Non-intrusive load decomposition based on CNN-LSTM hybrid deep learning model

TL;DR

This paper introduces a hybrid CNN-LSTM deep learning model for non-intrusive electrical load decomposition, demonstrating significant accuracy improvements over traditional methods on the UK-DALE dataset.

Contribution

The paper proposes a novel CNN-LSTM hybrid model that effectively captures spatial and temporal features for load decomposition, outperforming existing deep learning approaches.

Findings

Achieves 98% load decomposition accuracy on UK-DALE dataset.

Outperforms traditional deep learning and spectral decomposition methods.

Improves overall system performance in energy load monitoring.

Abstract

With the rapid development of science and technology, the problem of energy load monitoring and decomposition of electrical equipment has been receiving widespread attention from academia and industry. For the purpose of improving the performance of non-intrusive load decomposition, a non-intrusive load decomposition method based on a hybrid deep learning model is proposed. In this method, first of all, the data set is normalized and preprocessed. Secondly, a hybrid deep learning model integrating convolutional neural network (CNN) with long short-term memory network (LSTM) is constructed to fully excavate the spatial and temporal characteristics of load data. Finally, different evaluation indicators are used to analyze the mixture. The model is fully evaluated, and contrasted with the traditional single deep learning model. Experimental results on the open dataset UK-DALE show that the proposed algorithm improves the performance of the whole network system. In this paper, the proposed decomposition method is compared with the existing traditional deep learning load decomposition method. At the same time, compared with the obtained methods: spectral decomposition, EMS, LSTM-RNN, and other algorithms, the accuracy of load decomposition is significantly improved, and the test accuracy reaches 98%.