ESTSS—energy system time series suite

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

authored by

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 .

Organisation(s)

Institute of Electric Power Systems
Section Electrical Energy Storage Systems

Type

Article

Journal

Energy Informatics

Volume

No. of pages

Publication date

22.01.2024

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Information Systems, Energy Engineering and Power Technology, Computer Networks and Communications

Sustainable Development Goals

SDG 7 - Affordable and Clean Energy

Electronic version(s)

https://doi.org/10.1186/s42162-024-00304-8 (Access: Open)