Prediction of the Time Distribution of Shannon and Renyi Entropy Based on the Theory of Moments

The application of the theory of moments to distributed generation systems for the construction of a reducing and predicting polynomial of the time distribution of entropy changes in time at the base interval is proposed. It is shown that in order to improve the accuracy of forecasting, it is necessary to take into account the fractal nature of energy consumption processes and use Rényi entropy in calculations. By taking into account the fractal nature of the energy consumption process and the use of Rényi entropy in calculations, an increase in prediction accuracy by 11% is achieved, resulting in the prediction of the time distribution of Shannon's entropy for power consumption with an error not exceeding 23%.

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Publication Details

Journal
Microsystems Electronics and Acoustics
Published
2025-02-27
DOI
https://doi.org/10.20535/2523-4455.mea.320821
Primary Topic
Energy Load and Power Forecasting
Type
article
Field-Weighted Citation Impact
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article

Prediction of the Time Distribution of Shannon and Renyi Entropy Based on the Theory of Moments

Kateryna Serhiivna Klen, Yehor Sedliarov
Microsystems Electronics and Acoustics
Energy Load and Power Forecasting
article

Prediction of the Time Distribution of Shannon and Renyi Entropy Based on the Theory of Moments

Kateryna Serhiivna Klen, Yehor Sedliarov
article en

Abstract

The application of the theory of moments to distributed generation systems for the construction of a reducing and predicting polynomial of the time distribution of entropy changes in time at the base interval is proposed. It is shown that in order to improve the accuracy of forecasting, it is necessary to take into account the fractal nature of energy consumption processes and use Rényi entropy in calculations. By taking into account the fractal nature of the energy consumption process and the use of Rényi entropy in calculations, an increase in prediction accuracy by 11% is achieved, resulting in the prediction of the time distribution of Shannon's entropy for power consumption with an error not exceeding 23%.

Microsystems Electronics and AcousticsVol. 30(1)
National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” (UA)
Openalex Percentile: Top 89%
Energy Load and Power Forecasting
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