TY - GEN
T1 - A comprehensive feature study for appliance recognition on high frequency energy data
AU - Kahl, Matthias
AU - Haq, Anwar Ul
AU - Kriechbaumer, Thomas
AU - Jacobsen, Hans Arno
N1 - Publisher Copyright:
© 2017 ACM.
PY - 2017/5/16
Y1 - 2017/5/16
N2 - Awareness about the energy consumption of appliances can help to save energy in households. Non-intrusive Load Monitoring (NILM) is a feasible approach to provide consumption feedback at appliance level. In this paper, we evaluate a broad set of features for electrical appliance recognition, extracted from high frequency start-up events. these evaluations were applied on several existing high frequency energy datasets. To examine clean signatures, we ran all experiments on two datasets that are based on isolated appliance events; more realistic results were retrieved from two real household datasets. Our feature set consists of 36 signatures from related work including novel approaches, and from other research fields. the results of this work include a stand-alone feature ranking, promising feature combinations for appliance recognition in general and appliance-wise performances.
AB - Awareness about the energy consumption of appliances can help to save energy in households. Non-intrusive Load Monitoring (NILM) is a feasible approach to provide consumption feedback at appliance level. In this paper, we evaluate a broad set of features for electrical appliance recognition, extracted from high frequency start-up events. these evaluations were applied on several existing high frequency energy datasets. To examine clean signatures, we ran all experiments on two datasets that are based on isolated appliance events; more realistic results were retrieved from two real household datasets. Our feature set consists of 36 signatures from related work including novel approaches, and from other research fields. the results of this work include a stand-alone feature ranking, promising feature combinations for appliance recognition in general and appliance-wise performances.
KW - Appliance Recognition
KW - Feature Study
KW - High Frequency
KW - Load Information Retrieval
KW - NIALM
KW - NILM
UR - http://www.scopus.com/inward/record.url?scp=85021414589&partnerID=8YFLogxK
U2 - 10.1145/3077839.3077845
DO - 10.1145/3077839.3077845
M3 - Conference contribution
AN - SCOPUS:85021414589
T3 - e-Energy 2017 - Proceedings of the 8th International Conference on Future Energy Systems
SP - 121
EP - 131
BT - e-Energy 2017 - Proceedings of the 8th International Conference on Future Energy Systems
PB - Association for Computing Machinery, Inc
T2 - 8th ACM International Conference on Future Energy Systems, e-Energy 2017
Y2 - 16 May 2017 through 19 May 2017
ER -
