Generating synthetic energy time series: A review

M. Turowski, B. Heidrich, L. Weingärtner, L. Springer, K. Phipps, B. Schäfer, R. Mikut and V. Hagenmeyer

Renewable and Sustainable Energy Reviews, 2024, vol. 206, issue C

Abstract: As the energy system transitions to an intelligent smart grid with a mostly renewable energy supply, synthetic energy time series are required to facilitate the development and improvement of methods for smart grid applications. These synthetic energy time series must exhibit characteristics similar to the real energy time series and applicable to specific use cases. Furthermore, evaluation methods must be applied to verify that synthetic energy time series have the desired characteristics. Whilst many methods exist in the literature to generate synthetic energy time series, up until now, no work has focused on analysing and comparing these methods. Therefore, this study provides a structured literature review of generating synthetic energy time series. The review focuses on five aspects: (1) Identifying methods used to generate synthetic energy time series, (2) categorising these methods according to the generation approach taken, (3) analysing the characteristics of these generated synthetic energy time series, (4) identifying the uses cases for which the time series are generated, and (5) considering how the generated synthetic energy time series are evaluated. In total, this study reviews 169 articles focusing on generating synthetic energy time series and identifies several key research gaps leading to multiple open research fields. The most important open research fields include the need for a standardised evaluation, more generation methods for synthetic time series from generation and battery storage systems, and a stronger focus on further use cases.

Keywords: Energy time series; Time series generation; Synthetic time series; Generation approach; Synthetic time series characteristics; Synthetic time series use cases; Synthetic time series evaluation (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.rser.2024.114842

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