基于多变量相空间重构和径向基函数神经网络的综合能源系统电冷热超短期负荷预测  

Ultra-short-term Load Forecasting of Electrical,Cooling and Heating for Integrated Energy System Based on Multivariate Phase Space Reconstruction and Radial Basis Function Neural Network

作　　者：窦真兰 张春雁 许一洲 高煜焜 刘皓明[2] DOU Zhenlan;ZHANG Chunyan;XU Yizhou;GAO Yukun;LIU Haoming(State Grid Shanghai Integrated Energy Service Co.,Ltd.,Luwan District,Shanghai 200023,China;College of Energy and Electrical Engineering,Hohai University,Nanjing 211100,Jiangsu Province,China)

机构地区：[1]国网上海综合能源服务有限公司,上海市卢湾区200023 [2]河海大学能源与电气学院,江苏省南京市211100

出　　处：《电网技术》2024年第1期121-128,共8页Power System Technology

基　　金：国家电网有限公司总部科技项目(5400-202217177A-1-1-ZN)。

摘　　要：为解决能源危机问题,提高能源利用率,综合能源系统(integrated energy system,IES)成为发展创新型能源系统的重要方向。准确的多元负荷预测对IES的经济调度和优化运行有着重要的影响,而借助混沌理论能够进一步挖掘IES多元负荷潜在的耦合特性。提出了一种基于多变量相空间重构(multivariate phase space reconstruction,MPSR)和径向基函数神经网络(radial basis function neural network,RBFNN)相结合的IES超短期电冷热负荷预测模型。首先,分析了IES中能源子系统之间的耦合关系,运用Pearson相关性分析定量描述多元负荷和气象特征的相关性。然后,采用C-C法对时间序列进行MPSR以进一步挖掘电冷热负荷和气象特征在时间上的耦合特性。最后,利用RBFNN模型对电冷热负荷间耦合关系进行学习并预测。实验结果表明,所提方法有效挖掘并学习电冷热负荷在时间上的耦合特性,且在不同样本容量下具有良好且稳定的预测效果。In order to deal with the energy crisis and improve the energy utilization,the integrated energy system(IES)has become an important direction for developing the innovative energy systems.The accurate IES multivariate load forecasting is of great importance for the economic dispatch and optimal operation of the IES,and the coupling characteristics of the IES multivariate loads may be further explored by the chaos theory.In this paper,an IES ultra-short-term electrical,cooling and heating load forecasting model based on the combination of the multivariate phase space reconstruction(MPSR)and the radial basis function neural network(RBFNN)is proposed.First,the coupling relationship between the energy subsystems in the IES is analyzed,and the Pearson correlation analysis is applied to quantitatively describe the correlation between the electrical,cooling and heating loads and the meteorological characteristics.Then,the C-C method is used to perform the MPSR on the time series to further reflect the coupling characteristics of the electrical,cooling and heating loads and the meteorological characteristics on time.Finally,the RBFNN model is used to learn the coupling relationships among the multivariate loads for forecasting.The experimental results show that the proposed method effectively explores and learns the coupling characteristics of the multivariate loads on time,and has a good and stable forecasting effect under different sample sizes.

关 键 词：电冷热负荷预测 综合能源系统 多变量相空间重构 径向基函数神经网络 

分 类 号：TM721[电气工程—电力系统及自动化]