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 neces...

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Main Authors: Iehor V. Sedliarov, Kateryna Serhiivna Klen
Format: Article
Language:English
Published: Igor Sikorsky Kyiv Polytechnic Institute 2025-02-01
Series:Mìkrosistemi, Elektronìka ta Akustika
Subjects:
Online Access:https://elc.kpi.ua/article/view/320821
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author Iehor V. Sedliarov
Kateryna Serhiivna Klen
author_facet Iehor V. Sedliarov
Kateryna Serhiivna Klen
author_sort Iehor V. Sedliarov
collection DOAJ
description 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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institution Kabale University
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language English
publishDate 2025-02-01
publisher Igor Sikorsky Kyiv Polytechnic Institute
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series Mìkrosistemi, Elektronìka ta Akustika
spelling doaj-art-5f4768369c6747ee83c5bf1ba3f24f4c2025-08-20T03:46:54ZengIgor Sikorsky Kyiv Polytechnic InstituteMìkrosistemi, Elektronìka ta Akustika2523-44472523-44552025-02-0130110.20535/2523-4455.mea.320821Prediction of the Time Distribution of Shannon and Renyi Entropy Based on the Theory of MomentsIehor V. Sedliarov0Kateryna Serhiivna Klen1National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” 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%. https://elc.kpi.ua/article/view/320821distributed generation systemsShannon entropyRenyi entropyfractal dimension
spellingShingle Iehor V. Sedliarov
Kateryna Serhiivna Klen
Prediction of the Time Distribution of Shannon and Renyi Entropy Based on the Theory of Moments
Mìkrosistemi, Elektronìka ta Akustika
distributed generation systems
Shannon entropy
Renyi entropy
fractal dimension
title Prediction of the Time Distribution of Shannon and Renyi Entropy Based on the Theory of Moments
title_full Prediction of the Time Distribution of Shannon and Renyi Entropy Based on the Theory of Moments
title_fullStr Prediction of the Time Distribution of Shannon and Renyi Entropy Based on the Theory of Moments
title_full_unstemmed Prediction of the Time Distribution of Shannon and Renyi Entropy Based on the Theory of Moments
title_short Prediction of the Time Distribution of Shannon and Renyi Entropy Based on the Theory of Moments
title_sort prediction of the time distribution of shannon and renyi entropy based on the theory of moments
topic distributed generation systems
Shannon entropy
Renyi entropy
fractal dimension
url https://elc.kpi.ua/article/view/320821
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