TY - GEN
T1 - Significance of Classifier and Feature Selection in Automatic Identification of Electrical Appliances
AU - Ghorbanpour, Samira
AU - Mallipeddi, Rammohan
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - In non-intrusive load monitoring, identification of electrical loads based on single point measurement of different energy related parameters plays a significant role. In literature, different conventional features such as true power, reactive power, RMS voltage, RMS current, phase angle and frequency in addition to the non-conventional features were employed. In addition, a variety of classifiers such as k-nearest neighbors (k-NN), support vector machine (SVM), random forest and Gaussian mixture models (GMM) have been employed. In this paper, we demonstrate that the classification performance strongly depends on the classifier and associated features selected. The experiments are performed on ACS-F2 Database of Appliance Consumption Signatures consisting of 225 devices belonging to 15 different categories.
AB - In non-intrusive load monitoring, identification of electrical loads based on single point measurement of different energy related parameters plays a significant role. In literature, different conventional features such as true power, reactive power, RMS voltage, RMS current, phase angle and frequency in addition to the non-conventional features were employed. In addition, a variety of classifiers such as k-nearest neighbors (k-NN), support vector machine (SVM), random forest and Gaussian mixture models (GMM) have been employed. In this paper, we demonstrate that the classification performance strongly depends on the classifier and associated features selected. The experiments are performed on ACS-F2 Database of Appliance Consumption Signatures consisting of 225 devices belonging to 15 different categories.
KW - appliance identification
KW - appliance load monitoring
KW - feature selection
KW - non-intrusive load monitoring
UR - https://www.scopus.com/pages/publications/85062213350
U2 - 10.1109/SMC.2018.00709
DO - 10.1109/SMC.2018.00709
M3 - Conference contribution
AN - SCOPUS:85062213350
T3 - Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
SP - 4184
EP - 4189
BT - Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
Y2 - 7 October 2018 through 10 October 2018
ER -
