Wind speed NWP local revisions using a polynomial decomposition of the general partial differential equation
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F17%3A10237628" target="_blank" >RIV/61989100:27240/17:10237628 - isvavai.cz</a>
Nalezeny alternativní kódy
RIV/61989100:27730/17:10237628 RIV/61989100:27740/17:10237628
Výsledek na webu
<a href="http://dx.doi.org/10.1007/978-3-319-68321-8_5" target="_blank" >http://dx.doi.org/10.1007/978-3-319-68321-8_5</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-68321-8_5" target="_blank" >10.1007/978-3-319-68321-8_5</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Wind speed NWP local revisions using a polynomial decomposition of the general partial differential equation
Popis výsledku v původním jazyce
Precise daily weather forecasts are necessary for the utilization of renewable energy sources and their penetration into grid systems. Standard meteorological statistical post-processing methods relate local observations with numerical predictions to eliminate systematic forecast errors. Neural networks, trained with the last historical series, can model the current weather frame to refine a target forecast for specific local conditions and reduce random prediction errors. Their daily correction models can process numerical prediction model outcomes of the same data types (instead of the unknown data) to recalculate 24-hour wind speed forecast series. Global numerical weather models succeed generally in forecasting upper air patterns but are too crude to account for local variations in surface weather. Long-term complex forecast systems, which simulate the dynamics of the complete atmosphere in several layers, cannot exactly detail local conditions near the ground, determined by the terrain relief, structure, landscape character, pattern and other factors. Extended polynomial networks can decompose and solve general linear partial differential equations, being able to model properly unknown dynamic systems. In all the network nodes are produced series of relative polynomial derivative terms, which convergent sum combinations can directly define and substitute for the general differential equation to model an uncertain system target function. The proposed local forecast correction procedure using adaptive derivative regression model can improve numerical daily wind speed forecasts in the majority of days. © Springer International Publishing AG 2018.
Název v anglickém jazyce
Wind speed NWP local revisions using a polynomial decomposition of the general partial differential equation
Popis výsledku anglicky
Precise daily weather forecasts are necessary for the utilization of renewable energy sources and their penetration into grid systems. Standard meteorological statistical post-processing methods relate local observations with numerical predictions to eliminate systematic forecast errors. Neural networks, trained with the last historical series, can model the current weather frame to refine a target forecast for specific local conditions and reduce random prediction errors. Their daily correction models can process numerical prediction model outcomes of the same data types (instead of the unknown data) to recalculate 24-hour wind speed forecast series. Global numerical weather models succeed generally in forecasting upper air patterns but are too crude to account for local variations in surface weather. Long-term complex forecast systems, which simulate the dynamics of the complete atmosphere in several layers, cannot exactly detail local conditions near the ground, determined by the terrain relief, structure, landscape character, pattern and other factors. Extended polynomial networks can decompose and solve general linear partial differential equations, being able to model properly unknown dynamic systems. In all the network nodes are produced series of relative polynomial derivative terms, which convergent sum combinations can directly define and substitute for the general differential equation to model an uncertain system target function. The proposed local forecast correction procedure using adaptive derivative regression model can improve numerical daily wind speed forecasts in the majority of days. © Springer International Publishing AG 2018.
Klasifikace
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)
Návaznosti výsledku
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)<br>S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2017
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
Advances in Intelligent Systems and Computing. Volume 679
ISBN
978-3-319-68320-1
ISSN
2194-5357
e-ISSN
2194-5365
Počet stran výsledku
11
Strana od-do
45-55
Název nakladatele
Springer
Místo vydání
Cham
Místo konání akce
Varna
Datum konání akce
14. 9. 2017
Typ akce podle státní příslušnosti
WRD - Celosvětová akce
Kód UT WoS článku
—