Wind-power intra-day multi-step predictions using polynomial networks solutions of general PDEs based on Operational Calculus.

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F19%3A10244102" target="_blank" >RIV/61989100:27240/19:10244102 - isvavai.cz</a>

Výsledek na webu

<a href="https://inis.iaea.org/search/search.aspx?orig_q=RN:52034314" target="_blank" >https://inis.iaea.org/search/search.aspx?orig_q=RN:52034314</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Wind-power intra-day multi-step predictions using polynomial networks solutions of general PDEs based on Operational Calculus.

Popis výsledku v původním jazyce

Precise intra-day predictions of wind-power are challenging due to its intermittent nature and high correlation with large-scale atmospheric chaotic circulation processes. NWP systems solve sets of differential equations to predict a time-change of each 3D-grid cell in several atmospheric layers. Their surface forecasts of wind speed are not entirely adapted to specific local characteristics and anomalies, which largely influence its temporal-flow. AI methods us-ing historical observations can convert and refine the daily forecasts in consideration of wind farm siting, terrain asperity and ground level (hub height). Their independent wind-power predictions in horizon of several hours are also more precise then NWP model forecasts as these are usually produced every 6 hours. The designed method uses Polynomial neural networks to decompose and sub-stitute for the general linear Partial Differential Equation being able to describe n-variable functions of unknown complex dynamic systems. It solves specific 2-variable 2nd order PDEs, formed in PNN nodes, using a polynomial conver-sion based on Operational Calculus. The inverse Laplace transformation is ap-plied to the resulting rational terms to obtain the originals of node functions whose sum gives the complete PDE model. The composite PDE models are developed with data samples from the estimated optimal numbers of training days to represent spatial data relations in current weather, necessary for applicable predictions. They can predict wind power up to 12 hours ahead according to a trained data inputs-&gt;output time-shift. Intra-day multi-step predictions using the PDE models are more precise than those based on NWP model forecasts or statistical techniques allowing using local time-series of several variables only.

Název v anglickém jazyce

Wind-power intra-day multi-step predictions using polynomial networks solutions of general PDEs based on Operational Calculus.

Popis výsledku anglicky

Precise intra-day predictions of wind-power are challenging due to its intermittent nature and high correlation with large-scale atmospheric chaotic circulation processes. NWP systems solve sets of differential equations to predict a time-change of each 3D-grid cell in several atmospheric layers. Their surface forecasts of wind speed are not entirely adapted to specific local characteristics and anomalies, which largely influence its temporal-flow. AI methods us-ing historical observations can convert and refine the daily forecasts in consideration of wind farm siting, terrain asperity and ground level (hub height). Their independent wind-power predictions in horizon of several hours are also more precise then NWP model forecasts as these are usually produced every 6 hours. The designed method uses Polynomial neural networks to decompose and sub-stitute for the general linear Partial Differential Equation being able to describe n-variable functions of unknown complex dynamic systems. It solves specific 2-variable 2nd order PDEs, formed in PNN nodes, using a polynomial conver-sion based on Operational Calculus. The inverse Laplace transformation is ap-plied to the resulting rational terms to obtain the originals of node functions whose sum gives the complete PDE model. The composite PDE models are developed with data samples from the estimated optimal numbers of training days to represent spatial data relations in current weather, necessary for applicable predictions. They can predict wind power up to 12 hours ahead according to a trained data inputs-&gt;output time-shift. Intra-day multi-step predictions using the PDE models are more precise than those based on NWP model forecasts or statistical techniques allowing using local time-series of several variables only.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10200 - Computer and information sciences

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název statě ve sborníku

ITISE 2019 : International Conference on Time Series and Forecasting : proceedings of papers : 25-27 September 2019, Granada, Spain

ISBN

978-84-17970-78-9

ISSN

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e-ISSN

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Počet stran výsledku

10

Strana od-do

1-10

Název nakladatele

University of Granada

Místo vydání

Granada

Místo konání akce

Granada

Datum konání akce

25. 9. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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