THERMAL SCIENCE

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Min Cao

THERMAL STATION MODELLING AND OPTIMAL CONTROL BASED ON DEEP LEARNING

ABSTRACT
To solve the mismatch between heating quantity and demand of thermal stations, an optimized control method based on depth deterministic strategy gradient was proposed in this paper. In this paper, long short-time memory deep learning algorithm is used to model the thermal power station, and then the depth deterministic strategy gradient control algorithm is used to solve the water supply flow sequence of the primary side of the thermal power station in combination with the operation mechanism of the central heating system. In this paper, a large number of historical working condition data of a thermal station are used to carry out simulation experiment, and the results show that the method is effective, which can realize the on-demand heating of the thermal station a certain extent and improve the utilization rate of heat.
KEYWORDS
deterministic strategy, short- and long-term memory networks gradient thermal power station optimization control, deep learning algorithm
PAPER SUBMITTED: 2020-11-16
PAPER REVISED: 2020-12-28
PAPER ACCEPTED: 2021-01-16
PUBLISHED ONLINE: 2021-07-31
DOI REFERENCE: https://doi.org/10.2298/TSCI2104965C
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2021, VOLUME 25, ISSUE Issue 4, PAGES [2965 - 2973]
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Thermal station modelling and optimal control based on deep learning

© 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