A Non-intrusive Load Decomposition Method Based on Data Modeling

Abstract

Most of the existing non-intrusive power load decomposition systems use the active power and reactive power of the electric load as the load characteristics. In the environment with a large load type and quantity, the decomposed effect is relatively poor, the accuracy is low, and the load characteristics are seriously lost. Aiming at this problem, this paper proposes a non-intrusive power load decomposition method using full current waveform as the characteristic of power load system. Firstly, in the data-driven framework, using the time series data of load voltage and current, the current model of each load is obtained by training RBF neural network based on the parameter optimization of support vector machine (SVM), and the load model library is formed. Then, genetic algorithm is used to find the combination of load model which is most similar to the actual current. The optimal combination of load model is the type and quantity of the actual load. In order to make the similarity evaluation more accurate, this paper establishes the weighted value of root mean square error, correlation coefficient and correlation entropy as the evaluated basis of similarity. The experimental results show that the load model library established by the decomposition method of this paper has better generalization ability and lower error, and the accuracy of decomposition load is 99.0%, which can effectively realize load decomposition.