Models and Algorithms for Prediction of Electrical Energy Consumption Based on Canonical Expansions of Random Sequences

Abstract

The given chapter is devoted to the development of the mathematical support, in particular, mathematical models and algorithms, which can be successfully used for solving prediction tasks in various areas of human activity, including energetic and ecological management. The development peculiarities and the use of models and algorithms as elements of green technology to predict electric energy consumption based on mathematical apparatus of canonical expansions of random sequences are currently being discussed. Developed calculation method doesn’t impose any limitations on the qualities of random sequences of the change of electric energy consumption (requirement of linearity, Markovian behavior, monotony, stationarity etc.) and has maximal accuracy characteristics in this connection. Block diagrams of algorithms and results of the applied realization of the developed models and algorithms, for example the prediction of electric energy consumption by one of the local neighborhoods in Mykolaiv, Ukraine are introduced in the work. Comparative analysis of the results of a numerical experiment with the use of a Kalman filter and the linear prediction method confirms the high efficiency of the developed models and algorithms (relative error of prediction of electric energy consumption is 2–3%).