Stochastic load profile generator for residential EV charging

Sørensen, Åse Lekang; Westad, Maria Claire; Delgado, Benjamín Manrique; Lindberg, Karen Byskov

doi:10.1051/e3sconf/202236203005

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…[3,27,28,[31][32][33][34]), electric vehicle load profiles (e.g. [35][36][37]), grid and microgrid load profiles (e.g. [4,6,38]) and renewable energy load profiles (e.g.…”

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confidence: 99%

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ESTSS – Energy System Time Series Suite: A declustered, application-independent, semi-artificial load profile benchmark set

Günther,

Brandt,

Bensmann

et al. 2024

Preprint

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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 byproduct in this 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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“…[3,27,28,[31][32][33][34]), electric vehicle load profiles (e.g. [35][36][37]), grid and microgrid load profiles (e.g. [4,6,38]) and renewable energy load profiles (e.g.…”

mentioning

confidence: 99%

“…[13,22,23,41]), probabilistic/stochastic approaches (e.g. [3,[19][20][21][35][36][37]), clustering/segmentation (e.g. [3,18,24,25,29,34,42]), regression (e.g.…”

mentioning

confidence: 99%

ESTSS – Energy System Time Series Suite: A declustered, application-independent, semi-artificial load profile benchmark set

Günther,

Brandt,

Bensmann

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

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

Günther,

Brandt,

Bensmann

et al. 2024

Energy Inform

View full text Add to dashboard Cite

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.

show abstract

Stochastic load profile generator for residential EV charging

Cited by 2 publications

References 13 publications

ESTSS – Energy System Time Series Suite: A declustered, application-independent, semi-artificial load profile benchmark set

ESTSS – Energy System Time Series Suite: A declustered, application-independent, semi-artificial load profile benchmark set

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

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