中国电力2025，Vol.58Issue(6)：19-32,14.DOI:10.11930/j.issn.1004-9649.202410086
基于ICEEMDAN-PE和IDBO-Informer组合模型的短期负荷预测
Short-term Load Forecasting Based on a Combined ICEEMDAN-PE and IDBO-Informer Model
摘要
Abstract
To address the problems of insufficient noise processing,limited feature extraction ability and complex model training when using traditional methods to deal with complex load data,an innovative forecasting model based on a combined ICEEMDAN-PE and IDBO-Informer is proposed.Firstly,the raw load data were preprocessed using wavelet soft-threshold denoising algorithm to reduce noise interference.Secondly,ICEEMDAN was used for multi-scale decomposition of load data to precisely characterize load features,and the permutation entropy was used to evaluate the component complexity.Finally,an improved Dung Beetle Optimizer(IDBO)was proposed by synergistically integrating chaotic and opposition-based learning strategies for population initialization,incorporating adaptive step size,convex lens opposition imaging,and stochastic differential mutation strategies.This approach optimizes hyperparameters of the Informer forecasting model,significantly enhancing computational efficiency and prediction accuracy.The experimental results show that the model performs well in short-term load forecasting,with MAE of 81.3 MW(the original load data range is about 500 MW to 1500 MW),RMSE of 109.2 MW and R2 score of 0.991,which is much better than the traditional method,and fully verifies the innovation and superiority of the model.关键词
负荷预测/ICEEMDAN/改进蜣螂优化算法/InformerKey words
load forecasting/ICEEMDAN/improved dung beetle optimizer algorithm/Informer引用本文复制引用
于多,曹燚,王海荣,赵翱东,曹倩..基于ICEEMDAN-PE和IDBO-Informer组合模型的短期负荷预测[J].中国电力,2025,58(6):19-32,14.基金项目
国家自然科学基金资助项目(42305158) （42305158）
第二批国家级职业教育教师教学创新团队课题研究项目(ZI2021030103) （ZI2021030103）
江苏省高等教育教改研究立项重点课题(2021JSJG197). This work is supported by National Natural Science Foundation of China(No.42305158) （2021JSJG197）
The Second Batch of National Vocational Education Teachers'Teaching Innovation Team Research Project(No.ZI2021030103) （No.ZI2021030103）
Key Project of Jiangsu Higher Education Reform Research Project(No.2021JSJG197). （No.2021JSJG197）
