Publikation: How does Load Disaggregation Performanc...
Stammdaten
Titel: | How does Load Disaggregation Performance Depend on Data Characteristics? Insights from a Benchmarking Study |
Untertitel: | |
Kurzfassung: | Electrical consumption data contain a wealth of information, and their collection at scale is facilitated by the deployment of smart meters. Data collected this way is an aggregation of the power demands of all appliances within a building, hence inferences on the operation of individual devices cannot be drawn directly. By using methods to disaggregate data collected from a single measurement location, however, appliance-level detail can often be reconstructed. A major impediment to the improvement of such disaggregation algorithms lies in the way they are evaluated so far: Their performance is generally assessed using a small number of publicly available electricity consumption data sets recorded from actual buildings. As a result, algorithm parameters are often tuned to produce optimal results for the used data sets, but do not necessarily generalize to different input data well. We propose to break this tradition by presenting a toolchain to create synthetic benchmarking data sets for the evaluation of disaggregation performance in this work. Generated synthetic data with a configurable amount of concurrent appliance activity is subsequently used to comparatively evaluate eight existing disaggregation algorithms. This way, we not only create a baseline for the comparison of newly developed disaggregation methods, but also point out the data characteristics that pose challenges for the state-of-the-art. |
Schlagworte: |
Publikationstyp: | Beitrag in Proceedings (Autorenschaft) |
Erscheinungsdatum: | 24.06.2020 (Print) |
Erschienen in: |
In Proceedings of the Eleventh ACM International Conference on Future Energy Systems (e-Energy ’20)
In Proceedings of the Eleventh ACM International Conference on Future Energy Systems (e-Energy ’20)
(
ACM New York;
)
zur Publikation |
Titel der Serie: | - |
Bandnummer: | - |
Erstveröffentlichung: | Ja |
Version: | - |
Seite: | S. 167 - 177 |
Versionen
Keine Version vorhanden |
Erscheinungsdatum: | |
ISBN (e-book): | - |
eISSN: | - |
DOI: | http://dx.doi.org/10.1145/3396851.3397691 |
Homepage: | - |
Open Access |
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Erscheinungsdatum: | 24.06.2020 |
ISBN: | - |
ISSN: | - |
Homepage: | https://www.areinhardt.de/publications/2020/Reinhardt_eEnergy_2020.pdf |
AutorInnen
Zuordnung
Organisation | Adresse | ||||
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Fakultät für Technische Wissenschaften
Institut für Vernetzte und Eingebettete Systeme
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AT - 9020 Klagenfurt am Wörthersee |
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Peer Reviewed |
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Klassifikationsraster der zugeordneten Organisationseinheiten:
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Kooperationen
Organisation | Adresse | ||
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Technische Universität Clausthal
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DE
Clausthal |
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