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:	Günther, Sebastian [verfasserIn] Brandt, Jonathan Bensmann, Astrid Hanke-Rauschenbach, Richard

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Time series Time series analysis Time series features Feature engineering Load profiles Energy systems Machine learning Statistical analysis Systems modeling

Anmerkung:	© The Author(s) 2024

Übergeordnetes Werk:	Enthalten in: Energy informatics - Cham : Springer International Publishing, 2018, 7(2024), 1 vom: 22. Jan.
Übergeordnetes Werk:	volume:7 ; year:2024 ; number:1 ; day:22 ; month:01

Links:	Volltext

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

Katalog-ID:	SPR054482488

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allfields	10.1186/s42162-024-00304-8 doi (DE-627)SPR054482488 (SPR)s42162-024-00304-8-e DE-627 ger DE-627 rakwb eng Günther, Sebastian verfasserin aut ESTSS—energy system time series suite: a declustered, application-independent, semi-artificial load profile benchmark set 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 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. Time series (dpeaa)DE-He213 Time series analysis (dpeaa)DE-He213 Time series features (dpeaa)DE-He213 Feature engineering (dpeaa)DE-He213 Load profiles (dpeaa)DE-He213 Energy systems (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Statistical analysis (dpeaa)DE-He213 Systems modeling (dpeaa)DE-He213 Brandt, Jonathan aut Bensmann, Astrid aut Hanke-Rauschenbach, Richard aut Enthalten in Energy informatics Cham : Springer International Publishing, 2018 7(2024), 1 vom: 22. Jan. (DE-627)1031001387 (DE-600)2942905-5 2520-8942 nnns volume:7 year:2024 number:1 day:22 month:01 https://dx.doi.org/10.1186/s42162-024-00304-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2024 1 22 01
spelling	10.1186/s42162-024-00304-8 doi (DE-627)SPR054482488 (SPR)s42162-024-00304-8-e DE-627 ger DE-627 rakwb eng Günther, Sebastian verfasserin aut ESTSS—energy system time series suite: a declustered, application-independent, semi-artificial load profile benchmark set 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 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. Time series (dpeaa)DE-He213 Time series analysis (dpeaa)DE-He213 Time series features (dpeaa)DE-He213 Feature engineering (dpeaa)DE-He213 Load profiles (dpeaa)DE-He213 Energy systems (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Statistical analysis (dpeaa)DE-He213 Systems modeling (dpeaa)DE-He213 Brandt, Jonathan aut Bensmann, Astrid aut Hanke-Rauschenbach, Richard aut Enthalten in Energy informatics Cham : Springer International Publishing, 2018 7(2024), 1 vom: 22. Jan. (DE-627)1031001387 (DE-600)2942905-5 2520-8942 nnns volume:7 year:2024 number:1 day:22 month:01 https://dx.doi.org/10.1186/s42162-024-00304-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2024 1 22 01
allfields_unstemmed	10.1186/s42162-024-00304-8 doi (DE-627)SPR054482488 (SPR)s42162-024-00304-8-e DE-627 ger DE-627 rakwb eng Günther, Sebastian verfasserin aut ESTSS—energy system time series suite: a declustered, application-independent, semi-artificial load profile benchmark set 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 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. Time series (dpeaa)DE-He213 Time series analysis (dpeaa)DE-He213 Time series features (dpeaa)DE-He213 Feature engineering (dpeaa)DE-He213 Load profiles (dpeaa)DE-He213 Energy systems (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Statistical analysis (dpeaa)DE-He213 Systems modeling (dpeaa)DE-He213 Brandt, Jonathan aut Bensmann, Astrid aut Hanke-Rauschenbach, Richard aut Enthalten in Energy informatics Cham : Springer International Publishing, 2018 7(2024), 1 vom: 22. Jan. (DE-627)1031001387 (DE-600)2942905-5 2520-8942 nnns volume:7 year:2024 number:1 day:22 month:01 https://dx.doi.org/10.1186/s42162-024-00304-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2024 1 22 01
allfieldsGer	10.1186/s42162-024-00304-8 doi (DE-627)SPR054482488 (SPR)s42162-024-00304-8-e DE-627 ger DE-627 rakwb eng Günther, Sebastian verfasserin aut ESTSS—energy system time series suite: a declustered, application-independent, semi-artificial load profile benchmark set 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 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. Time series (dpeaa)DE-He213 Time series analysis (dpeaa)DE-He213 Time series features (dpeaa)DE-He213 Feature engineering (dpeaa)DE-He213 Load profiles (dpeaa)DE-He213 Energy systems (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Statistical analysis (dpeaa)DE-He213 Systems modeling (dpeaa)DE-He213 Brandt, Jonathan aut Bensmann, Astrid aut Hanke-Rauschenbach, Richard aut Enthalten in Energy informatics Cham : Springer International Publishing, 2018 7(2024), 1 vom: 22. Jan. (DE-627)1031001387 (DE-600)2942905-5 2520-8942 nnns volume:7 year:2024 number:1 day:22 month:01 https://dx.doi.org/10.1186/s42162-024-00304-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2024 1 22 01
allfieldsSound	10.1186/s42162-024-00304-8 doi (DE-627)SPR054482488 (SPR)s42162-024-00304-8-e DE-627 ger DE-627 rakwb eng Günther, Sebastian verfasserin aut ESTSS—energy system time series suite: a declustered, application-independent, semi-artificial load profile benchmark set 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 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. Time series (dpeaa)DE-He213 Time series analysis (dpeaa)DE-He213 Time series features (dpeaa)DE-He213 Feature engineering (dpeaa)DE-He213 Load profiles (dpeaa)DE-He213 Energy systems (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Statistical analysis (dpeaa)DE-He213 Systems modeling (dpeaa)DE-He213 Brandt, Jonathan aut Bensmann, Astrid aut Hanke-Rauschenbach, Richard aut Enthalten in Energy informatics Cham : Springer International Publishing, 2018 7(2024), 1 vom: 22. Jan. (DE-627)1031001387 (DE-600)2942905-5 2520-8942 nnns volume:7 year:2024 number:1 day:22 month:01 https://dx.doi.org/10.1186/s42162-024-00304-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2024 1 22 01
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author	Günther, Sebastian
spellingShingle	Günther, Sebastian misc Time series misc Time series analysis misc Time series features misc Feature engineering misc Load profiles misc Energy systems misc Machine learning misc Statistical analysis misc Systems modeling ESTSS—energy system time series suite: a declustered, application-independent, semi-artificial load profile benchmark set
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topic_title	ESTSS—energy system time series suite: a declustered, application-independent, semi-artificial load profile benchmark set Time series (dpeaa)DE-He213 Time series analysis (dpeaa)DE-He213 Time series features (dpeaa)DE-He213 Feature engineering (dpeaa)DE-He213 Load profiles (dpeaa)DE-He213 Energy systems (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Statistical analysis (dpeaa)DE-He213 Systems modeling (dpeaa)DE-He213
topic	misc Time series misc Time series analysis misc Time series features misc Feature engineering misc Load profiles misc Energy systems misc Machine learning misc Statistical analysis misc Systems modeling
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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_sort	estss—energy system time series suite: a declustered, application-independent, semi-artificial load profile benchmark set
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. © The Author(s) 2024
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. © The Author(s) 2024
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. © The Author(s) 2024
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ESTSS—energy system time series suite: a declustered, application-independent, semi-artificial load profile benchmark set

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