Heat Load Prediction of Building Rooms Using Only the Whole Building Data via Heat Allocation Approach

  • TAN Xin ,
  • WANG Yahui ,
  • SUN Guoxin ,
  • WU Linfeng ,
  • YU Qihui ,
  • YU Yongheng
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  • 1. School of Mechanical Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China
    2. Ordos Energy Research Institute, Peking University, Ordos 017000, China
    3. New Energy and Equipment Laboratory, Inner Mongolia University of Science and Technology, Baotou 014010, China

网络出版日期: 2025-05-06

基金资助

This research was supported by the National Natural Science Foundation of China, 61765012; Natural Science Foundation of Inner Mongolia Autonomous Region, 2023MS05047; Basic research funds for universities directly under the Inner Mongolia Autonomous Region (2023RCTD011, 2023YXXS012). The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

版权

Science Press, Institute of Engineering Thermophysics, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2025

Heat Load Prediction of Building Rooms Using Only the Whole Building Data via Heat Allocation Approach

  • TAN Xin ,
  • WANG Yahui ,
  • SUN Guoxin ,
  • WU Linfeng ,
  • YU Qihui ,
  • YU Yongheng
Expand
  • 1. School of Mechanical Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China
    2. Ordos Energy Research Institute, Peking University, Ordos 017000, China
    3. New Energy and Equipment Laboratory, Inner Mongolia University of Science and Technology, Baotou 014010, China

Online published: 2025-05-06

Supported by

This research was supported by the National Natural Science Foundation of China, 61765012; Natural Science Foundation of Inner Mongolia Autonomous Region, 2023MS05047; Basic research funds for universities directly under the Inner Mongolia Autonomous Region (2023RCTD011, 2023YXXS012). The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Copyright

Science Press, Institute of Engineering Thermophysics, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2025

摘要

下一代供暖系统对于合理热分配和精细管理至关重要,它们在很大程度上依赖于准确的区域特定热负荷预测。本文介绍了一种基于热消耗分配和数据驱动技术的快速区域特定热负荷预测方法。该方法包括预测建筑物的整体热负荷,然后根据热消耗矩阵重新分配总热量。这消除了从每个房间实时收集数据的需要,从而节省了硬件成本并提高了计算效率。整体建筑热负荷数据是通过数据驱动算法获得的,而热消耗矩阵是通过能源软件模拟分析构建的。以中国包头工业园区的建筑2为案例研究,本文分析了实际测量值与房间估算值之间的差异。实验结果表明,所提出的估算方法平均误差为7.02%。尽管在热负荷预测方面没有达到高精度(>95%),但这种准确度被认为足以满足前馈控制的要求。

本文引用格式

TAN Xin , WANG Yahui , SUN Guoxin , WU Linfeng , YU Qihui , YU Yongheng . Heat Load Prediction of Building Rooms Using Only the Whole Building Data via Heat Allocation Approach[J]. 热科学学报, 2025 , 34(3) : 953 -969 . DOI: 10.1007/s11630-025-2109-2

Abstract

The next-generation heating systems, crucial for rational heat distribution and refined management, rely heavily on accurate zone-specific heat load predictions. This paper introduces a method for rapid zone-specific heat load prediction based on heat consumption allocation and data-driven techniques. The approach involves predicting the overall heat load of the building and then redistributing the total heat according to a heat consumption matrix. This eliminates the need for real-time data collection from each room, resulting in cost savings on hardware and improved computational efficiency. The overall building heat load data is obtained through a data-driven algorithm, while the heat consumption matrix is constructed through energy software simulation analysis. Using Building 2 in the Baotou Industrial Park, China, as a case study, the paper analyzes the differences between actual measurements and room estimates. Experimental results indicate an average error of 7.02% for the proposed estimation method. Although not achieving high precision (>95%) in heat load prediction, this level of accuracy is deemed sufficient to meet the requirements of feedforward control.

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