Non intrusive load identification method based on deep learning

Abstract

The current non-invasive load identification methods have limited recognition range and are prone to increasing time consumption. Therefore, a non-invasive load identification method based on deep learning is proposed. Based on actual recognition requirements and standards, extract recognition features, adopt multi-level processing forms, break through the limitations of recognition range, and set them as multi-level adaptive recognition nodes. We constructed a deep learning non-invasive load identification model and utilized GAGOA multi-objective recognition optimization to achieve non-invasive processing. The test results show that the time consumption of the non-invasive load identification unit obtained by applying the proposed method is controlled below 0.5 seconds, which is shorter. This indicates that the practical application effect of this method is better and has certain practical application value.