Research on Heating Load Prediction Model based on the Influence of Multiple Meteorological Elements using Deep Learning

Research on Heating Load Prediction Model based on the Influence of Multiple Meteorological Elements using Deep Learning

Accurate heating load forecasting is crucial for enhancing the efficiency of district heating systems and improving indoor comfort in buildings.This study takes Tianjin， a major city in northern China， as a case study.Based on hourly heating load and meteorological data from the heating season in 20...

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Bibliographic Details
Main Authors: Rui MIAO, Mingcai LI, Meiling SUN, Di PAN, Xifan ZHANG
Format: Article
Language:Chinese
Published: Science Press, PR China 2025-06-01
Series:Gaoyuan qixiang
Subjects:
meteorological factors
heating load prediction
narx neural network
centralized heating
Online Access:http://www.gyqx.ac.cn/EN/10.7522/j.issn.1000-0534.2024.00099
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Summary:Accurate heating load forecasting is crucial for enhancing the efficiency of district heating systems and improving indoor comfort in buildings.This study takes Tianjin， a major city in northern China， as a case study.Based on hourly heating load and meteorological data from the heating season in 2021 -2022， the impacts of comprehensive meteorological factors， such as temperature， wind speed， relative humidity， and solar radiation， on heating load is analyzed.An efficient short-term heating load prediction model is constructed using the Nonlinear Autoregressive with Exogenous Inputs （NARX） neural network algorithm.The results show that the hourly heating load that displays significant diurnal and monthly variations， has a notably negative correlation with temperature， weakly negative correlation with solar radiation， while the relationships with humidity and wind speed vary depending on the season.Compared to the prediction model considering only temperature， the model incorporating temperature， wind speed， relative humidity， and solar radiation together has better prediction performance， reducing the relative error by approximately 1.4%.By comparing the forecast results with the LSTM neural network prediction model， the NARX model significantly enhances prediction accuracy with decreasing the relative error by about 3.6%.
ISSN:1000-0534

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