PV power intra-day predictions using PDE models of polynomial networks based on operational calculus

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F20%3A10244920" target="_blank" >RIV/61989100:27240/20:10244920 - isvavai.cz</a>

Výsledek na webu

<a href="https://digital-library.theiet.org/content/journals/10.1049/iet-rpg.2019.1208" target="_blank" >https://digital-library.theiet.org/content/journals/10.1049/iet-rpg.2019.1208</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1049/iet-rpg.2019.1208" target="_blank" >10.1049/iet-rpg.2019.1208</a>

Jazyk výsledku

angličtina

Název v původním jazyce

PV power intra-day predictions using PDE models of polynomial networks based on operational calculus

Popis výsledku v původním jazyce

Precise daily forecasts of photo-voltaic (PV) power production are necessary for its planning, utilisation and integration into the electrical grid. PV power is conditioned by the current amount of specific solar radiation components. Numerical weather prediction systems are usually run every 6 h and provide only rough local prognoses of cloudiness with a delay. Statistical methods can predict PV power, considering a specific plant situation. Their intra-day models are usually more precise if rely only on the latest data observations and power measurements. Differential polynomial neural network (D-PNN) is a novel neuro-computing technique based on analogies with brain pulse signal processing. D-PNN decomposes the general partial differential equation (PDE), being able to describe the local atmospheric dynamics, into specific sub-PDEs in its nodes. These are converted using adapted procedures of operational calculus to obtain the Laplace images of unknown node functions, which are inverse L-transformed to obtain the originals. D-PNN can select from dozens of input variables to produce applicable sum PDE components which can extend, one by one, its composite models towards the optima. The PDE models are developed with historical spatial data from the estimated optimal numbers of the last days for each 1-9-h inputs-output time-shift to predict clear sky index in the trained time-horizon.

Název v anglickém jazyce

PV power intra-day predictions using PDE models of polynomial networks based on operational calculus

Popis výsledku anglicky

Precise daily forecasts of photo-voltaic (PV) power production are necessary for its planning, utilisation and integration into the electrical grid. PV power is conditioned by the current amount of specific solar radiation components. Numerical weather prediction systems are usually run every 6 h and provide only rough local prognoses of cloudiness with a delay. Statistical methods can predict PV power, considering a specific plant situation. Their intra-day models are usually more precise if rely only on the latest data observations and power measurements. Differential polynomial neural network (D-PNN) is a novel neuro-computing technique based on analogies with brain pulse signal processing. D-PNN decomposes the general partial differential equation (PDE), being able to describe the local atmospheric dynamics, into specific sub-PDEs in its nodes. These are converted using adapted procedures of operational calculus to obtain the Laplace images of unknown node functions, which are inverse L-transformed to obtain the originals. D-PNN can select from dozens of input variables to produce applicable sum PDE components which can extend, one by one, its composite models towards the optima. The PDE models are developed with historical spatial data from the estimated optimal numbers of the last days for each 1-9-h inputs-output time-shift to predict clear sky index in the trained time-horizon.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

10200 - Computer and information sciences

Projekt

<a href="/cs/project/EF17_049%2F0008425" target="_blank" >EF17_049/0008425: Platforma pro výzkum orientovaný na Průmysl 4.0 a robotiku v ostravské aglomeraci</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název periodika

IET Renewable Power Generation

ISSN

1752-1416

e-ISSN

—

Svazek periodika

14

Číslo periodika v rámci svazku

8

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

8

Strana od-do

"1405 – 1412"

Kód UT WoS článku

000540464900019

EID výsledku v databázi Scopus

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