^ Application of Neural Networks for Control of District Heating Wykorzystanie Sieci Neuronowych Do Regulacji W Ciepłownictwie | Structurae
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Application of Neural Networks for Control of District Heating Wykorzystanie Sieci Neuronowych Do Regulacji W Ciepłownictwie

Author(s):
Medium: journal article
Language(s): English
Published in: Archives of Civil Engineering, , n. 3, v. 56
Page(s): 219-238
DOI: 10.2478/v.10169-010-0012-y
Abstract:

The annual usage of heat for the demand of heating systems in municipal sector has been estimated as about 650PJ. It is mostly addressed for the demand of central heating systems and hot water consumption. The mode of adopted solutions concerning regulation and control, as well as energy management system, essentially influence its consumption. In the case of residential buildings, the costs of energy constitute the greatest share related to the total cost of building maintenance. Providing buildings with modern digital systems for control and regulation of heating installations is a basic condition enabling their rational usage. In currently employed solutions, algorithms PI or PID are usually applied. However, due to the non-linear properties of heating control systems, they do not secure proper quality. The sequences are often unstable and major control deviations occur. The application of neural networks is an alternative solution to those presently employed. They are especially recommended for adaptive control of non-stationary systems. Such cases occur in heating objects since they demonstrate non-linear properties with a great range of variability of parameters; this especially refers to district heating equipped with flux-through heat exchangers. In this paper, a compile model of heating system control aided by neural networks is presented. The results of the investigation clearly prove the usefulness of such solutions, cause the quality of control is much better than that one applied in traditional systems. Presently, works on the implementation of the proposed solutions are under way.

Structurae cannot make the full text of this publication available at this time. The full text can be accessed through the publisher via the DOI: 10.2478/v.10169-010-0012-y.
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    data sheet
  • Reference-ID
    10477057
  • Published on:
    16/11/2020
  • Last updated on:
    26/02/2021