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Elements of Randomized Forecasting and Its Application to Daily Electrical Load Prediction in a Regional Power System

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An Erratum to this article was published on 01 December 2020

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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.

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Authors and Affiliations

  1. Federal Research Center “Information Science and Control,” Russian Academy of Sciences, Moscow, Russia

    Yu.S. Popkov, A.Yu. Popkov & Yu.A. Dubnov

  2. Trapeznikov Institute of Control Sciences, Russian Academy of Sciences, Moscow, Russia

    Yu.S. Popkov

  3. National Research University Higher School of Economics, Moscow, Russia

    Yu.A. Dubnov

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  1. Yu.S. Popkov
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  2. A.Yu. Popkov
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  3. Yu.A. Dubnov
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Popkov, Y., Popkov, A. & Dubnov, Y. Elements of Randomized Forecasting and Its Application to Daily Electrical Load Prediction in a Regional Power System. Autom Remote Control 81, 1286–1306 (2020). https://doi.org/10.1134/S0005117920070103

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