Pre-forecast modeling of airport electricity consumption time series

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41110%2F24%3A100141" target="_blank" >RIV/60460709:41110/24:100141 - isvavai.cz</a>
Result on the web
<a href="https://www.e3s-conferences.org/articles/e3sconf/abs/2024/117/e3sconf_greenenergy24_01019/e3sconf_greenenergy24_01019.html" target="_blank" >https://www.e3s-conferences.org/articles/e3sconf/abs/2024/117/e3sconf_greenenergy24_01019/e3sconf_greenenergy24_01019.html</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1051/e3sconf/202458701019" target="_blank" >10.1051/e3sconf/202458701019</a>

Result language
angličtina
Original language name
Pre-forecast modeling of airport electricity consumption time series
Original language description
The article analyzes the relevance of pre-forecast modeling of time series of electricity consumption by airports, systematizes the methods and ways of the specified pre-forecast modeling and considers some problems arising in the process of their use. A separate stage of preforecast modeling of electricity consumption by the airport is proposed, which contributes, on the one hand, to a fairly quick receipt of primary information about the forecasted object, and on the other hand - to a more effective and adequate final forecast. It is proposed to build a series of neural network models at the stage of preliminary forecasting, including convolutional, recurrence. As a model example, a neural network preforecast model of electricity consumption for the Lviv International Airport is built on the basis of statistical data for the period of relatively stable development of the Ukrainian economy. A comparative analysis of the obtained results of the neural network model with the constructed trend-seasonal model using analytical methods was carried out, which gave a positive result. Conclusions are made on the prospects of building preforecast models of time series of electricity consumption by the airport using neural networks
Czech name
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Czech description
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Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
International Scientific Conference on Green Energy, GreenEnergy 2024
ISBN
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ISSN
2267-1242
e-ISSN
2267-1242
Number of pages
17
Pages from-to
1-17
Publisher name
E3S Web of Conferences
Place of publication
Kyiv
Event location
Kyiv
Event date
Jan 1, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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