## 6 Conclusions
**Cluster 2**  
\begin{tabular}{c c c c c c c c c} \hline
**Variable** & **Family** & **Bedrooms** & **Age_Person** & **Employ** & **House_Type** & **Age_House** & **Appliances** & **Floor_Area** \\ \hline number\_small\_peaks & 0.569 & 0.546 & 0.546 & 0.522 & 0.516 & 0.527 & 0.526 & 0.508 \\ s\_num\_peaks & 0.569 & 0.546 & 0.546 & 0.522 & 0.516 & 0.527 & 0.526 & 0.508 \\ f\_min\_wd\_ve & 0.508 & 0.51 & 0.51 & 0.511 & 0.552 & 0.512 & 0.503 & 0.537 \\ r\_morning\_wd\_we & 0.567 & 0.483 & 0.483 & 0.579 & 0.544 & 0.509 & 0.467 & 0.499 \\ t\_daily\_max & 0.502 & 0.506 & 0.506 & 0.494 & 0.507 & 0.523 & 0.516 & 0.55 \\ s\_cor\_we & 0.525 & 0.519 & 0.519 & 0.533 & 0.506 & 0.523 & 0.54 & 0.515 \\ s\_cor\_wd\_we & 0.548 & 0.541 & 0.541 & 0.562 & 0.511 & 0.503 & 0.544 & 0.542 \\ percent\_above\_base & 0.63 & 0.585 & 0.585 & 0.499 & 0.542 & 0.511 & 0.571 & 0.547 \\ s\_cor\_wd & 0.549 & 0.545 & 0.545 & 0.55 & 0.522 & 0.521 & 0.584 & 0.517 \\ t\_above\_mean & 0.569 & 0.556 & 0.556 & 0.558 & 0.523 & 0.541 & 0.542 & 0.516 \\ ts\_ad\_mean3h & 0.547 & 0.561 & 0.561 & 0.518 & 0.561 & 0.52 & 0.565 & 0.527 \\ t\_daily\_min & 0.544 & 0.549 & 0.549 & 0.53 & 0.535 & 0.507 & 0.555 & 0.548 \\ ts\_ac\_mean3h\_weekday & 0.589 & 0.565 & 0.565 & 0.497 & 0.52 & 0.51 & 0.574 & 0.526 \\ \hline
**Cluster 3**  
\begin{tabular}{c c c c c c c c c} \hline
**Variable** & **Family** & **Bedrooms** & **Age_Person** & **Employ** & **House_Type** & **Age_House** & **Appliances** & **Floor_Area** \\ \hline r\_mean\_max & 0.57 & 0.59 & 0.59 & 0.527 & 0.587 & 0.528 & 0.598 & 0.539 \\ t\_above\_base & 0.718 & 0.584 & 0.584 & 0.519 & 0.502 & 0.516 & 0.552 & 0.528 \\ r\_day\_night\_to\_min & 0.582 & 0.529 & 0.529 & 0.553 & 0.512 & 0.539 & 0.568 & 0.506 \\ wide\_peaks & 0.457 & 0.541 & 0.541 & 0.497 & 0.577 & 0.508 & 0.588 & 0.515 \\ c\_max & 0.758 & 0.635 & 0.635 & 0.636 & 0.545 & 0.546 & 0.634 & 0.557 \\ c\_wd\_max & 0.757 & 0.63 & 0.63 & 0.642 & 0.538 & 0.557 & 0.629 & 0.544 \\ c\_we\_max & 0.746 & 0.634 & 0.634 & 0.623 & 0.546 & 0.538 & 0.616 & 0.541 \\ s\_max\_avg & 0.783 & 0.647 & 0.647 & 0.65 & 0.551 & 0.549 & 0.652 & 0.543 \\ value\_above\_base & 0.779 & 0.65 & 0.65 & 0.609 & 0.55 & 0.559 & 0.632 & 0.54 \\ c\_sm\_max & 0.766 & 0.646 & 0.646 & 0.639 & 0.564 & 0.551 & 0.653 & 0.551 \\ c\_min & 0.658 & 0.641 & 0.641 & 0.574 & 0.578 & 0.52 & 0.635 & 0.533 \\ sm\_variety & 0.731 & 0.631 & 0.631 & 0.584 & 0.575 & 0.517 & 0.612 & 0.56 \\ \hline
**Cluster 4** \begin{tabular}{c c c c c c c c} \hline
**Variable** & **Family** & **Bedrooms** & **Age_Person** & **Employ** & **House_Type** & **Age_House** & **Appliances** & **Floor_Area** \\ \hline c\_wd\_min & 0.661 & 0.647 & 0.647 & 0.572 & 0.585 & 0.519 & 0.661 & 0.514 \\ c\_we\_evening & 0.737 & 0.649 & 0.649 & 0.63 & 0.573 & 0.541 & 0.619 & 0.554 \\ \hline \end{tabular}   
**Cluster 5**  
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* _[PERSON] et al. (2018)_ [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Heuristics-Based Detection of Abnormal Energy Consumption. In _International Conference on Smart Grid Inspired Future Technologies_; [PERSON], [PERSON], [PERSON], [PERSON]; Springer: Cham, Switzerland, 2018; pp. 21-31. * _[PERSON] et al. (2016)_ [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Creating a Detailed Energy Breakdown from just the Monthly Electricity Bill. In Proceedings of the 3 rd International NILM Workshop, San Francisco, CA, USA, 14-15 May 2016. * _[PERSON] et al. (2016)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] Collect, compare, and score: A generic data-driven anomaly detection method for buildings.