Publications:Ranking Abnormal Substations by Power Signature Dispersion
From ISLAB/CAISR
| Title | Ranking Abnormal Substations by Power Signature Dispersion |
|---|---|
| Author | Ece Calikus and Sławomir Nowaczyk and Anita Sant'Anna and Stefan Byttner |
| Year | 2018 |
| PublicationType | Journal Paper |
| Journal | Energy Procedia |
| HostPublication | |
| Conference | 16th International Symposium on District Heating and Cooling, DHC2018, Hamburg, Germany, 9-12 September, 2018 |
| DOI | http://dx.doi.org/10.1016/j.egypro.2018.08.198 |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:1260587 |
| Abstract | The relation between heat demand and outdoor temperature (heat power signature) is a typical feature used to diagnose abnormal heat demand. Prior work is mainly based on setting thresholds, either statistically or manually, in order to identify outliers in the power signature. However, setting the correct threshold is a difficult task since heat demand is unique for each building. Too loose thresholds may allow outliers to go unspotted, while too tight thresholds can cause too many false alarms.Moreover, just the number of outliers does not reflect the dispersion level in the power signature. However, high dispersion is often caused by fault or configuration problems and should be considered while modeling abnormal heat demand.In this work, we present a novel method for ranking substations by measuring both dispersion and outliers in the power signature. We use robust regression to estimate a linear regression model. Observations that fall outside of the threshold in this model are considered outliers. Dispersion is measured using coefficient of determination R2 which is a statistical measure of how close the data are to the fitted regression line.Our method first produces two different lists by ranking substations using number of outliers and dispersion separately. Then, we merge the two lists into one using the Borda Count method. Substations appearing on the top of the list should indicate higher abnormality in heat demand compared to the ones on the bottom. We have applied our model on data from substations connected to two district heating networks in the south of Sweden. Three different approaches i.e. outlier-based, dispersion-based and aggregated methods are compared against the rankings based on return temperatures. The results show that our method significantly outperforms the state-of-the-art outlier-based method. © 2018 The Authors. Published by Elsevier Ltd. |
