基于数据增广的区域供热系统热力站负荷预测模型准确率提升方法研究

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万方数据
维普

中文摘要：开展了热力站数据生成模型研究,基于生成对抗网络和去噪扩散概率模型建立了数据生成模型,通过学习气象、室温、热力站运行数据的联合分布,对原始训练数据进行增广,为预测模型训练提供充足的数据支撑,从而提高预测模型的准确率.在北京市某热力站进行了验证和实际测试,结果表明:该方法可以将热力站一次侧电动调节阀开度和二次网供水温度的预测误差分别降低约7％和11％;同时,应用准确率提升后的负荷预测值进行供热量调节得到的预计室温与室温目标值之间的偏差可进一步降低5.44％.基于生成对抗网络的生成模型能够扩展预测模型的预测范围,基于去噪扩散概率模型的生成模型能够在原预测范围内提高预测模型的准确率.本文研究可为进一步提高区域供热系统热力站负荷预测能力与按需精准调控水平提供支撑.

外文标题：Research on accuracy improvement method of load forecast model for heat supply station in district heating systems based on data augmentation

外文摘要：This paper conducts a study on the data generative model of heat supply stations.A data generation model is established based on the generative adversarial network(GAN)and the denoising diffusion probabilistic model(DDPM).By learning the joint distribution of meteorological,room temperature,and operational data of the heat supply station,the original training data is augmented to provide sufficient data support for the training of the forecast model,thereby improving the accuracy of the forecast model.It has been verified and tested at a heat supply station in Beijing.The results show that this method can reduce the forecast errors of the opening degree of the primary side electric control valve of the heat supply station and the supply water temperature of the secondary network by about 7％and 11％,respectively.Meanwhile,the deviation between the expected room temperature obtained by adjusting the heat supply using the load forecast value with improved application accuracy and the target room temperature can be further reduced by 5.44％.The generative model based on GAN can expand the forecast range of the forecast model,and the generation model based on DDPM can improve the accuracy of the forecast model within the original forecast range.This study can provide support for further improving the load forecast ability and on-demand precise regulation level of heat supply stations in district heating systems.

外文关键词：

district heatingheat supply stationload forecastdata augmentationgenerative adversarial networkdenoising diffusion probabilistic modelgenerative model

作者：

白云、林小杰、钟崴、罗政、章宁

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作者单位：

北京市热力集团有限责任公司,北京

浙江大学,杭州

关键词：

区域供热热力站负荷预测数据增广生成对抗网络去噪扩散概率模型生成模型

基金：

基于数字孪生模型的工业园区综合能源系统灵活性优化技术研究及示范应用项目

项目编号：

2019YFE0126000

出版年：

2024

DOI：

10.19991/j.hvac1971.2024.09.22

暖通空调

亚太建设科技信息研究院中国建筑设计研究院中国建筑学会暖通空调分会

暖通空调

CSTPCD

影响因子：0.711

ISSN：1002-8501

年,卷(期)：2024.54(9)