智能系统学报2025，Vol.20Issue(4)：858-870,13.DOI:10.11992/tis.202406008
渐进式分层特征提取的综合能源多任务负荷预测
Multi-task load forecasting of integrated energy based on progressive layered feature extraction
摘要
Abstract
Due to the complex coupling relationship between electric,cold and heat loads in an integrated energy sys-tem,it is difficult for traditional multi-task learning models to learn effective multi-load coupling characteristics,which may lead to reduced prediction accuracy.In this paper,a comprehensive energy multi-task load forecasting model with progressive layered feature extraction is proposed,considering the complex coupling relationship of multiple loads.Firstly,divide the annual data by season and analyze the coupling strength between electricity,cooling,and heating loads in each season.Then,by using variational mode decomposition,the historical load sequence is decomposed into multiple components of different frequencies,which can better explore the deep time series features of multiple loads.Finally,the coupling features of multiple loads are extracted progressively and the influence weights of the coupling fea-tures on the prediction results are dynamically allocated to avoid the degradation of the model prediction accuracy when the coupling features are invalid.Experimental results show that the proposed model has better performance in terms of prediction accuracy under different multi-component load coupling intensities.The conclusion can be used to guide the process of load forecasting of integrated energy.关键词
负荷预测/综合能源/多任务学习/多元负荷/渐进式分层/特征提取/最大信息系数/变分模态分解Key words
load forecasting/integrated energy/multi-task learning/multiple loads/progressive layered/feature extrac-tion/maximum information coefficient/variational mode decomposition分类
信息技术与安全科学引用本文复制引用
王德文,安涵,张林飞,赵文清..渐进式分层特征提取的综合能源多任务负荷预测[J].智能系统学报,2025,20(4):858-870,13.基金项目
国家自然科学基金项目(62371188). （62371188）
