Robust energy disaggregation using appliance-specific temporal contextual information

Pascal Schirmer, Iosif Mporas, Akbar Sheikh-Akbari

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
34 Downloads (Pure)


An extension of the baseline non-intrusive load monitoring approach for energy disaggregation using temporal contextual information is presented in this paper. In detail, the proposed approach uses a two-stage disaggregation methodology with appliance-specific temporal contextual information in order to capture time-varying power consumption patterns in low-frequency datasets. The proposed methodology was evaluated using datasets of different sampling frequency, number and type of appliances. When employing appliance-specific temporal contextual information, an improvement of 1.5% up to 7.3% was observed. With the two-stage disaggregation architecture and using appliance-specific temporal contextual information, the overall energy disaggregation accuracy was further improved across all evaluated datasets with the maximum observed improvement, in terms of absolute increase of accuracy, being equal to 6.8%, thus resulting in a maximum total energy disaggregation accuracy improvement equal to 10.0%.
Original languageEnglish
Article number3
JournalEURASIP Journal on Advances in Signal Processing
Issue number1
Publication statusPublished - 11 Feb 2020


  • Contextual temporal information
  • Energy disaggregation
  • Load monitoring
  • Non-intrusive
  • Two-stage energy disaggregation


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