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)
Conference or Workshop Item, Bibliographie
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.
| Item Type: | Conference or Workshop Item |
|---|---|
| Erschienen: | 2021 |
| Creators: | Beykirch, Mario ; Janke, Tim ; Tayeche, Imed ; Steinke, Florian |
| Type of entry: | Bibliographie |
| Title: | Probabilistic Forecast Combination for Anomaly Detection in Building Heat Load Time Series |
| Language: | English |
| Date: | 2021 |
| Event Title: | 2021 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe) |
| Event Location: | virtual Conference |
| Event Dates: | 18.10.2021-21.10.2021 |
| Corresponding Links: | |
| 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. |
| Uncontrolled Keywords: | EnEff Campus LW |
| Divisions: | 18 Department of Electrical Engineering and Information Technology 18 Department of Electrical Engineering and Information Technology > Institute of Computer Engineering > Energy Information Networks and Systems Lab (EINS) 18 Department of Electrical Engineering and Information Technology > Institute of Computer Engineering Forschungsfelder Forschungsfelder > Energy and Environment Forschungsfelder > Energy and Environment > Integrated Energy Systems |
| Date Deposited: | 26 Jul 2021 10:56 |
| Last Modified: | 09 May 2022 12:02 |
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| Suche nach Titel in: | TUfind oder in Google |
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