Wind Power Intra-day Multi-step Predictions Using PDE Sum Models of Polynomial Networks Based on the PDE Conversion and Substitution with the L-Transformation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F21%3A10245222" target="_blank" >RIV/61989100:27240/21:10245222 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-030-49345-5_27" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-030-49345-5_27</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-49345-5_27" target="_blank" >10.1007/978-3-030-49345-5_27</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Wind Power Intra-day Multi-step Predictions Using PDE Sum Models of Polynomial Networks Based on the PDE Conversion and Substitution with the L-Transformation

Popis výsledku v původním jazyce

Precise forecasts of wind power are required as they allow full integration of wind farms into the electrical grid and their active operation. Their daily base poses a challenge due to the chaotic nature of global atmospheric dynamical processes resulting in local wind fluctuations and waves. Surface wind forecasts of NWP models are not fully adapted to local anomalies which can influence significantly in addition its temporal-flow. AI methods using historical observations can convert or refine forecasts in consideration of wind farm location, topography and hub positions. Their independent intra-day wind-power predictions are more precise then those based on NWP data as these are usually produced every 6 h with a delay. The designed AI method combines structures of polynomial networks with some mathematic techniques to decompose and substitute for the n-variable linear Partial Differential Equation, which allow complex representation of unknown dynamic systems. Particular 2-variable PDEs, produced in network nodes, are converted using the Laplace transformed derivatives. The inverse L-transformation is applied to the resulting pure rational terms to obtain the originals of unknown node functions, whose sum is the composite PDE model. Statistical models are developed with data samples from estimated periods of the last days which optimally represent spatial patterns in the current weather. They process the latest available data to predict wind power in the next 1-12 h according to the trained data inputs RIGHTWARDS ARROW output time-shift.

Název v anglickém jazyce

Wind Power Intra-day Multi-step Predictions Using PDE Sum Models of Polynomial Networks Based on the PDE Conversion and Substitution with the L-Transformation

Popis výsledku anglicky

Precise forecasts of wind power are required as they allow full integration of wind farms into the electrical grid and their active operation. Their daily base poses a challenge due to the chaotic nature of global atmospheric dynamical processes resulting in local wind fluctuations and waves. Surface wind forecasts of NWP models are not fully adapted to local anomalies which can influence significantly in addition its temporal-flow. AI methods using historical observations can convert or refine forecasts in consideration of wind farm location, topography and hub positions. Their independent intra-day wind-power predictions are more precise then those based on NWP data as these are usually produced every 6 h with a delay. The designed AI method combines structures of polynomial networks with some mathematic techniques to decompose and substitute for the n-variable linear Partial Differential Equation, which allow complex representation of unknown dynamic systems. Particular 2-variable PDEs, produced in network nodes, are converted using the Laplace transformed derivatives. The inverse L-transformation is applied to the resulting pure rational terms to obtain the originals of unknown node functions, whose sum is the composite PDE model. Statistical models are developed with data samples from estimated periods of the last days which optimally represent spatial patterns in the current weather. They process the latest available data to predict wind power in the next 1-12 h according to the trained data inputs RIGHTWARDS ARROW output time-shift.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/EF17_049%2F0008425" target="_blank" >EF17_049/0008425: Platforma pro výzkum orientovaný na Průmysl 4.0 a robotiku v ostravské aglomeraci</a><br>

Návaznosti

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

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název statě ve sborníku

Advances in Intelligent Systems and Computing. Volume 1182

ISBN

978-3-030-49344-8

ISSN

2194-5357

e-ISSN

2194-5365

Počet stran výsledku

10

Strana od-do

254-265

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Haidarábád

Datum konání akce

13. 12. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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