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
邢晓萱 1巩敦卫 2孙晓燕 1张勇 1梁睿3
作者信息
- 1. 中国矿业大学信息与控制工程学院,江苏省 徐州市 221116
- 2. 青岛科技大学自动化与电子工程学院,山东省 青岛市 266000
- 3. 中国矿业大学电气工程学院,江苏省 徐州市 221116
- 折叠
摘要
综合能源系统的运行场景存在极端模式,且含有异常数据,亟剧增加了综合能源负荷预测的难度.该文提出基于极端模式识别和误差重构的综合能源系统极端模式短期负荷预测方法,通过极端模式的识别,异常数据的检测,提高综合能源负荷预测的精度.首先,基于最小累积距离的综合能源负荷数据聚类,识别系统的极端模式;然后,利用深度学习模型的残差和聚类误差进行误差重构,检测异常数据;最后,采用改进的Stacking集成学习方法,进行极端模式的综合能源负荷预测.将所提方法应用于典型的综合能源系统,并与已有方法比较,实验结果表明,所提方法能够很好地解决极端模式的综合能源系统短期负荷预测问题.
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引用本文复制引用
基金项目
国家重点研发计划项目(2022YFE0199000)
国家自然科学基金项目(62133015)
出版年
2024
国家科技期刊平台
NSTL
万方数据