基于VMD-GRU网络大型公共建筑冷负荷预测

基于冷负荷时间序列固有的复杂性和不规则性，针对预测过程中容易出现梯度消失、模态混叠和过拟合等问题，提出一种集成变分模态分解（variational mode decomposition,VMD）和门控循环单元网络（gated recurrent unit,GRU）的VMD-GRU模型。对原始数据进行相关性分析，挑选出相关性高的进行预测；使用VMD将原始数据序列分解为独立固有模式函数；使用GRU对每个分量进行预测；将分量预测结果相加得出冷负荷预测值。为验证模型的有效性，以西安某大型公共建筑为例进行能耗分析，并与BP、 GRU、EMD-BP、VMD-BP、EMD-GRU等其他预测模型进行对比。实验结果表明，提出的VMD-GRU模型可有效解决梯度消失、模态混叠和过拟合等问题，预测精度显著提高，预测效果优于其它预测模型，符合大型公共建筑冷负荷的变化规律，为节能优化提供有力数据支撑。

Research on cold load forecasting model of large public buildings based on VMD-GRU network cold load forecasting model of large public buildings based on VMD-GRU network

Due to the inherent complexity and irregularity of cold load time series data, problems such as gradient disappearance, modal aliasing and over-fitting are prone to occur during the prediction process. Predicting the cold load of large public buildings remains a challenging task. To solve this problem and improve the prediction accuracy, the VMD-GRU model is proposed in this study. Real data from large public buildings were utilized to test the proposed model. The prediction process involves the following steps: 1) Correlation analysis of the original data and selection of highly correlated predictors; 2) Decomposition of the original data sequence into independent eigenmode functions using VMD; 3) Prediction of each component using GRU ; 4) Aggregation of component prediction results to obtain the cold load prediction value. To validate the model''s effectiveness, a large public building in Xi''an is taken as an example for energy consumption analysis. The results are compared with other prediction models, including BP, GRU, EMD-BP, VMD-BP, EMD-GRU. Experimental results show that the proposed model effectively solves the problems, such as gradient disappearance, modal aliasing and over-fitting, accurately predicting the cold load of large public buildings.