Load Prediction Based on Hybrid Model of VMD-mRMR-BPNN-LSSVM

• Published in: Complexity (New York, N.Y.) Vol. 2020; no. 2020; pp. 1 - 20
• Main Authors: Hou, Jinwang, Chao, Hailiang, He, Qiang, Li, Meng, Li, Pingli, Liu, Hongchi, Zhang, Gang, Zhang, Jiangbin
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 2020; Hindawi; John Wiley & Sons, Inc; Hindawi Limited; Hindawi-Wiley
• Subjects: Accuracy; Algorithms; Artificial neural networks; Autoregressive moving-average models; China; Correlation analysis; Data processing; Decomposition; Electric power systems; Electricity consumption; Electricity distribution; Empirical analysis; Forecasting; Low frequencies; Mathematical models; Methods; Neural networks; Power; Redundancy; Support vector machines; Time series; Variation; Volatility; Wavelet transforms; Weather; China
• ISSN: 1076-2787; 1099-0526
• DOI: 10.1155/2020/6940786

Power system load forecasting is an important part of power system scheduling. Since the power system load is easily affected by environmental factors such as weather and time, it has high volatility and multi-frequency. In order to improve the prediction accuracy, this paper proposes a load forecasting method based on variational mode decomposition (VMD) and feature correlation analysis. Firstly, the original load sequence is decomposed using VMD to obtain a series of intrinsic mode function (IMF), it is referred to below as a modal component, and they are divided into high frequency, intermediate frequency, and low frequency signals according to their fluctuation characteristics. Then, the feature information related to the power system load change is collected, and the correlation between each IMF and each feature information is analyzed using the maximum relevance minimum redundancy (mRMR) based on the mutual information to obtain the best feature set of each IMF. Finally, each component is input into the prediction model together with its feature set, in which back propagation neural network (BPNN) is used to predict high-frequency components, least square-support vector machine (LS-SVM) is used to predict intermediate and low frequency components, and BPNN is also used to integrate the prediction results to obtain the final load prediction value, and compare the prediction results of method in this paper with that of the prediction models such as autoregressive moving average model (ARMA), LS-SVM, BPNN, empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and VMD. This paper carries out an example analysis based on the data of Xi’an Power Grid Corporation, and the results show that the prediction accuracy of method in this paper is higher.