ESTSS—energy system time series suite

a declustered, application-independent, semi-artificial load profile benchmark set

verfasst von

Sebastian Günther, Jonathan Brandt, Astrid Bensmann, Richard Hanke-Rauschenbach

Abstract

This paper introduces an univariate application-independent set of load profiles or time series derived from real-world energy system data. The generation involved a two-step process: manifolding the initial dataset through signal processors to increase diversity and heterogeneity, followed by a declustering process that removes data redundancy. The study employed common feature engineering and machine learning techniques: the time series are transformed into a normalized feature space, followed by a dimensionality reduction via hierarchical clustering, and optimization. The resulting dataset is uniformly distributed across multiple feature space dimensions while retaining typical time and frequency domain characteristics inherent in energy system time series. This data serves various purposes, including algorithm testing, uncovering functional relationships between time series features and system performance, and training machine learning models. Two case studies demonstrate the claims: one focused on the suitability of hybrid energy storage systems and the other on quantifying the onsite hydrogen supply cost in green hydrogen production sites. The declustering algorithm, although a bys study, shows promise for further scientific exploration. The data and source code are openly accessible, providing a robust platform for future comparative studies. This work also offers smaller subsets for computationally intensive research. Data and source code can be found at github.com/s-guenther/estss and zenodo.org/records/10213145 .

Organisationseinheit(en)

Institut für Elektrische Energiesysteme

Typ

Artikel

Journal

Energy Informatics

Band

7

Anzahl der Seiten

26

Publikationsdatum

22.01.2024

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Information systems, Energieanlagenbau und Kraftwerkstechnik, Computernetzwerke und -kommunikation

Ziele für nachhaltige Entwicklung

SDG 7 – Erschwingliche und saubere Energie

Elektronische Version(en)

https://doi.org/10.1186/s42162-024-00304-8 (Zugang: Offen)