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Precise Control for Heating Supply to Households Based on Heating Load Prediction

  • Ruiting Wang
  • Fulin WangEmail author
  • Zhaohan Nan
  • Minjie Xiao
  • Aijun Ding
Conference paper
  • 220 Downloads
Part of the Environmental Science and Engineering book series (ESE)

Abstract

Commonly, no control measures are mounted at the heating terminals in a household in district heating systems in China, so the problem of unbalanced heating pipe network causes problems of overheating, under-heating, energy loss, etc. At the same time, the mandatory installation of calorimeters in residential buildings, which aims to solve the inefficient energy-using behavior by charging heating cost according to heat amount used. However, charging heating cost according to heat amount was not successfully adopted by heating company. As a result, the aim of energy saving was not achieved, and giant investment was wasted as well. Aiming to solve these problems, this paper proposed a precise heating terminal control system utilizing the heating amounts measured by the calorimeter, which can solve the unbalance problem of heating supply and can precisely control the heat supplied to a household to meet the individual heating requirement of the household as well. The proposed control system takes advantage of the data predicting ability of artificial neural network (ANN) to predict heating requirement using the information of weather, desired indoor temperature, building envelope, etc. By comparing the predicted heating load with the heating amount measured by the calorimeter, the on/off of heating water valve is controlled to make the supplied heating amount match the predicted heating load. In this paper, the heating load prediction by ANN is described and the results show that the load prediction is accurate enough for the purpose of achieving precise control of heating amount supplied to the terminal users.

Keywords

Precise heating control Heating system Heating load prediction Neural network 

Notes

Acknowledgements

This research is supported by Innovative Research Groups of the National Natural Science Foundation of China (grant number 51521005).

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, School of ArchitectureTsinghua UniversityBeijingChina
  2. 2.Kechuang Jieneng Mechanical and Electrical Engineering Co. LtdYantaiChina

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