This article is cited in 9 papers

Intellectual Control Systems, Data Analysis

Elements of randomized forecasting and its application to daily electrical load prediction in a regional power system

Yu. S. Popkov^ab, A. Yu. Popkov^a, Yu. A. Dubnov<sup>ac

a</sup> Federal Research Center “Information Science and Control”, Russian Academy of Sciences, Moscow, Russia
^b Trapeznikov Institute of Control Sciences, Russian Academy of Sciences, Moscow, Russia
^c National Research University Higher School of Economics, Moscow, Russia

Abstract: A randomized forecasting method based on the generation of ensembles of entropy-optimal forecasting trajectories is developed. The latter are generated by randomized dynamic regression models containing random parameters, measurement noises, and a random input. The probability density functions of random parameters and measurement noises are estimated using real data within the randomized machine learning procedure. The ensembles of forecasting trajectories are generated by the sampling of the entropy-optimal probability distributions. This procedure is used for the randomized prediction of the daily electrical load of a regional power system. A stochastic oscillatory dynamic regression model is designed. One-, two-, and three-day forecasts of the electrical load are constructed, and their errors are analyzed.

Keywords: forecasting, hierarchical randomization, oscillatory dynamic regression, entropy functional, empirical balance, daily electrical load of power system, sampling of probability density.

Presented by the member of Editorial Board: A. I. Kibzun

Received: 14.10.2019
Revised: 11.12.2020
Accepted: 30.01.2020

DOI: 10.31857/S0005231020070107

 English version: Automation and Remote Control, 2020, 81:7, 1286–1306

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