MODELING AND FORECASTING OF WHOLESALE MARKET INDICATORS ELECTRICITY IN RUSSIA USING COMBINATION METHODS DATA OF DIFFERENT FREQUENCIES

МОДЕЛИРОВАНИЕ И ПРОГНОЗИРОВАНИЕ ПОКАЗАТЕЛЕЙ ОПТОВОГО РЫНКА ЭЛЕКТРОЭНЕРГИИ РОССИИ С ИСПОЛЬЗОВАНИЕМ МЕТОДОВ СОВМЕЩЕНИЯ ДАННЫХ РАЗНОЙ ЧАСТОТНОСТИ

Kaukin, Andrey (Каукин, Андрей) (), Kasyanova, Ksenia (Касьянова, Ксения) and Kosarev, Vladimir (Косарев, Владимир)
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Kaukin, Andrey (Каукин, Андрей): The Russian Presidential Academy of National Economy and Public Administration
Kasyanova, Ksenia (Касьянова, Ксения): The Russian Presidential Academy of National Economy and Public Administration
Kosarev, Vladimir (Косарев, Владимир): The Russian Presidential Academy of National Economy and Public Administration

Working Papers from Russian Presidential Academy of National Economy and Public Administration

Abstract: The aim of this study is to develop new methods for forecasting time series with data of different frequencies among exogenous factors; forecasting the indicators of the wholesale electricity market in Russia using methods of combining data of different frequencies, including those based on algorithms of convolutional neural networks. The structure of the work is presented in four sections. The first section analyzes methods for forecasting time series with combining data of different frequencies. The second section presents the architecture of a convolutional network that allows the use of data of different frequencies. The third section presents a price model for the wholesale electricity market using data from the Atlas of Russian Energy. The fourth section presents recommendations and main conclusions of the work.

Keywords: electricity demand; wholesale electricity market; generation capacity; day-ahead market; price modeling; multi-frequency data; convolutional neural networks (search for similar items in EconPapers)
Pages: 71 pages
Date: 2021-11-12
New Economics Papers: this item is included in nep-cis and nep-ene
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