![](bg9.png)
0.902
0.752
0.762
0.877
0.690
0.695
0.874
0.707
0.633
0.898
0.750
0.734
0.0 0.2 0.4 0.6 0.8 1.0
Performance
B-route 30-min/1 Wh A-route Estimated
Accuracy
Precision
(Unoccupied)
Recall
(Unoccupied)
Figure 10: Performance of occupancy detection by SVM.
Table 3: Contribution Ratio of Each Explanatory Variable
by Random Forests.
Variable B-route 30-min/1 Wh A-route Estimated
id 0.103 0.131 0.049 0.121
mean 0.183 0.165 0.149 0.173
max 0.211 0.236 0.072 0.208
min 0.149 0.138 0.116 0.130
range 0.129 0.084 0.028 0.032
std 0.093 0.088 0.033 0.023
hour 0.056 0.074 0.232 0.147
temp 0.005 0.073 0.316 0.160
season 0.070 0.009 0.006 0.007
tion and A-route readout tends to be large when con-
sumption is small. As a result, its contribution ratio of
explanatory variables regarding electricity consump-
tion decreased because it does not reflect the charac-
teristics of occupied and unoccupied household well.
In contrast, the estimated consumption data are con-
sidered to reflect the characteristics appropriately be-
cause its ratio of explanatory variables regarding elec-
tricity consumption is higher than that of A-route.
5 CONCLUSION
This paper proposed an actual consumption estima-
tion algorithm that detects occupancy with high accu-
racy using electricity consumption data with low reso-
lution. The proposed algorithm estimates actual con-
sumption using the cumulative consumption of the A-
route readout and the segmented line that exists within
the range of true cumulative consumption. The exper-
imental results of occupancy detection using the con-
sumption data estimated by the proposed algorithm
show an improvement in performance compared to
the result obtained with raw A-route readout. The re-
sults also show that the estimated consumption data
reflects the characteristics of occupied and unoccu-
pied states appropriately.
The proposed algorithm is expected to be useful
for various tasks such as profile analysis of household
attributes based on A-route data. Future work also
includes occupancy detection targeting Japanese do-
mestic households and efficient feature selection for
improving performance.
REFERENCES
Batra, N., Kelly, J., Parson, O., Dutta, H., Knottenbelt,
W., Rogers, A., Singh, A., and Srivastava, M. (2014).
Nilmtk: An open source toolkit for non-intrusive load
monitoring. In Proceedings of the 5th International
Conference on Future Energy Systems, pages 265–
276. ACM.
Beckel, C., Kleiminger, W., Cicchetti, R., Staake, T., and
Santini, S. (2014). The ECO data set and the perfor-
mance of non-intrusive load monitoring algorithms. In
Proceedings of the 1st ACM Conference on Embedded
Systems for Energy-Efficient Buildings, pages 80–89.
ACM.
Chang, H.-H., Lin, C.-L., and Lee, J.-K. (2010). Load
identification in nonintrusive load monitoring using
steady-state and turn-on transient energy algorithms.
In Computer Supported Cooperative Work in Design
(CSCWD), 2010 14th International Conference on,
pages 27–32. IEEE.
Chen, D., Barker, S., Subbaswamy, A., Irwin, D., and
Shenoy, P. (2013). Non-intrusive occupancy monitor-
ing using smart meters. In Proceedings of the 5th ACM
Workshop on Embedded Systems For Energy-Efficient
Buildings, pages 1–8. ACM.
Froehlich, J., Larson, E., Gupta, S., Cohn, G., Reynolds,
M., and Patel, S. (2011). Disaggregated end-use en-
ergy sensing for the smart grid. IEEE Pervasive Com-
puting, 10(1):28–39.
Hart, G. W. (1992). Nonintrusive appliance load monitor-
ing. Proceedings of the IEEE, 80(12):1870–1891.
Japkowicz, N. et al. (2000). Learning from imbalanced data
sets: A comparison of various strategies. In AAAI
Workshop on Learning from Imbalanced Data Sets,
volume 68, pages 10–15.
Kim, H., Marwah, M., Arlitt, M. F., Lyon, G., and Han,
J. (2011). Unsupervised disaggregation of low fre-
quency power measurements. In SDM, volume 11,
pages 747–758. SIAM.
Kleiminger, W., Beckel, C., and Santini, S. (2015). House-
hold occupancy monitoring using electricity meters.
In Proceedings of the 2015 ACM International Joint
Conference on Pervasive and Ubiquitous Computing,
pages 975–986. ACM.
Komatsu, H. and Nishio, K. (2015). How can smart data
analytics improve the way of information provision?
–the trend of methods and an exploratory analysis–
. In Proceedings of 8th International Conference on
Actual Consumption Estimation Algorithm for Occupancy Detection using Low Resolution Smart Meter Data
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