电力建设2024，Vol.45Issue(12)：162-173,12.DOI:10.12204/j.issn.1000-7229.2024.12.013
结合CNN与软共享机制的综合能源系统多元负荷预测
Multivariate Load Forecasting of Integrated Energy Systems Based on Convolutional Neural Network and Soft Sharing Mechanism
摘要
Abstract
Integrated energy systems can improve energy utilization efficiency and reduce carbon emissions;accurate load forecasting is an important prerequisite for its operation scheduling and energy control.In this study,a multivariate load-coupled forecasting model(CNN-MMoE-LSTM)is proposed to achieve higher accuracy in load forecasting.The model is based on convolutional neural networks(CNN),integrating multi-gate mixed expert models and long short-term memory neural network models.The model achieves deep coupling of cold,thermal,and power load relationships and significantly improves the accuracy of multivariate load forecasting through the organic combination of CNNs and soft sharing mechanism for multi-task learning.The results indicate that the proposed model can further improve the joint prediction accuracy of multiple loads.The root mean square error of electricity,heat,and cooling loads is reduced by 5.86%,5.98%,and 2.67%,respectively,compared to the long short-term neural memory network model based on multi-gate hybrid expert models(MMoE-LSTM).关键词
综合能源系统/负荷预测/机器学习/多任务学习/软共享机制Key words
integrated energy system/load forecasting/machine learning/multi task learning/soft sharing mechanism分类
信息技术与安全科学引用本文复制引用
葛众,隆交凤,李健,解通..结合CNN与软共享机制的综合能源系统多元负荷预测[J].电力建设,2024,45(12):162-173,12.基金项目
This work is supported by the National Natural Science Foundation of China(No.52106017) （No.52106017）
Beijing Natural Science Foundation(No.3222031) （No.3222031）
Yunnan Province Applied Basic Research Program Youth Project(No.202001BB050070)and Funding support for the'Xingdian Talent Support Program'Project. 国家自然科学基金青年科学基金项目(52106017) （No.202001BB050070）
北京市自然科学基金面上项目(3222031) （3222031）
云南省应用基础研究计划青年项目(202001BB050070) （202001BB050070）
'兴滇英才支持计划'项目 （）
