Feature selection identification method for non-invasive power load monitoring

Abstract

The invention relates to a feature selection identification method for non-invasive power load monitoring, which is characterized in that the method comprises the following steps: step 1, detecting aload start-stop event from a total active power of a power consumption scene and extracting a load event feature; step 2, establishing a training data set of a machine learning classify; step 3, carrying out feature selection processing on that train data set, and optimally configuring the features used for the train classification model; step 4, establishing a supervised classification model, andtraining that supervised classification model with the train data set in the step 2 and the feature selected in the step 3; step 5, obtaining that value of each characteristic of the unknown kind ofload event through the step 1, analyzing the machine learn classification model established in the step 4, obtaining the classification result, and finally realizing the detection and identification of the load event. The invention can improve the identification performance of the machine learning classification model established for the identification load event through the feature selection.

Description

technical field [0001] The invention belongs to the technical field of electricity consumption details monitoring, and relates to a feature selection identification method for electric load monitoring, in particular to a feature selection identification method for non-intrusive electric load monitoring. Background technique [0002] At present, electric energy is gradually becoming the main energy source of society. The power grid is the carrier of electric energy transmission, distribution and use. Maintaining the balance and stability of the power grid is the fundamental goal of power system planning, operation and management. To achieve this goal, on the one hand, from Starting from the power generation side, another aspect is to improve the forecast of the demand side load. In the context of the smart grid, the traditional monitoring of total electricity consumption information can no longer meet the needs. The concept of electricity consumption detail monitoring, on the...

AI Technical Summary

Problems solved by technology

Traditional identification methods use a variety of power and harmonic features, and these features are not screened, resulting in overfitting of the classification model and poor identification performance

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