TY - GEN
T1 - A hybrid approach for intrusive appliance load monitoring in smart home
AU - Nguyen, Vanh Khuyen
AU - Phan, Minh Hieu
AU - Zhang, Wei Emma
AU - Sheng, Quan Z.
AU - Vo, Trung Duc
PY - 2020
Y1 - 2020
N2 - Appliance Load Monitoring (ALM) has become a crucial task in energy sector since the residential loads have been ever-increasing in the recent years. Several studies have been undertaken to monitor energy consumption of household appliances while also analyze the power data to obtain more useful insights of consumers' behaviors. The remaining challenge of the recent approaches is automatic appliance recognition. In this work, we propose a novel hybrid method which includes two main processes, namely the feature importance process and the appliance identification process. In the first phase, feature importance process extracts the temporal trends. We then replace the classification layer of Convolutional Neural Network (CNN) by the SVM classifier; thereby achieving a set of important features which is data input for the next phase. After that, we set the CNN's weights based on the analyzed feature importance of SVM, instead of initializing weights randomly. As a result, the proposed method of this study outperformed other approaches with more than 90% for both of accuracy and macro F1-score.
AB - Appliance Load Monitoring (ALM) has become a crucial task in energy sector since the residential loads have been ever-increasing in the recent years. Several studies have been undertaken to monitor energy consumption of household appliances while also analyze the power data to obtain more useful insights of consumers' behaviors. The remaining challenge of the recent approaches is automatic appliance recognition. In this work, we propose a novel hybrid method which includes two main processes, namely the feature importance process and the appliance identification process. In the first phase, feature importance process extracts the temporal trends. We then replace the classification layer of Convolutional Neural Network (CNN) by the SVM classifier; thereby achieving a set of important features which is data input for the next phase. After that, we set the CNN's weights based on the analyzed feature importance of SVM, instead of initializing weights randomly. As a result, the proposed method of this study outperformed other approaches with more than 90% for both of accuracy and macro F1-score.
KW - Appliance Load Monitoring
KW - Convolutional Neural Network
KW - Support Vector Machine
KW - Internet of Things
UR - http://www.scopus.com/inward/record.url?scp=85091979203&partnerID=8YFLogxK
U2 - 10.1109/SmartIoT49966.2020.00031
DO - 10.1109/SmartIoT49966.2020.00031
M3 - Conference proceeding contribution
AN - SCOPUS:85091979203
T3 - Proceedings - 2020 IEEE International Conference on Smart Internet of Things, SmartIoT 2020
SP - 154
EP - 160
BT - Proceedings - 2020 IEEE International Conference on Smart Internet of Things, SmartIoT 2020
PB - Institute of Electrical and Electronics Engineers (IEEE)
CY - Los Alamitos, California
T2 - 4th IEEE International Conference on Smart Internet of Things, SmartIoT 2020
Y2 - 14 August 2020 through 16 August 2020
ER -