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Since the refrigerator continuously works, we use its power consumption pattern to profile user¡¯s power consumption
and identify his/her usage behaviour,
We use the power data points of the refrigerator in three months to train our model and extract the patterns,
Workdays and weekends/holidays are categorized into two separate categories and for each category, the similarity between the power usage time series of each day and the other days¡¯ is measured by DTW,
Two profiles are defined for these load types based on the DTW matrix, namely workday load profile (WoLP) and
weekend/holiday load profile (WeLP),
For each profile, a set of usage time series is extracted, in which has minimum Euclidean distance to other days¡¯ load data at the same category,
As shown in Table 1, in workday profile, the extracted load patterns set averagely has 1170.67 DTW distance to other load data time series and the load patterns set in weekend profile has 1244.75 DTW distance to other load data time series,
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IEEE/ICACT20230034 Slide.13
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