Post-processing of Wind-speed Forecasts Using the extended Perfect Prog method with Polynomial Neural Networks to elicit PDE models

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F18%3A10240159" target="_blank" >RIV/61989100:27240/18:10240159 - isvavai.cz</a>

Alternative codes found

RIV/61989100:27730/18:10240159

Result on the web

<a href="https://www.springer.com/gp/book/9783030143466" target="_blank" >https://www.springer.com/gp/book/9783030143466</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Post-processing of Wind-speed Forecasts Using the extended Perfect Prog method with Polynomial Neural Networks to elicit PDE models

Original language description

Anomalies in local weather cause inaccuracies in daily predictions using meso-scale numerical models. Statistical methods using historical data can adapt the forecasts to specific local conditions. Differential polynomial network is a recent machine learning technique used to develop post-processing models. It decom-poses and substitutes for the general linear Partial Differential Equation being able to describe the local atmospheric dynamics which is too complex to be mod-elled by standard soft-computing. The complete derivative formula is decom-posed, using a multi-layer polynomial network structure, into specific sub-PDE solutions of the unknown node sum functions. The sum PDE models, using a polynomial PDE substitution based on Operational Calculus, represent spatial da-ta relations between the relevant meteorological inputs-&gt;output quantities. The proposed forecasts post-processing is based on the 2-stage approach of the Per-fect Prog method used routinely in meteorology. The original procedure is ex-tended with initial estimations of the optimal numbers of training days whose lat-est data observations are used to elicit daily prediction models in the 1st stage. De-termination of the optimal models initialization time allows for improvements in the middle-term numerical forecasts of wind speed in prevailing more or less set-tled weather. In the 2nd stage the correction model is applied to forecasts of the training input variables to calculate 24-hour prediction series of the target wind speed at the corresponding time.

Czech name

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Czech description

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Type

J_ost - Miscellaneous article in a specialist periodical

CEP classification

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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)

Publication year

2018

Confidentiality

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

Name of the periodical

Advances in Intelligent Systems and Computing. Volume 923

ISSN

2194-5357

e-ISSN

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Volume of the periodical

923

Issue of the periodical within the volume

únor-březen, 2019

Country of publishing house

CH - SWITZERLAND

Number of pages

10

Pages from-to

1-10

UT code for WoS article

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EID of the result in the Scopus database

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