Power quality validation in micro off-grid daily load using modular differential, LSTM deep, and probability statistics models processing NWP-data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10255312" target="_blank" >RIV/61989100:27240/24:10255312 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1080/21642583.2024.2395400" target="_blank" >https://doi.org/10.1080/21642583.2024.2395400</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/21642583.2024.2395400" target="_blank" >10.1080/21642583.2024.2395400</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Power quality validation in micro off-grid daily load using modular differential, LSTM deep, and probability statistics models processing NWP-data

Popis výsledku v původním jazyce

Load corrections with respect to power quality (PQ) after the first pre-estimate of Renewable Energy (RE) power consumption must ensure system-tolerant performance without malfunctions. First, acceptable daily load sequences for the attached equipment are combined and determined according to the RE potential and charge states in accommodation to user needs and normal operation. The main motivation is a consequent day-to-day verification of algorithmically scheduled power consumption tasks in the proposed two-stage optimisation according to the system resources and user needs. Statistical artificial intelligence (AI) is employed, as local atmospheric turbulences with terrain obstacles and unexpected user activity result in various operational states in real microsystems. A new unconventional neurocomputing strategy, called Differential Learning (DfL), was applied in the modelling and prediction of the high dynamical PQ parameters in an experimental RE based system according to input-output training data, without an exact specification of its behaviour. The DfL models were compared with recent deep and machine learning techniques. Prediction models were formed after an initial detection of adequate daily training intervals. The AI models are finally tested to process the complete 24-hour forecast series of related input variables used in learning, to estimate the PQ target output at the corresponding times.

Název v anglickém jazyce

Power quality validation in micro off-grid daily load using modular differential, LSTM deep, and probability statistics models processing NWP-data

Popis výsledku anglicky

Load corrections with respect to power quality (PQ) after the first pre-estimate of Renewable Energy (RE) power consumption must ensure system-tolerant performance without malfunctions. First, acceptable daily load sequences for the attached equipment are combined and determined according to the RE potential and charge states in accommodation to user needs and normal operation. The main motivation is a consequent day-to-day verification of algorithmically scheduled power consumption tasks in the proposed two-stage optimisation according to the system resources and user needs. Statistical artificial intelligence (AI) is employed, as local atmospheric turbulences with terrain obstacles and unexpected user activity result in various operational states in real microsystems. A new unconventional neurocomputing strategy, called Differential Learning (DfL), was applied in the modelling and prediction of the high dynamical PQ parameters in an experimental RE based system according to input-output training data, without an exact specification of its behaviour. The DfL models were compared with recent deep and machine learning techniques. Prediction models were formed after an initial detection of adequate daily training intervals. The AI models are finally tested to process the complete 24-hour forecast series of related input variables used in learning, to estimate the PQ target output at the corresponding times.

Druh

J_imp - Článek v periodiku v databázi Web of Science

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

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Systems Science &amp; Control Engineering

ISSN

2164-2583

e-ISSN

2164-2583

Svazek periodika

12

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

16

Strana od-do

1-16

Kód UT WoS článku

001299684500001

EID výsledku v databázi Scopus

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