercial and residential buildings](https://mlsanity.com/paper/1408.6595/) (2014)
- [Learning to Fairly Classify the Quality of Wireless Links](https://mlsanity.com/paper/2102.11655/) (2021)

## Abstract (arXiv)

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.