基于VMD-GRU网络大型公共建筑冷负荷预测
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西安建筑科技大学

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基金项目:

基于大数据的绿色建筑能耗管理分析平台关键技术及示范(2017ZDL-SF-16-5);碑林区应用技术研发类项目(GX1903)


Research on Cold Load Forecasting Model of Large Public Buildings Based on VMD-GRU Network
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Affiliation:

1.Xi &2.amp;3.#39;4.&5.an University of Architecture and Technology;6.Xi ''an University of Architecture and Technology

Fund Project:

Key technology and demonstration of green building energy management analysis platform based on big data (2017ZDL-SF-16-5); Beilin District Applied Technology R&D Project (GX1903)

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    摘要:

    由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,提出了一种集成变分模态分解(VMD)和门控循环单元网络(GRU)的VMD-GRU模型。预测方法过程如下:1)对原始数据进行相关性分析,挑选出相关性高的进行预测;2)使用VMD将原始数据序列分解为独立固有模式函数;3)使用GRU对每个分量进行预测;4)将分量预测结果相加得出冷负荷预测值。为了验证模型的有效性,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,可以有效地解决梯度消失、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的数据支撑。

    Abstract:

    Due to the inherent complexity and irregularity of the cold load time series, problems such as gradient disappearance, modal aliasing and over-fitting are prone to occur in the prediction process. It is still a difficult task to predict the cold load of large public buildings. To solve this problem and improve the prediction accuracy, the VMD-GRU model is proposed. The proposed model was tested using real data from large public buildings. The prediction method process is as follows: 1) Correlation analysis of the original data, selection of highly correlated predictions; 2) Decomposition of the original data sequence into independent eigenmode functions using VMD; 3) Prediction of each component using GRU ; 4) Add the component prediction results to obtain the cold load prediction value. In order to verify the validity of the model, a large public building in Xi'an is taken as an example to analyze the energy consumption and compare it with other prediction models such as BP, GRU, EMD-BP, VMD-BP, EMD-GRU. The experimental results show that the proposed model can effectively solve the problems of gradient disappearance, modal aliasing and over-fitting, and accurately predict the cold load of large public buildings.

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  • 收稿日期:2020-07-13
  • 最后修改日期:2020-09-06
  • 录用日期:2020-10-09
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