APPLICATION OF PERIODICALLY CORRELATED STOCHASTIC PROCESSES FOR FORECASTING ELECTRICITY CONSUMPTION

Abstract

The article substantiates the application of the mathematical apparatus of periodically correlated stochastic processes (PCSP) for modeling and forecasting electricity consumption in power systems. The relevance of the research is determined by the need to improve the accuracy of energy load forecasting under conditions of complex temporal consumption structure with pronounced daily, weekly, and seasonal periodicity.

The aim of the work is to develop a new approach to energy load forecasting based on the energy theory of stochastic signals using the PCSP model. For the analysis, experimental data of hourly electricity consumption from a private household were used, aggregated at daily, weekly, and monthly scales.

A common-phase method for processing electricity consumption signals is proposed, both with and without consideration of cross-correlation relationships between components. It was established that the consumption correlation function demonstrates periodic behavior with a 24-hour period, with the daily harmonic accounting for 65-75% of the total signal energy.

The research results showed that the common-phase method with consideration of cross-correlation relationships ensures the detection of hidden patterns in the energy consumption structure and allows accounting for the inertia of power systems. The obtained correlation components can be used as informative features for load forecasting and training artificial intelligence models.

The practical significance of the work lies in creating a theoretical foundation for developing adaptive algorithms for energy consumption forecasting and their implementation in smart grid management systems.