2022/05/09 by Gregor Cerar, Blaž Bertalanič, Cerar, Gregor +7
Computer Science · Decision Sciences · #Advanced Database Systems and Queries #Artificial Intelligence (cs.AI) #Distributed and Parallel Computing Systems #FOS: Computer and information sciences #FOS: Electrical engineering #Scientific Computing and Data Management #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2205.04267
openalex publication_date 2022/05/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.