This page has only limited features, please log in for full access.
Non-intrusive load monitoring (NILM) is a core technology for demand response (DR) and energy conservation services. Traditional NILM methods are rarely combined with practical applications, and most studies aim to disaggregate the whole loads in a household, which leads to low identification accuracy. In this method, the event detection method is used to obtain the switching event sets of all loads, and the power consumption curves of independent unknown electrical appliances in a period are disaggregated by utilizing comprehensive features. A linear discriminant classifier group based on multi-feature global similarity is used for load identification. The uniqueness of our algorithm is that it designs an event detector based on steady-state segmentation and a linear discriminant classifier group based on multi-feature global similarity. The simulation is carried out on an open source data set. The results demonstrate the effectiveness and high accuracy of the multi-feature integrated classification (MFIC) algorithm by using the state-of-the-art NILM methods as benchmarks.
Jinying Yu; Yuchen Gao; Yuxin Wu; Dian Jiao; Chang Su; Xin Wu. Non-Intrusive Load Disaggregation by Linear Classifier Group Considering Multi-Feature Integration. Applied Sciences 2019, 9, 3558 .
AMA StyleJinying Yu, Yuchen Gao, Yuxin Wu, Dian Jiao, Chang Su, Xin Wu. Non-Intrusive Load Disaggregation by Linear Classifier Group Considering Multi-Feature Integration. Applied Sciences. 2019; 9 (17):3558.
Chicago/Turabian StyleJinying Yu; Yuchen Gao; Yuxin Wu; Dian Jiao; Chang Su; Xin Wu. 2019. "Non-Intrusive Load Disaggregation by Linear Classifier Group Considering Multi-Feature Integration." Applied Sciences 9, no. 17: 3558.
Non-intrusive load monitoring (NILM) is an effective method to optimize energy consumption patterns. Since the concept of NILM was proposed, extensive research has focused on energy disaggregation or load identification. The traditional method is to disaggregate mixed signals, and then identify the independent load. This paper proposes a multi-label classification method using Random Forest (RF) as a learning algorithm for non-intrusive load identification. Multi-label classification can be used to determine which categories data belong to. This classification can help to identify the operation states of independent loads from mixed signals without disaggregation. The experiments are conducted in real environment and public data set respectively. Several basic electrical features are selected as the classification feature to build the classification model. These features are also compared to select the most suitable features for classification by feature importance parameters. The classification accuracy and F-score of the proposed method can reach 0.97 and 0.98, respectively.
Xin Wu; Yuchen Gao; Dian Jiao; Wu; Gao; Jiao. Multi-Label Classification Based on Random Forest Algorithm for Non-Intrusive Load Monitoring System. Processes 2019, 7, 337 .
AMA StyleXin Wu, Yuchen Gao, Dian Jiao, Wu, Gao, Jiao. Multi-Label Classification Based on Random Forest Algorithm for Non-Intrusive Load Monitoring System. Processes. 2019; 7 (6):337.
Chicago/Turabian StyleXin Wu; Yuchen Gao; Dian Jiao; Wu; Gao; Jiao. 2019. "Multi-Label Classification Based on Random Forest Algorithm for Non-Intrusive Load Monitoring System." Processes 7, no. 6: 337.