Multi-site post-processing of numerical forecasts using a polynomial network substitution for the general differential equation based on operational calculus

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27730%2F18%3A10240151" target="_blank" >RIV/61989100:27730/18:10240151 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1016/j.asoc.2018.08.040" target="_blank" >https://doi.org/10.1016/j.asoc.2018.08.040</a>

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Multi-site post-processing of numerical forecasts using a polynomial network substitution for the general differential equation based on operational calculus

Popis výsledku v původním jazyce

Precise daily forecasts of local wind speed are necessary for planning of the changeable wind power production. 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. Based on a 2-stage approach of the Perfect Prog method, used routinely in meteorology, the article proposes an enhanced forecast correction procedure with initial estimations of the optimal numbers of training days whose latest data observations are used to elicit daily prediction models. Determination of this main training parameter allows for improvements in the middle-term numerical forecasts of wind speed in the majority of prediction days. Subsequently in the 2nd stage the correction model post-processes numerical forecasts of the training input variables to calculate 24-hour prediction series of the target wind speed at the corresponding time. Differential polynomial network is used to develop the test and post-processing models, which represent the current spatial data relations between the relevant meteorological inputs-&gt;output quantities. This innovative machine learning method defines and substitutes for the general linear partial differential equation being able to describe the local atmospheric dynamics which is too complex and uncertain to be represented by standard soft-computing techniques. The complete derivative formula is decomposed into specific sub-solutions of node unknown sum functions in the multi-layer polynomial network structure using Operational Calculus to model the searched separable output function.

Název v anglickém jazyce

Multi-site post-processing of numerical forecasts using a polynomial network substitution for the general differential equation based on operational calculus

Popis výsledku anglicky

Precise daily forecasts of local wind speed are necessary for planning of the changeable wind power production. 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. Based on a 2-stage approach of the Perfect Prog method, used routinely in meteorology, the article proposes an enhanced forecast correction procedure with initial estimations of the optimal numbers of training days whose latest data observations are used to elicit daily prediction models. Determination of this main training parameter allows for improvements in the middle-term numerical forecasts of wind speed in the majority of prediction days. Subsequently in the 2nd stage the correction model post-processes numerical forecasts of the training input variables to calculate 24-hour prediction series of the target wind speed at the corresponding time. Differential polynomial network is used to develop the test and post-processing models, which represent the current spatial data relations between the relevant meteorological inputs-&gt;output quantities. This innovative machine learning method defines and substitutes for the general linear partial differential equation being able to describe the local atmospheric dynamics which is too complex and uncertain to be represented by standard soft-computing techniques. The complete derivative formula is decomposed into specific sub-solutions of node unknown sum functions in the multi-layer polynomial network structure using Operational Calculus to model the searched separable output function.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

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

2018

Kód důvěrnosti údajů

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

Název periodika

Applied Soft Computing

ISSN

1568-4946

e-ISSN

—

Svazek periodika

73

Číslo periodika v rámci svazku

73

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

11

Strana od-do

192-202

Kód UT WoS článku

000450124900014

EID výsledku v databázi Scopus

—