Analysis on Influencing Parameters of Heating Consumption Prediction

Ye Tian,Rui Li,G.I Rashed,M Kheshti

doi:10.1051/e3sconf/202125203024

Ye Tian, Rui Li + Show 2 more

Open Access

https://doi.org/10.1051/e3sconf/202125203024

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Abstract

This paper studies the influencing parameters of the heating consumption prediction in heating substation, uses the BP neural network to predict the heating consumption, and establishes four BP neural network structures to change the outdoor average temperature, the lowest temperature and the highest outdoor temperature, the predicted results found that when the input variables include the average outdoor temperature, the lowest outdoor temperature, the highest outdoor temperature, the wind speed, and the heating consumption of the previous three days, the prediction results are better , the relative error is equal or less than 0.25%.

Highlights

The accuracy of heating consumption of district heating system affects the heating effect and economy of the heating system operation
As a new type of prediction method, artificial neural network prediction has been used by many scholars in the research of heating consumption prediction
The heating consumption of heating substation is affected by multiple parameters and has non-linear characteristics [1]; Zhu et al studied the prediction of BP neural network in heating energy saving and found that the prediction of BP neural network greatly reduces the operating energy consumption of the district heating system [2]

Summary

Introduction

The accuracy of heating consumption of district heating system affects the heating effect and economy of the heating system operation. It shows that some of the existing prediction methods are not accurate enough, and sometimes heating consumption is higher than the demand, and sometimes can not meet the needs of users. The heating consumption of heating substation is affected by multiple parameters and has non-linear characteristics [1]; Zhu et al studied the prediction of BP neural network in heating energy saving and found that the prediction of BP neural network greatly reduces the operating energy consumption of the district heating system [2]

Methods

Results

Conclusion