THERMAL SCIENCE

International Scientific Journal

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Yang Liu

,

Huijie Liu

SHORT TERM LOAD PREDICTION OF REGIONAL HEATING AND HEAT STORAGE SYSTEM BASED ON NEURAL NETWORK

ABSTRACT
Accurate heat load prediction is the key to achieve fine control, energy conservation, and carbon reduction of regional hydronics. Taking the regional hydronics of a city in the north of China as the research object, the author, respectively uses back propagation neural network (BPNN), genetic algorithm (GA) optimized BPNN (GA-BPNN), and autoregressive integrated moving average model (ARI­MA) combined BPNN (ARIMA BPNN) to predict its heat load, and compares the accuracy and applicability of each prediction method. The results indicate that GA-BPNN has the smallest prediction error, followed by ARIMA-BPNN, but the latter requires less data for prediction. In practical engineering, if there is a sufficient amount of data related to heat load, it is recommended to use GA-BPNN. If there is a small amount of data, ARIMA-BP prediction method can be used.
KEYWORDS
differential autoregressive moving average model, load prediction, BPNN, GA, district heating
PAPER SUBMITTED: 2023-05-02
PAPER REVISED: 2023-07-28
PAPER ACCEPTED: 2023-09-01
PUBLISHED ONLINE: 2024-04-13
DOI REFERENCE: https://doi.org/10.2298/TSCI2402183L
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2024, VOLUME 28, ISSUE Issue 2, PAGES [1183 - 1190]
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Short term load prediction of regional heating and heat storage system based on neural network

© 2024 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence