Beykirch, Mario ; Janke, Tim ; Tayeche, Imed ; Steinke, Florian (2021)
Probabilistic Forecast Combination for Anomaly Detection in Building Heat Load Time Series.
2021 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe). virtual Conference (18.10.2021-21.10.2021)
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
We consider the problem of automated anomaly detection for building level heat load time series. An anomaly detection model must be applicable to a diverse group of buildings and provide robust results on heat load time series with low signal-to-noise ratios, several seasonalities, and significant exogenous effects. We propose to employ a probabilistic forecast combination approach based on an ensemble of deterministic forecasts in an anomaly detection scheme that classifies observed values based on their probability under a predictive distribution. We show empirically that forecast based anomaly detection provides improved accuracy when employing a forecast combination approach.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2021 |
| Autor(en): | Beykirch, Mario ; Janke, Tim ; Tayeche, Imed ; Steinke, Florian |
| Art des Eintrags: | Bibliographie |
| Titel: | Probabilistic Forecast Combination for Anomaly Detection in Building Heat Load Time Series |
| Sprache: | Englisch |
| Publikationsjahr: | 2021 |
| Veranstaltungstitel: | 2021 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe) |
| Veranstaltungsort: | virtual Conference |
| Veranstaltungsdatum: | 18.10.2021-21.10.2021 |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | We consider the problem of automated anomaly detection for building level heat load time series. An anomaly detection model must be applicable to a diverse group of buildings and provide robust results on heat load time series with low signal-to-noise ratios, several seasonalities, and significant exogenous effects. We propose to employ a probabilistic forecast combination approach based on an ensemble of deterministic forecasts in an anomaly detection scheme that classifies observed values based on their probability under a predictive distribution. We show empirically that forecast based anomaly detection provides improved accuracy when employing a forecast combination approach. |
| Freie Schlagworte: | EnEff Campus LW |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Datentechnik > Energieinformationsnetze und Systeme (EINS) 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Datentechnik Forschungsfelder Forschungsfelder > Energy and Environment Forschungsfelder > Energy and Environment > Integrated Energy Systems |
| Hinterlegungsdatum: | 26 Jul 2021 10:56 |
| Letzte Änderung: | 09 Mai 2022 12:02 |
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