Direct Wind Power Forecasting using a Polynomial Decomposition of the General Differential Equation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F18%3A10237629" target="_blank" >RIV/61989100:27240/18:10237629 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27730/18:10237629

Výsledek na webu

<a href="http://ieeexplore.ieee.org/document/8260922" target="_blank" >http://ieeexplore.ieee.org/document/8260922</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TSTE.2018.2794515" target="_blank" >10.1109/TSTE.2018.2794515</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Direct Wind Power Forecasting using a Polynomial Decomposition of the General Differential Equation

Popis výsledku v původním jazyce

The wind power is primarily induced by local wind speed, whose accurate daily forecasts are important for the planning of the unstable power generation and its integration into the electrical grid. The main problem of the wind speed or direct output power forecasting is its intermittent nature due to the high correlation with chaotic large-scale pattern atmospheric circulation processes which together with local characteristics and anomalies largely influence its temporal-flow. Numerical global weather systems solve sets of differential equations to model the time-change of each grid cell in several atmospheric layers. They provide only rough short-term surface wind speed prognoses which are not entirely adequate to specific local conditions, e.g. the wind farm siting, surrounding terrain and ground level (hub height). Statistical methods using historical observations can particularize daily forecasts or calculate independent predictions for several hours. Extended polynomial networks can produce rational substitution sum terms, in all the nodes in consideration of data samples, to decompose and substitute for the general linear partial differential equation, being able to describe the local atmospheric dynamics. The designed method using the inverse Laplace transformation aims at the formation of stand-alone spatial derivative models which represent current local weather conditions for a trained input-output time-shift to predict the daily wind power up to 12 hours ahead. The proposed intra-day multi-step predictions are more precise than those based on middle-term numerical forecasts or adaptive intelligence techniques using local time-series, which are worthless beyond a few hours.

Název v anglickém jazyce

Direct Wind Power Forecasting using a Polynomial Decomposition of the General Differential Equation

Popis výsledku anglicky

The wind power is primarily induced by local wind speed, whose accurate daily forecasts are important for the planning of the unstable power generation and its integration into the electrical grid. The main problem of the wind speed or direct output power forecasting is its intermittent nature due to the high correlation with chaotic large-scale pattern atmospheric circulation processes which together with local characteristics and anomalies largely influence its temporal-flow. Numerical global weather systems solve sets of differential equations to model the time-change of each grid cell in several atmospheric layers. They provide only rough short-term surface wind speed prognoses which are not entirely adequate to specific local conditions, e.g. the wind farm siting, surrounding terrain and ground level (hub height). Statistical methods using historical observations can particularize daily forecasts or calculate independent predictions for several hours. Extended polynomial networks can produce rational substitution sum terms, in all the nodes in consideration of data samples, to decompose and substitute for the general linear partial differential equation, being able to describe the local atmospheric dynamics. The designed method using the inverse Laplace transformation aims at the formation of stand-alone spatial derivative models which represent current local weather conditions for a trained input-output time-shift to predict the daily wind power up to 12 hours ahead. The proposed intra-day multi-step predictions are more precise than those based on middle-term numerical forecasts or adaptive intelligence techniques using local time-series, which are worthless beyond a few hours.

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

IEEE Transactions on Sustainable Energy

ISSN

1949-3029

e-ISSN

—

Svazek periodika

9

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

11

Strana od-do

1529-1539

Kód UT WoS článku

000445275900004

EID výsledku v databázi Scopus

2-s2.0-85041675702