Using a Fuzzy Prognostic Model in the Operative-Dispatch Analysis of Heat-Supply Systems’ Operation

The heating network of a big city is a complex hydrodynamic system consisting of dozens of heating stations with its own local network, and random changes occurring in any of the elements are more noticeable in it. In this case, since each thermal station is autonomous, it can be a normal and efficient source of heat due to the peculiarities of energy control based on its own hydrodynamic properties. At the same time, it is problematic to take into account all influencing factors in specific territories as well as to collect geographically distributed information. In this regard, the development of analysis methods and the prediction of the operation of thermal centers based on the modern theory of analysis and modeling of dynamic processes that are convenient for implementation and adequately describe real physical situations are relevant. In this case, the most important task of the analysis of heat consumption is to determine the patterns of change in the studied phenomenon, which are formed under the influence of a set of reasons that act on it constantly for a long time. These reasons are sometimes completely random, which complicates the study process and can lead to an incorrect conclusion. To get out of this situation, it is necessary to use a fuzzy logic apparatus, which reduces the influence of random in-row changes by introducing linguistic terms. With this in mind, the goal of the article is to build a prognostic model based on the theory of fuzzy logic parameters, which may be key to the development of the energy-management process based on archived data from thermal centers. A method for constructing a forecast model of a fuzzy approach for a time series with in-row multiplicative changes and the possibility of using it for operational dispatch analysis of heat-supply systems are presented. The simulation is based on the calculation of predicted values for the membership function built on the statistical indicators of row-by-row changes. The proposed method is applied to the time series of dynamic parameters in heat-supply systems. Based on the performed experiments and computational experiments, it was found that the average relative error between the predicted and actual indicators was 3.5% for the temperature in the supply and 4.7% in the return pipe of the system.