Abstract
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.
Highlights
Load monitoring technology is of great significance to the demand side management (DSM) [1].It can transfer the collected user’s power consumption information to the grid to improve the efficiency of power grid utilization, and it can help users to adjust their electricity consumption habits [2].Load monitoring technology contains two categories: “intrusive” and “non-intrusive”
This paper chose F-score and accuracy as the evaluation criteria of the algorithm, which will be introduced in the following. This accuracy is used to evaluate the overall level of classification algorithm, representing the correct proportion of prediction results
The model uses Random Forest (RF) as classification algorithm; (2) feature importance is used as a criterion to select the most suitable features
Summary
Load monitoring technology is of great significance to the demand side management (DSM) [1].It can transfer the collected user’s power consumption information to the grid to improve the efficiency of power grid utilization, and it can help users to adjust their electricity consumption habits [2].Load monitoring technology contains two categories: “intrusive” and “non-intrusive”. Load monitoring technology is of great significance to the demand side management (DSM) [1] It can transfer the collected user’s power consumption information to the grid to improve the efficiency of power grid utilization, and it can help users to adjust their electricity consumption habits [2]. Non-intrusive load monitoring (NILM) can monitor the whole internal load of the user by placing a monitoring device at the entrance of the user’s power supply, which has little impact on the user.
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