Peak load estimation based on consumer heating type classification powered by deep learning
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| Title: | Peak load estimation based on consumer heating type classification powered by deep learning |
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| Authors: | Fürst, Kristoffer, 1990, Chen, Peiyuan, 1983 |
| Source: | CIRED 2024 Vienna Workshop , Vienna, Austria IET Conference Proceedings. 2024(5):1010-1014 |
| Subject Terms: | Smart meter, Classification, Deep learning, Heating types, Peak load estimation |
| Description: | The heating system of a building could significantly impact the aggregated electricity peak load. It is in the interest of the distribution system operators (DSOs) to both know what type of heating system that is connected to the grid, and the impact of end-users changing their heating system. Focusing on the transition from non-electric heating systems to heat pumps, this paper investigates its impact on peak load consumption. A state-of-the-art heating type classification method using smart meter data and deep learning was used to to first classify the heating types of single-family dwellings. Building upon previous work, a multi-label approach was adopted with the classifier to accommodate buildings with multiple heating sources. To assess the impact of heating system changes, smart meter data were substituted with data from similar buildings equipped with heat pumps. This process was repeated for statistical confidence. A geographical analyses identify areas susceptible to a large peak load increase, demonstrating practical application. |
| Access URL: | https://research.chalmers.se/publication/545114 |
| Database: | SwePub |
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| Items | – Name: Title Label: Title Group: Ti Data: Peak load estimation based on consumer heating type classification powered by deep learning – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Fürst%2C+Kristoffer%22">Fürst, Kristoffer</searchLink>, 1990<br /><searchLink fieldCode="AR" term="%22Chen%2C+Peiyuan%22">Chen, Peiyuan</searchLink>, 1983 – Name: TitleSource Label: Source Group: Src Data: <i>CIRED 2024 Vienna Workshop , Vienna, Austria IET Conference Proceedings</i>. 2024(5):1010-1014 – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Smart+meter%22">Smart meter</searchLink><br /><searchLink fieldCode="DE" term="%22Classification%22">Classification</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Heating+types%22">Heating types</searchLink><br /><searchLink fieldCode="DE" term="%22Peak+load+estimation%22">Peak load estimation</searchLink> – Name: Abstract Label: Description Group: Ab Data: The heating system of a building could significantly impact the aggregated electricity peak load. It is in the interest of the distribution system operators (DSOs) to both know what type of heating system that is connected to the grid, and the impact of end-users changing their heating system. Focusing on the transition from non-electric heating systems to heat pumps, this paper investigates its impact on peak load consumption. A state-of-the-art heating type classification method using smart meter data and deep learning was used to to first classify the heating types of single-family dwellings. Building upon previous work, a multi-label approach was adopted with the classifier to accommodate buildings with multiple heating sources. To assess the impact of heating system changes, smart meter data were substituted with data from similar buildings equipped with heat pumps. This process was repeated for statistical confidence. A geographical analyses identify areas susceptible to a large peak load increase, demonstrating practical application. – Name: URL Label: Access URL Group: URL Data: <link linkTarget="URL" linkTerm="https://research.chalmers.se/publication/545114" linkWindow="_blank">https://research.chalmers.se/publication/545114</link> |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1049/icp.2024.1965 Languages: – Text: English PhysicalDescription: Pagination: PageCount: 5 StartPage: 1010 Subjects: – SubjectFull: Smart meter Type: general – SubjectFull: Classification Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Heating types Type: general – SubjectFull: Peak load estimation Type: general Titles: – TitleFull: Peak load estimation based on consumer heating type classification powered by deep learning Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Fürst, Kristoffer – PersonEntity: Name: NameFull: Chen, Peiyuan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 27324494 – Type: issn-locals Value: CTH_SWEPUB Numbering: – Type: volume Value: 2024 – Type: issue Value: 5 Titles: – TitleFull: CIRED 2024 Vienna Workshop , Vienna, Austria IET Conference Proceedings Type: main |
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