Photo-Voltaic Power Daily Statistical Predictions Using PDE Models of Stepwise Evolved Polynomial Networks with the Sum PDE Partition and L-transform Substitution

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F22%3A10247884" target="_blank" >RIV/61989100:27240/22:10247884 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-030-87178-9_6" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-030-87178-9_6</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-87178-9_6" target="_blank" >10.1007/978-3-030-87178-9_6</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Photo-Voltaic Power Daily Statistical Predictions Using PDE Models of Stepwise Evolved Polynomial Networks with the Sum PDE Partition and L-transform Substitution

Popis výsledku v původním jazyce

Computational methods based on Artificial Intelligence (AI) can convert or post-process data produced by Numerical Weather Prediction (NWP) systems to predict Photo-Voltaic (PV) power in consideration of a plant specific situation. Their statistical models, developed with historical data series, are more precise if they rely on the latest weather observations and PV measurements. NWP models are usually run every 6 h with the prognoses delayed a few hours. Moreover, their accuracy is mostly inadequate for PV plant actual operation. Differential Polynomial Neural Network (D-PNN) is a novel biologically inspired neuro-computing technique which can model complex patterns without reducing significantly the data dimensionality as standard regression or soft-computing does. D-PNN combines appropriate 2-inputs to decompose the n-variable Partial Differential Equation (PDE), being able to describe the atmospheric dynamics, into a set of particular sub-PDEs in its nodes. The selected 2-variable composed PDEs are converted using adapted procedures of Operational Calculus (OC) to obtain the Laplace images of unknown node functions, which are inverse L-transformed to obtain the originals. D-PNN produces applicable sum PDE components in its nodes to extend step by step its composite models towards the optima. The compared AI models are developed with spatial historical data from the estimated optimal daily training periods to process the last day input data series and predict Clear Sky Index (CSI) at the corresponding 24-h horizon.

Název v anglickém jazyce

Photo-Voltaic Power Daily Statistical Predictions Using PDE Models of Stepwise Evolved Polynomial Networks with the Sum PDE Partition and L-transform Substitution

Popis výsledku anglicky

Computational methods based on Artificial Intelligence (AI) can convert or post-process data produced by Numerical Weather Prediction (NWP) systems to predict Photo-Voltaic (PV) power in consideration of a plant specific situation. Their statistical models, developed with historical data series, are more precise if they rely on the latest weather observations and PV measurements. NWP models are usually run every 6 h with the prognoses delayed a few hours. Moreover, their accuracy is mostly inadequate for PV plant actual operation. Differential Polynomial Neural Network (D-PNN) is a novel biologically inspired neuro-computing technique which can model complex patterns without reducing significantly the data dimensionality as standard regression or soft-computing does. D-PNN combines appropriate 2-inputs to decompose the n-variable Partial Differential Equation (PDE), being able to describe the atmospheric dynamics, into a set of particular sub-PDEs in its nodes. The selected 2-variable composed PDEs are converted using adapted procedures of Operational Calculus (OC) to obtain the Laplace images of unknown node functions, which are inverse L-transformed to obtain the originals. D-PNN produces applicable sum PDE components in its nodes to extend step by step its composite models towards the optima. The compared AI models are developed with spatial historical data from the estimated optimal daily training periods to process the last day input data series and predict Clear Sky Index (CSI) at the corresponding 24-h horizon.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

Lecture Notes in Networks and Systems. Volume 330

ISBN

978-3-030-87177-2

ISSN

2367-3370

e-ISSN

2367-3389

Počet stran výsledku

10

Strana od-do

55-65

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Soči

Datum konání akce

30. 9. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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