中国电机工程学报2024，Vol.44Issue(9)：3476-3488,中插11,14.DOI:10.13334/j.0258-8013.pcsee.222510
基于重构误差和极端模式识别的综合能源系统短期负荷预测
Short-term Load Forecasting of Integrated Energy System Based on Reconstruction Error and Extreme Patterns Recognition
摘要
Abstract
The operation scenarios of integrated energy systems have extreme patterns and contain abnormal data,which sharply increases the difficulty of integrated energy load forecasting.This paper aims to improve the accuracy of forecasting integrated energy loads by recognizing extreme patterns,detecting abnormal data,and proposing a method of short-term load forecasting of integrated energy systems based on reconstruction error and extreme pattern recognition.First,by clustering integrated energy load data based on the smallest cumulative distance,extreme patterns of the system are found.Then,error reconstruction is performed using clustering error and the residual of the deep learning model to detect anomalous data.Finally,the improved Stacking integrated learning method is used to forecast integrated energy loads in extreme patterns.The proposed method is tested against previous methods on a typical integrated energy system.The experimental results show that the proposed method is effective in addressing the issue of forecasting integrated energy loads with extreme patterns.关键词
综合能源系统/负荷预测/极端模式识别/重构误差/集成学习Key words
integrated energy system/load forecasting/extreme pattern recognition/reconstruction error/ensemble learning分类
信息技术与安全科学引用本文复制引用
邢晓萱,巩敦卫,孙晓燕,张勇,梁睿..基于重构误差和极端模式识别的综合能源系统短期负荷预测[J].中国电机工程学报,2024,44(9):3476-3488,中插11,14.基金项目
国家重点研发计划项目(2022YFE0199000) （2022YFE0199000）
国家自然科学基金项目(62133015). National Key R&D Program of China(2022YFE0199000) （62133015）
Project Supported by National Natural Science Foundation of China(62133015). （62133015）
