Open AccessApplication of Neural Networks for Control of District Heating \ Wykorzystanie Sieci Neuronowych Do Regulacji W Ciepłownictwie
Author(s) -
W. J. Chmielnicki
Publication year - 2010
Publication title -
archives of civil engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.208
H-Index - 15
eISSN - 2300-3103
pISSN - 1230-2945
DOI - 10.2478/v.10169-010-0012-y
Subject(s) - heating system , pid controller , control (management) , artificial neural network , quality (philosophy) , computer science , heat exchanger , control system , energy consumption , process engineering , temperature control , control engineering , engineering , mechanical engineering , electrical engineering , artificial intelligence , philosophy , epistemology
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.