The work is devoted to the problem of excessive spending of people's funds on utilities, especially in winter, when these costs can amount to more than 25% of the family budget. The question of the possibility of saving at least part of these costs by monitoring their possible value and reducing this indicator is an urgent task. Hence, the development of a software system for forecasting utility costs is an urgent practical task. To solve this problem, the authors propose to use a neural network, because it is advisable to use it in situations where there is predetermined known information and on its basis the user needs to get the predicted new information. The method for forecasting utility costs based on the use of a neural network takes into account user's data of utility service costs entered manually or obtained from the EPS system, which is convenient because you can immediately get a large set of input data to more accurately predict future costs. Another type of input data is data obtained from weather forecast sites to determine forecast indicators for correct the training of neural network. Based on these data, the network studies and builds a separate model for forecasting utility costs for each user. Considering that the data on utility service costs entered by users into the system each month may not match the date, it is proposed to take into account this inaccuracy, to given the input data for forecasting as an interval corridor of values which containing the minimum and maximum forecast limits. The developed software system and the method of forecasting utility service costs were tested on the example of a real user of the EPS system.