Forecasting a Photovoltaic Power Output with Ordinary Differential Equation Solutions using the “Aladin” model

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F16%3A86099462" target="_blank" >RIV/61989100:27240/16:86099462 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Forecasting a Photovoltaic Power Output with Ordinary Differential Equation Solutions using the “Aladin” model

Popis výsledku v původním jazyce

Accurate forecasting of the renewable power generation is important for the system operation, utilization and integration in the electricity grid. The photovoltaic output power is primarily dependent on the solar radiation, which short-term local forecasts, available from the numerical model “Aladin”, can enter power models, trained with corresponding real time-series of few last days, to predict the following day electricity production. Presented daily updated polynomial derivative models can describe fluctuant function relations between input solar irradiance time-series and the scalar output power, which conventional regression solutions usually fail. Differential polynomial network is a new neural network type, which can define and solve a selective form of the linear ordinary sum differential equation to model 1-variable function series. Partial sum relative fraction terms, produced in all layer nodes of the network backward structure, can substitute for the time derivatives at several time-points of data series.

Název v anglickém jazyce

Forecasting a Photovoltaic Power Output with Ordinary Differential Equation Solutions using the “Aladin” model

Popis výsledku anglicky

Accurate forecasting of the renewable power generation is important for the system operation, utilization and integration in the electricity grid. The photovoltaic output power is primarily dependent on the solar radiation, which short-term local forecasts, available from the numerical model “Aladin”, can enter power models, trained with corresponding real time-series of few last days, to predict the following day electricity production. Presented daily updated polynomial derivative models can describe fluctuant function relations between input solar irradiance time-series and the scalar output power, which conventional regression solutions usually fail. Differential polynomial network is a new neural network type, which can define and solve a selective form of the linear ordinary sum differential equation to model 1-variable function series. Partial sum relative fraction terms, produced in all layer nodes of the network backward structure, can substitute for the time derivatives at several time-points of data series.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

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

Projekt

<a href="/cs/project/LQ1602" target="_blank" >LQ1602: IT4Innovations excellence in science</a><br>

Návaznosti

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

Rok uplatnění

2016

Kód důvěrnosti údajů

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

Název statě ve sborníku

Advances in intelligent systems and computing. Volume 565

ISBN

978-3-319-60833-4

ISSN

2194-5357

e-ISSN

neuvedeno

Počet stran výsledku

10

Strana od-do

28-37

Název nakladatele

Springer

Místo vydání

Berlin

Místo konání akce

Marrákeš

Datum konání akce

21. 11. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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