PREDICTION THE HEAT CONSUMPTION OF SOCIAL AND PUBLIC SECTOR BUILDINGS USING NEURAL NETWORKS

Yu. V. Parfenenko, V. V. Shendryk, O. S. Galichenko

Abstract


The method of improvement the process of decision support to improve management of heat supplies’ modes through the development
of prediction heat consumption model of the social and public sector building is proposed. The object of the study is the process of choosing
the most optimal architecture of the neural network to solve goals of forecasting of heat consumption of the building of social and public
sector. The subject of the study is models prediction heat consumption of buildings of social and public sector using artificial neural networks.
The purpose of this study is to improve the forecasting reliability of heat energy demand of social and public sector buildings. Models of shortterm prediction of heat energy demand of social and public sector buildings using artificial neural networks that take into account the influence of weather conditions, fluctuations in demand for thermal energy depending on the type of day of the week and the previous values of heat energy demand are proposed. Models are based on such architectures of neural network’s as a nonlinear network input-output, a nonlinear autoregressive network, a nonlinear autoregressive neural network with external inputs. The proposed models were implemented in the programming environment Matlab, to train their the Levenberg-Marquard algorithm was used. Experiments on the study of the accuracy of the developed models, which showed that the greatest accuracy of prediction can be achieved by using a model type NARX, were carried. Using the proposed model within the information system «HeatCAM» for the prediction of building’s heat consumption during the heating season
can increase the effectiveness of decision support in the management of heating modes, which reduces heat consumption.

Keywords


heat consumption, management, modeling, information systems, prediction, neural networks, energy saving.

References


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DOI: https://doi.org/10.15588/1607-3274-2015-2-5



Copyright (c) 2015 Yu. V. Parfenenko, V. V. Shendryk, O. S. Galichenko

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