ESTSS—energy system time series suite: a declustered, application-independent, semi-artificial load profile benchmark set

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...
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Autor*in:	Sebastian Günther [verfasserIn] Jonathan Brandt [verfasserIn] Astrid Bensmann [verfasserIn] Richard Hanke-Rauschenbach [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Time series Time series analysis Time series features Feature engineering Load profiles Energy systems

Übergeordnetes Werk:	In: Energy Informatics - SpringerOpen, 2018, 7(2024), 1, Seite 26
Übergeordnetes Werk:	volume:7 ; year:2024 ; number:1 ; pages:26

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1186/s42162-024-00304-8

Katalog-ID:	DOAJ092205062

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author	Sebastian Günther
spellingShingle	Sebastian Günther misc HD9502-9502.5 misc Time series misc Time series analysis misc Time series features misc Feature engineering misc Load profiles misc Energy systems misc Energy industries. Energy policy. Fuel trade ESTSS—energy system time series suite: a declustered, application-independent, semi-artificial load profile benchmark set
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topic_title	HD9502-9502.5 ESTSS—energy system time series suite: a declustered, application-independent, semi-artificial load profile benchmark set Time series Time series analysis Time series features Feature engineering Load profiles Energy systems
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title	ESTSS—energy system time series suite: a declustered, application-independent, semi-artificial load profile benchmark set
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title_auth	ESTSS—energy system time series suite: a declustered, application-independent, semi-artificial load profile benchmark set
abstract	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 https://github.com/s-guenther/estss and https://zenodo.org/records/10213145 .
abstractGer	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 https://github.com/s-guenther/estss and https://zenodo.org/records/10213145 .
abstract_unstemmed	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 https://github.com/s-guenther/estss and https://zenodo.org/records/10213145 .
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title_short	ESTSS—energy system time series suite: a declustered, application-independent, semi-artificial load profile benchmark set
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