Wind speed NWP local revisions using a polynomial decomposition of the general partial differential equation

Result code in 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>

Alternative codes found

RIV/61989100:27730/17:10237628 RIV/61989100:27740/17:10237628

Result on the web

<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>

Result language

angličtina

Original language name

Wind speed NWP local revisions using a polynomial decomposition of the general partial differential equation

Original language description

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.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

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

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year

2017

Confidentiality

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

Article name in the collection

Advances in Intelligent Systems and Computing. Volume 679

ISBN

978-3-319-68320-1

ISSN

2194-5357

e-ISSN

2194-5365

Number of pages

11

Pages from-to

45-55

Publisher name

Springer

Place of publication

Cham

Event location

Varna

Event date

Sep 14, 2017

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

—