PV Energy Prediction in 24 h Horizon Using Modular Models Based on Polynomial Conversion of the L-Transform PDE Derivatives in Node-by-Node-Evolved Binary-Tree Networks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F22%3A10249955" target="_blank" >RIV/61989100:27240/22:10249955 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/2673-4591/18/1/34/htm" target="_blank" >https://www.mdpi.com/2673-4591/18/1/34/htm</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/engproc2022018034" target="_blank" >10.3390/engproc2022018034</a>

Result language

angličtina

Original language name

PV Energy Prediction in 24 h Horizon Using Modular Models Based on Polynomial Conversion of the L-Transform PDE Derivatives in Node-by-Node-Evolved Binary-Tree Networks

Original language description

Accurate daily photovoltaic (PV) power predictions are challenging as near-ground atmospheric processes include complicated chaotic interactions among local factors (ground temperature, cloudiness structure, humidity, visibility factor, etc.). Fluctuations in solar irradiance resulting from the cloud structure dynamics are influenced by many uncertain parameters, which can be described by differential equations. Recent artificial intelligence (AI) computational tools allow us to transform and post-validate forecast data from numerical weather prediction (NWP) systems to estimate PV power generation in relation to on-site local specifics. However, local NWP models are usually produced each six hours to simulate the progress of main weather quantities in a medium-scale target area. Their delay usually covers several hours, further increasing the inadequate operational quality required in PV plants. All-day prediction models perform better, if they are developed with the last historical weather and PV data. Differential polynomial neural network (D-PNN) is a recently designed computational method, based on a new learning approach, which allows us to represent complicated data relations contained in local weather patterns to account for irregular phenomena. D-PNN combines two-input variables to split the partial differential equation (PDE), defined in the general order k and n variables, into partition elements of two-input node PDEs of recognized order and type. The node-determined sub-PDEs can be easily converted using operator calculus (OC), in several types of predefined convert schemes, to define unknown node functions expressed in the Laplace images form Application of the inverse L-transformation formula to the L-converts results in obtaining the prime function originals. D-PNN elicits a progressive modular tree structure to assess one-by-one the optimal PDE node solutions to be inserted in the sum output of the overall expanded computing model. Statistical modular models are the result of learning schemes of preadjusted day data records from various observational localities. They are applied after testing to the rest of unseen daily series of known data to compute estimations of clear-sky index (CSI) in the 24 h input-delayed time-sequences.

Czech name

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Czech description

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Type

D - Article in proceedings

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

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2022

Confidentiality

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

Article name in the collection

The 8th International Conference on Time Series and Forecasting : Gran Canaria, Spain : 27–30 June 2022

ISBN

978-3-0365-5451-8

ISSN

2673-4591

e-ISSN

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Number of pages

10

Pages from-to

1-10

Publisher name

MDPI Open Access Publishing

Place of publication

Basilej

Event location

Gran Canaria

Event date

Jun 27, 2022

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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