Generating Synthetic Electricity Load Time Series at District Scale Using Probabilistic Forecasts Article Swipe
Lucas Richter
,
Tom Bender
,
Steve Lenk
,
Peter Bretschneider
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/en17071634
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/en17071634
Thanks to various European directives, individuals are empowered to share and trade electricity within Renewable Energy Communities, enhancing the operational efficiency of local energy systems. The digital transformation of the energy market enables the integration of decentralized energy resources using cloud computing, the Internet of Things, and artificial intelligence. In order to assess the feasibility of new business models based on data-driven solutions, various electricity consumption time series are necessary at this level of aggregation. Since these are currently not yet available in sufficient quality and quantity, and due to data privacy reasons, synthetic time series are essential in the strategic planning of smart grid energy systems. By enabling the simulation of diverse scenarios, they facilitate the integration of new technologies and the development of effective demand response strategies. Moreover, they provide valuable data for assessing novel load forecasting methodologies that are essential to manage energy efficiently and to ensure grid stability. Therefore, this research proposes a methodology to synthesize electricity consumption time series by applying the Box–Jenkins method, an intelligent sampling technique for data augmentation and a probabilistic forecast model. This novel approach emulates the stochastic nature of electricity consumption time series and synthesizes realistic ones of Renewable Energy Communities concerning seasonal as well as short-term variations and stochasticity. Comparing autocorrelations, distributions of values, and principle components of daily sequences between real and synthetic time series, the results exhibit nearly identical characteristics to the original data and, thus, are usable in designing and studying efficient smart grid systems.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/en17071634
- https://www.mdpi.com/1996-1073/17/7/1634/pdf?version=1711641492
- OA Status
- gold
- Cited By
- 5
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4393255206
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4393255206Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/en17071634Digital Object Identifier
- Title
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Generating Synthetic Electricity Load Time Series at District Scale Using Probabilistic ForecastsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-03-28Full publication date if available
- Authors
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Lucas Richter, Tom Bender, Steve Lenk, Peter BretschneiderList of authors in order
- Landing page
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https://doi.org/10.3390/en17071634Publisher landing page
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https://www.mdpi.com/1996-1073/17/7/1634/pdf?version=1711641492Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/1996-1073/17/7/1634/pdf?version=1711641492Direct OA link when available
- Concepts
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Electricity, Series (stratigraphy), Probabilistic logic, Scale (ratio), Time series, Environmental science, Econometrics, Computer science, Reliability engineering, Engineering, Economics, Machine learning, Geography, Artificial intelligence, Electrical engineering, Geology, Cartography, PaleontologyTop concepts (fields/topics) attached by OpenAlex
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5Total citation count in OpenAlex
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2025: 2, 2024: 3Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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