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

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

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.

Cite this article

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]. Journal of Thermal Science, 2025 , 34(3) : 953 -969 . DOI: 10.1007/s11630-025-2109-2

References

[1] Gudmundsson O., Schmidt R.R., Dyrelund A., Economic comparison of 4GDH and 5GDH systems – Using a case study. Energy, 2022, 238: 121613.
[2] Ehmer M., Khan F., A comparative study of white box, black box and grey box testing techniques. International Journal of Advanced Computer Science & Applications, 2012, 3(6): 1–12. DOI:10.14569/IJACSA.2012.030603.
[3] Zhang Q., Tian Z., Ma Z., Development of the heating load prediction model for the residential building of district heating based on model calibration. Energy, 2020, 205: 117949.
[4] Nielsen H.A., Madsen H., Modeling the heat consumption in district heating systems using a grey-box approach. Energy and Buildings, 2006, 38: 63–71.
[5] Xi W., Wen J., Commercial building cooling energy forecasting using proactive system identification: A whole building experiment study. Science & Technology for the Built Environment, 2016, 22(6): 674–691. 
DOI:10.1080/23744731.2016.1188654.
[6] Sulzer M., Christen A., Matzarakis A., Predicting indoor air temperature and thermal comfort in occupational settings using weather forecasts, indoor sensors, and artificial neural networks. Building and Environment, 2023, 234: 110077.
[7] Fouladfar M.H., Soppelsa A., Nagpal H., Adaptive thermal load prediction in residential buildings using artificial neural networks. Journal of Building Engineering, 2023, 77: 107464.
[8] Sakawa M., Katagiri H., Matsui T., Heat load prediction in district heating and cooling systems through a recurrent neural network with data characteristics. Scientiae Mathematicae Japonicae, 2010, 72(3): 237–252.
[9] Ye H., Ni W., Static and transient performance prediction for CFB boilers using a Bayesian-Gaussian Neural Network. Journal of Thermal Science, 1997, 6: 141–148. 
[10] Qi L., Feng Z., Improved BP neural network of heat load forecasting based on temperature and date type. Journal of System Simulation, 2019, 30(4): 1464–1472.
[11] Lin Y., Feng S., Study on the air-conditioning load prediction model based on BP neural network. Applied Energy Technology, 2016, 9: 46–51. 
[12] Ahn J., Cho S., Development of an intelligent building controller to mitigate indoor thermal dissatisfaction and peak energy demands in a district heating system. Building and Environment, 2017, 124: 57–68.
[13] Ding Y., Zhang Q., Yuan T., Model input selection for building heating load prediction: A case study for an office building in Tianjin. Energy and Buildings, 2018, 159: 254–270.
[14] Gu J., Wang J., Qi C., Medium-term heat load prediction for an existing residential building based on a wireless on-off control system. Energy, 2018, 152: 709–718.
[15] Cholewa T., Siggelsten S., Balen I., Heat cost allocation in buildings: Possibilities, problems and solutions. Journal of Building Engineering, 2020, 31: 101349.
[16] Pakanen J., Karjalainen S., Estimating static heat flows in buildings for energy allocation systems. Energy and Buildings, 2006, 38(9): 1044–1052.
[17] Gambarotta A., Morini M., Pompini N., Optimization of load allocation strategy of a multi-source energy system by means of dynamic programming. Energy Procedia, 2015, 81: 30–39.
[18] Yuan J., Xiao F., Gang W., Load allocation methods for the thermal and electrical chillers in distributed energy systems for system efficiency improvement. Energy Conversion and Management, 2023, 292: 117334.
[19] Cerri G., Monacchia S., Seyedan B., Optimum load allocation in cogeneration gas-steam combined plants. Turbo Expo: Power for Land, Sea, and Air. American Society of Mechanical Engineers, 1999, 78606: V003T02A001.
[20] Liu Z., Yang P., Peng J., Capacity allocation for regional integrated energy system considering typical day economic operation. 2018 IEEE International Conference on Energy Internet (ICEI). IEEE, 2018, pp: 60–65.
[21] Morini M., Pinelli M., Spina P.R., Optimal allocation of thermal, electric and cooling loads among generation technologies in household applications. Applied Energy, 2013, 112: 205–214.
[22] Matsko T.N., Moss W.H., Scheib T.J., Optimal boiler load allocation in distributed control. 1982 American Control Conference. IEEE, 1982: 1140–1145.
[23] Yu S., Cui Y., Xu X., Feng G., Impact of civil envelope on energy consumption based on EnergyPlus. Procedia Engineering 2015, 121: 1528e34.
[24] Li Q., The influence of flat locations on space heating consumption and heating price in residential building. Tianjin University, 2014.
[25] Yuan J., Huang K., Lu S., Analysis of influencing factors on heat consumption of large residential buildings with different occupancy rates-Tianjin case study. Energy, 2022, 238: 121834.
[26] Klein S.A., Beckman W.A., Duffie J.A., TRNSYS-a transient simulation program. ASHRAE Transactions, 1976, 82: 123–134.
[27] Nasouri, M., Delgarm, N., Efficiency-based Pareto Optimization of Building Energy Consumption and Thermal Comfort: A Case Study of a Residential Building in Bushehr, Iran. Journal of Thermal Science, 2024, 3: 1037–1054.
[28] Ganoe R.D., Stackhouse Jr.P.W., De Young.R.J., RETScreen plus software tutorial. 2014.
[29] Breton P.F., Experimental validation of Autodesk® 3ds Max® Design 2009 and DAYSIM 3.0. Leukos, 2009, 6(1): 7–35.
[30] Wasilowski H., Reinhart C., Samuelson H.W., Modeling an existing building in DesignBuilder/EnergyPlus: Custom vs. Default Inputs, 2009.
[31] Tindale A., DesignBuilder and EnergyPlus, 2004.
[32] Dahl M., Brun A., Andresen G.B., Using ensemble weather predictions in district heating operation and load forecasting. Applied Energy, 2017, 193: 455–465.
[33] Powell K.M., Sriprasad A., Cole W.J., Heating, cooling, and electrical load forecasting for a large-scale district energy system. Energy, 2014, 74: 877–885.
[34] O’Shea K., Nash R., An introduction to convolutional neural networks. Computer Science, 2015, 1511.08458.
[35] Informatik F.F., Schmidhuber J., LSTM can solve hard long time lag problems. MIT Press, 1996.
[36] Myers L., Sirois M.J., Spearman correlation coefficients, differences between. Encyclopedia of Statistical Sciences, 2004. DOI:10.1002/9781118445112.stat06250
[37] Tan X., Zhu Z., Sun G., Room thermal load prediction based on analytic hierarchy process and back-propagation neural networks. Building Simulation, 2022, 15: 1989–2002.
[38] Reddy T.A., Maor I., Panjapornpon C., Calibrating detailed building energy simulation programs with measured data—Part I: General Methodology (RP-1051). Hvac & R Research, 2007, 13(2): 221–241.

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