Primena veštačkih neuronskih mreža za kratkoročno predviđanje i analizu sistema daljinskog grejanja

Abstract

The subject of the research relates to the development and implementation of algorithms for short-term prediction of the district heating system characteristics using artificial neural networks. The research is aimed at developing algorithms for the selection of standard feedforward and recurrent artificial neural networks and their architectures, choice and adjustment their parameters, choice and definition of adequate inputs, modification of network architecture and its adaptation to meet the demands imposed by the application of artificial neural networks for short-term prediction of heat load as main characteristic od district heating system. Special attention will be devoted to a comparative analysis of proposed and adopted artificial neural networks with their different architectures to obtain optimal algorithms. An adequate heat load prediction and satisfying consumer demands with delivered heat energy in sense of control system, energy saving and environment protection, are very important preconditions for optimal adjusting of district heating system Improving quality of prediction, as one of the dissertation objective, has positive impact to control of district heating system, in general. The main focus is on adequate choice of input vector, number of input nodes and other parameters for standard types of neural networks, contrary to solutions of some authors from literature, where they are creating totally new and unique networks for solving specific problem. On that way, they are loosing possibility of generalization which is opposite to one of the dissertation objective. Specific attention is given to problem of transient regime of heating, where there are no continuation in heating during a day and defined heating period. Achieving qualitative prediction for short period is very important for decrease heat consumption and increase the coefficient of equipment exploitation. This is more important due the fact that district heating systems in Serbia are intermitted by definition which means that heating is not realized in continuation but with turning on and off in the morning and evening hours. Short term prediction is realized for prediction of selected parameters and district heating system characteristics for period of one, three and seven days. Deigned modified feedforward and recurrent neural networks satisfy needed quality of prediction for district heating systems, adequately predict peak loads in transient heating regimes and through the realization of neural networks of the same architecture on four different data heat sources, they are showing possibility of generalization on specific level.