Power Quality Day-Ahead Evaluation in MicrogridPower Consumption Plans Using L-Transform Differential, LSTM Deep, and EnsembleTree Learning Based on NWP Replacements

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10258474" target="_blank" >RIV/61989100:27240/25:10258474 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1002/eng2.70333" target="_blank" >https://doi.org/10.1002/eng2.70333</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/eng2.70333" target="_blank" >10.1002/eng2.70333</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Power Quality Day-Ahead Evaluation in MicrogridPower Consumption Plans Using L-Transform Differential, LSTM Deep, and EnsembleTree Learning Based on NWP Replacements

Popis výsledku v původním jazyce

Detachable smart systems contingent on unsteady renewable energy (RE) require timely planning and control in power demand and storage on daily scheduling. Power quality (PQ) denotes the fault-free operation of grids in various modes of household use. The great variability in detached system states and exponential increase in combinatorial load under uncertain environments make optimization difficult. Algebraic equations cannot define exact relations between PQ parameters and observational data. For that reason, statistical artificial intelligence (AI) helps to model the characteristics of undefined systems in local atmospheric and terrain uncertainties. The RE production and operational conditions primarily determine the first plans of power consumption, which are re-evaluated and optimized secondary to PQ. User needs are accommodated and balanced with daily energy and charge potential in acceptable terms. The main question is the first efficient algorithmizing of load scheduling tasks and their consequent day-to-day verification in the proposed two-stage PQ irregularity revealing tool. A new unconventional neurocomputing strategy, called Differential Learning (DfL), allows modeling high dynamical PQ characteristics without behavioral knowledge, considering only input–output data. The DfL results were evaluated with deep and stochastic learning. Their models produce similar output, except for a deep learning deficiency in voltage. The numerical results show DfL superiority and better stability in computing power and power factor (avg. RMSE = 0.29 kW and 0.032), while probabilistic learning predominates in voltage (RMSE = 1.95 V). After an initial pre-processing of the training series, the detected weather and binary-coded load combination time interval samples are used in training. AI statistics allow processing entire 24-h forecast series, replacing related real-valued quantities available in the learning stage to compute final PQ targets at the corresponding prediction times. Parametric C++ software including measured system and environment observation data is accessible in public data archives to allow for additional experimental comparisons and investigations.

Název v anglickém jazyce

Power Quality Day-Ahead Evaluation in MicrogridPower Consumption Plans Using L-Transform Differential, LSTM Deep, and EnsembleTree Learning Based on NWP Replacements

Popis výsledku anglicky

Detachable smart systems contingent on unsteady renewable energy (RE) require timely planning and control in power demand and storage on daily scheduling. Power quality (PQ) denotes the fault-free operation of grids in various modes of household use. The great variability in detached system states and exponential increase in combinatorial load under uncertain environments make optimization difficult. Algebraic equations cannot define exact relations between PQ parameters and observational data. For that reason, statistical artificial intelligence (AI) helps to model the characteristics of undefined systems in local atmospheric and terrain uncertainties. The RE production and operational conditions primarily determine the first plans of power consumption, which are re-evaluated and optimized secondary to PQ. User needs are accommodated and balanced with daily energy and charge potential in acceptable terms. The main question is the first efficient algorithmizing of load scheduling tasks and their consequent day-to-day verification in the proposed two-stage PQ irregularity revealing tool. A new unconventional neurocomputing strategy, called Differential Learning (DfL), allows modeling high dynamical PQ characteristics without behavioral knowledge, considering only input–output data. The DfL results were evaluated with deep and stochastic learning. Their models produce similar output, except for a deep learning deficiency in voltage. The numerical results show DfL superiority and better stability in computing power and power factor (avg. RMSE = 0.29 kW and 0.032), while probabilistic learning predominates in voltage (RMSE = 1.95 V). After an initial pre-processing of the training series, the detected weather and binary-coded load combination time interval samples are used in training. AI statistics allow processing entire 24-h forecast series, replacing related real-valued quantities available in the learning stage to compute final PQ targets at the corresponding prediction times. Parametric C++ software including measured system and environment observation data is accessible in public data archives to allow for additional experimental comparisons and investigations.

Druh

J_imp - Článek v periodiku v databázi Web of Science

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

2025

Kód důvěrnosti údajů

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

Název periodika

Engineering Reports

ISSN

2577-8196

e-ISSN

2577-8196

Svazek periodika

7

Číslo periodika v rámci svazku

9

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

15

Strana od-do

1-15

Kód UT WoS článku

001585376600011

EID výsledku v databázi Scopus

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