计算机与现代化Issue(10)：37-43,7.DOI:10.3969/j.issn.1006-2475.2025.10.007
基于DWT-SCINet-MDSC的电价预测混合模型
Hybrid Model for Electricity Price Prediction Based on DWT-SCINet-MDSC
摘要
Abstract
Due to the volatile and complex nonlinear characteristics of electricity prices,the prediction accuracy of existing mod-els is often inadequate.To improve prediction accuracy and dig deeper for valuable information within complex nonlinear charac-teristics,a hybrid model for electricity price prediction based on DWT-SCINet-MDSC is proposed.Firstly,Discrete Wavelet Transform(DWT)is employed by the model to decompose the data into sub-signals at different time scales.This process not only effectively filtered out high-frequency noise but also more significantly reduced data volatility,thereby enhancing the signal-to-noise ratio and rendering the data clearer and more stable.Secondly,multi-scale separable convolutions are utilized to capture rich information across different time scales while effectively minimizing the number of model parameters,thus accelerat-ing the training process.Lastly,to overcome the limitations of manual feature engineering,a feature weighting module is incorpo-rated to adjust the weights of key features,assigning greater importance to critical features for more efficient feature extraction.A simulation experiment was conducted on an electricity price dataset from a region in Australia.The results indicate that,com-pared with SCINet and other comparative models,the average absolute error is reduced by 23.29%.This demonstrates that the prediction performance of the DWT-SCINet-MDSC hybrid model is significantly improved.关键词
深度学习/电价预测/样本卷积交互/深度可分离卷积/离散小波变换Key words
deep learning/electricity price prediction/sample convolution interaction/depth separable convolution/discrete wavelet transform分类
动力与电气工程引用本文复制引用
李雪,魏延,李林骏..基于DWT-SCINet-MDSC的电价预测混合模型[J].计算机与现代化,2025,(10):37-43,7.基金项目
重庆市技术创新与应用发展专项重点项目(cstc2019jscx-mbdxX0061) （cstc2019jscx-mbdxX0061）
