Neural model for internal temperature prediction in the multi-family building premi

Abstract

Intelligent Heating Systems, operated by SCADA (Supervisory Control and Data Acquisition) that are used today in heating systems are a source of great amount of measurement data. Very often information contained therein is lost because data analysis creates problems of a methodological nature. This paper presents the results of research on the use of data mining methods to predict air temperature in 31 premises of a multi-family building. For this purpose, the time series of indoor temperature and daily sums of indoor temperature during one heating season (October-May) were analyzed using Artificial Neural Networks (ANN). The quality of neuron prediction models was assessed on the basis of values of linear correlation coefficients and the quotient of standard deviations between actual and predicted data. It has been shown that the proposed method can be used as a tool to support the calculation of heating fees in the case of short-term failures of the monitoring system.