Advancing Electric Load Forecasting: Leveraging Federated Learning for Distributed, Non-Stationary, and Discontinuous Time Series Article Swipe
Lucas Richter
,
Steve Lenk
,
Peter Bretschneider
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/smartcities7040082
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/smartcities7040082
In line with several European directives, residents are strongly encouraged to invest in renewable power plants and flexible consumption systems, enabling them to share energy within their Renewable Energy Community at lower procurement costs. This, along with the ability for residents to switch between such communities on a daily basis, leads to dynamic portfolios, resulting in non-stationary and discontinuous electrical load time series. Given poor predictability as well as insufficient examination of such characteristics, and the critical importance of electrical load forecasting in energy management systems, we propose a novel forecasting framework using Federated Learning to leverage information from multiple distributed communities, enabling the learning of domain-invariant features. To achieve this, we initially utilize synthetic electrical load time series at district level and aggregate them to profiles of Renewable Energy Communities with dynamic portfolios. Subsequently, we develop a forecasting model that accounts for the composition of residents of a Renewable Energy Community, adapt data pre-processing in accordance with the time series process, and detail a federated learning algorithm that incorporates weight averaging and data sharing. Following the training of various experimental setups, we evaluate their effectiveness by applying different tests for white noise in the forecast error signal. The findings suggest that our proposed framework is capable of effectively forecast non-stationary as well as discontinuous time series, extract domain-invariant features, and is applicable to new, unseen data through the integration of knowledge from multiple sources.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/smartcities7040082
- OA Status
- gold
- Cited By
- 6
- References
- 33
- Related Works
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- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4401242579Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/smartcities7040082Digital Object Identifier
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Advancing Electric Load Forecasting: Leveraging Federated Learning for Distributed, Non-Stationary, and Discontinuous Time SeriesWork title
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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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2024-07-28Full publication date if available
- Authors
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Lucas Richter, Steve Lenk, Peter BretschneiderList of authors in order
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https://doi.org/10.3390/smartcities7040082Publisher landing page
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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://doi.org/10.3390/smartcities7040082Direct OA link when available
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Series (stratigraphy), Computer science, Time series, Distributed computing, Industrial engineering, Machine learning, Engineering, Geology, PaleontologyTop concepts (fields/topics) attached by OpenAlex
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6Total citation count in OpenAlex
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2025: 5, 2024: 1Per-year citation counts (last 5 years)
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33Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.detail | 163 |
| abstract_inverted_index.energy | 24, 83 |
| abstract_inverted_index.invest | 11 |
| abstract_inverted_index.plants | 15 |
| abstract_inverted_index.series | 118, 160 |
| abstract_inverted_index.switch | 42 |
| abstract_inverted_index.unseen | 225 |
| abstract_inverted_index.weight | 170 |
| abstract_inverted_index.within | 25 |
| abstract_inverted_index.ability | 38 |
| abstract_inverted_index.achieve | 109 |
| abstract_inverted_index.between | 43 |
| abstract_inverted_index.capable | 206 |
| abstract_inverted_index.develop | 136 |
| abstract_inverted_index.dynamic | 52, 132 |
| abstract_inverted_index.extract | 217 |
| abstract_inverted_index.propose | 87 |
| abstract_inverted_index.series, | 216 |
| abstract_inverted_index.series. | 62 |
| abstract_inverted_index.setups, | 181 |
| abstract_inverted_index.several | 3 |
| abstract_inverted_index.signal. | 197 |
| abstract_inverted_index.suggest | 200 |
| abstract_inverted_index.through | 227 |
| abstract_inverted_index.utilize | 113 |
| abstract_inverted_index.various | 179 |
| abstract_inverted_index.European | 4 |
| abstract_inverted_index.Learning | 94 |
| abstract_inverted_index.accounts | 141 |
| abstract_inverted_index.applying | 187 |
| abstract_inverted_index.critical | 76 |
| abstract_inverted_index.district | 120 |
| abstract_inverted_index.enabling | 20, 102 |
| abstract_inverted_index.evaluate | 183 |
| abstract_inverted_index.findings | 199 |
| abstract_inverted_index.flexible | 17 |
| abstract_inverted_index.forecast | 195, 209 |
| abstract_inverted_index.learning | 104, 166 |
| abstract_inverted_index.leverage | 96 |
| abstract_inverted_index.multiple | 99, 233 |
| abstract_inverted_index.process, | 161 |
| abstract_inverted_index.profiles | 126 |
| abstract_inverted_index.proposed | 203 |
| abstract_inverted_index.sharing. | 174 |
| abstract_inverted_index.sources. | 234 |
| abstract_inverted_index.strongly | 8 |
| abstract_inverted_index.systems, | 19, 85 |
| abstract_inverted_index.training | 177 |
| abstract_inverted_index.Community | 29 |
| abstract_inverted_index.Federated | 93 |
| abstract_inverted_index.Following | 175 |
| abstract_inverted_index.Renewable | 27, 128, 149 |
| abstract_inverted_index.aggregate | 123 |
| abstract_inverted_index.algorithm | 167 |
| abstract_inverted_index.averaging | 171 |
| abstract_inverted_index.different | 188 |
| abstract_inverted_index.features, | 219 |
| abstract_inverted_index.features. | 107 |
| abstract_inverted_index.federated | 165 |
| abstract_inverted_index.framework | 91, 204 |
| abstract_inverted_index.initially | 112 |
| abstract_inverted_index.knowledge | 231 |
| abstract_inverted_index.renewable | 13 |
| abstract_inverted_index.residents | 6, 40, 146 |
| abstract_inverted_index.resulting | 54 |
| abstract_inverted_index.synthetic | 114 |
| abstract_inverted_index.Community, | 151 |
| abstract_inverted_index.accordance | 156 |
| abstract_inverted_index.applicable | 222 |
| abstract_inverted_index.electrical | 59, 79, 115 |
| abstract_inverted_index.encouraged | 9 |
| abstract_inverted_index.importance | 77 |
| abstract_inverted_index.management | 84 |
| abstract_inverted_index.Communities | 130 |
| abstract_inverted_index.communities | 45 |
| abstract_inverted_index.composition | 144 |
| abstract_inverted_index.consumption | 18 |
| abstract_inverted_index.directives, | 5 |
| abstract_inverted_index.distributed | 100 |
| abstract_inverted_index.effectively | 208 |
| abstract_inverted_index.examination | 70 |
| abstract_inverted_index.forecasting | 81, 90, 138 |
| abstract_inverted_index.information | 97 |
| abstract_inverted_index.integration | 229 |
| abstract_inverted_index.portfolios, | 53 |
| abstract_inverted_index.portfolios. | 133 |
| abstract_inverted_index.procurement | 32 |
| abstract_inverted_index.communities, | 101 |
| abstract_inverted_index.experimental | 180 |
| abstract_inverted_index.incorporates | 169 |
| abstract_inverted_index.insufficient | 69 |
| abstract_inverted_index.Subsequently, | 134 |
| abstract_inverted_index.discontinuous | 58, 214 |
| abstract_inverted_index.effectiveness | 185 |
| abstract_inverted_index.non-stationary | 56, 210 |
| abstract_inverted_index.pre-processing | 154 |
| abstract_inverted_index.predictability | 65 |
| abstract_inverted_index.characteristics, | 73 |
| abstract_inverted_index.domain-invariant | 106, 218 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5028594370 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I4210111500 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.7900000214576721 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.84949935 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |