Abstract
Purpose of reseach. Development of a forecast model of energy consumption and assessment of factors influencing its consumption. The obtained forecast estimates of energy consumption will improve the quality and efficiency of management decisions at all levels of administrative management.Methods. The article presents an analytical review of the existing methods of cognitive modelling and forecasting of electric power consumption, the description of the software implementation of the information-computing system that allows to make a forecast of electric power consumption by the population of the administrative-territorial formation. The approach to the description of factors of electric power consumption by both population and various branches of national economy, as well as organisations engaged in rendering various services has been proposed. Special software has been developed, which allows to obtain model results of electric power consumption in an automated mode, to carry out factor analysis of power consumption. The experimental verification of the work of the programme of cognitive modelling and forecasting of electric power consumption by the population of Lgovsky district of Kursk region is given. The developed software also makes it possible to evaluate the adequacy of the obtained results and promptly adjust the model parameters.Results. As a result of the research a fuzzy cognitive map of energy consumption for a municipal entity was developed. The concepts of the subject area describing the influence of various groups of factors on the level of electric energy consumption were identified. Forecast estimates of electricity consumption were obtained, which were based on the data for the retrospective period. Adequacy indicators based on the calculation of statistical criteria are determined for the obtained estimates.Conclusion. The results of the study have shown that the combination of cognitive and statistical methods allows to achieve an adequate solution when solving the problem of energy consumption forecasting.
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