摘要
针对综合能源系统中多元负荷的随机性和高波动性所带来的挑战,现有的负荷预测方法通常难以实现高精度和稳定的预测效果。为解决这一问题,提出一种基于模态分解和多模型融合的IES短期负荷预测方法。首先,利用最大互信息系数对输入特征进行筛选,旨在有效识别与负荷变化相关的关键因素;其次,将样本熵结合互信息为适应度函数,采用指数三角优化算法获得VMD的最优参数组合,从而实现对IES负荷的有效分解,得到多个本征模态函数;接着,采用排列熵对分解结果进行筛选,提取出反映负荷变化特征的低频和高频分量;最后,采用BiLSTM网络对低频分量进行预测,并通过BiTCN-LPTransformer-BiGRU模型对高频分量的预测,将各分量的预测结果叠加得到最终预测结果。通过对实际负荷数据验证,以春季电负荷为例,该模型的RMSE、R2、MAPE分别为118.394kW、0.991和0.351%,相较于传统模型,显著提高了预测精度,验证所提方法的有效性。
To address the challenges posed by the randomness and high volatility of multi-loads in integrated energy systems,existing load forecasting methods often struggle to achieve high accuracy and stable prediction performance.To overcome this issue,this paper proposes a short-term load forecasting method for IES based on modal decomposition and multi-model fusion.First,the maximum mutual information coefficient is used for feature selection,aiming to effectively identify key factors closely related to load variation.Next,sample entropy combined with mutual information is employed as the fitness function,and the exponential triangular optimization algorithm is applied to obtain the optimal parameter combination for variational mode decomposition(VMD),enabling effective decomposition of IES loads into multiple intrinsic mode functions.Then,permutation entropy is used to filter the decomposition results and extract low-frequency and high-frequency components that reflect the load variation characteristics.Finally,a BiLSTM network is used to predict the low-frequency components,while a BiTCN-LPTransformer-BiGRU model is applied to forecast the high-frequency components.The final load prediction is obtained by aggregating the predictions of all components.Verification using actual load data,specifically for spring electricity load,shows that the model achieves an RMSE of 118.394 kW,an R 2 of 0.991,and an MAPE of 0.351%.Compared to traditional models,this approach significantly improves prediction accuracy,validating the effectiveness of the proposed method.
作者
李大华
赵志成
田禾
高强
Li Dahua;Zhao Zhicheng;Tian He;Gao Qiang(Tianjin Key Laboratory of Control Theory&Application in Complicated Systems,Tianjin University of Technology,Tianjin 300384,China)
出处
《电子测量技术》
北大核心
2025年第17期81-93,共13页
Electronic Measurement Technology
基金
天津市企业科技特派员项目(22YDTPJC00170)资助。
关键词
综合能源系统
模态分解
最大互信息系数
指数三角优化算法
负荷预测
integrated energy system
modal decomposition
maximum mutual information coefficient
exponential triangular optimization algorithm
load forecasting
