Pre-forecast modeling of airport electricity consumption time series

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Pre-forecast modeling of airport electricity consumption time series

Popis výsledku v původním jazyce

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

Název v anglickém jazyce

Pre-forecast modeling of airport electricity consumption time series

Popis výsledku anglicky

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

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

50201 - Economic Theory

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

International Scientific Conference on Green Energy, GreenEnergy 2024

ISBN

—

ISSN

2267-1242

e-ISSN

2267-1242

Počet stran výsledku

17

Strana od-do

1-17

Název nakladatele

E3S Web of Conferences

Místo vydání

Kyiv

Místo konání akce

Kyiv

Datum konání akce

1. 1. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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