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

Result code in 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>

Alternative codes found

RIV/61989100:27730/18:10237629

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

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

IEEE Transactions on Sustainable Energy

ISSN

1949-3029

e-ISSN

—

Volume of the periodical

9

Issue of the periodical within the volume

4

Country of publishing house

US - UNITED STATES

Number of pages

11

Pages from-to

1529-1539

UT code for WoS article

000445275900004

EID of the result in the Scopus database

2-s2.0-85041675702