Development of mathematical models for the short-term forecasting of daily consumption schedules of active power by Moscow

Abstract

The present article presents the results of research into solving the problem of increasing the accuracy of forecasting power consumption. The purpose of these studies is to develop mathematical models for short-term forecasting of daily schedules of active power consumption in Moscow, taking into account meteorological factors. Research has been carried out on four predictive models based on singular spectral analysis (SSA), least-squares method, trigonometric interpolation, neural and neural fuzzy networks (NFN). It is shown that the NFN and hybrid model based on MSSA and NFN has the smallest error.