Power quality 24-hour prediction using differential, deep and statistics machine learning based on weather data in an off-grid

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10250150" target="_blank" >RIV/61989100:27240/23:10250150 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.sciencedirect.com/science/article/pii/S0016003222004586" target="_blank" >http://www.sciencedirect.com/science/article/pii/S0016003222004586</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jfranklin.2022.06.048" target="_blank" >10.1016/j.jfranklin.2022.06.048</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Power quality 24-hour prediction using differential, deep and statistics machine learning based on weather data in an off-grid

Popis výsledku v původním jazyce

Prediction of Power Quality (PQ) on a daily basis is inevitable in planning Renewable Energy (RE) supply and scheduling the assumed power load in smart off-grid autonomous systems. Various combinations of the attached household appliances lead to specific operating states, related to the charge and load switching-time in different out-side conditions. Complexity and irregularities require modelling with the use of Artificial Intelligence (AI) to represent the uncertainty in load transitions and RE power oscillations. These specifics cannot be numerically solved with respect to unexpected states in detached micro-grid systems showing great variability. Recent Differential Learning, developed by the author, using a novel designed neuro-computing approach, enables one to model high non-linear and indefinable chaotic physical systems. The optimal training periods of day-set records were initially pre-assessed, based on AI input-output statistics. This model initialization allows operable day-ahead PQ-predictions in processing the last 24-hour series in one sequence. The proposed 2-level PQ-management evaluates initial load scheduling plans, based on available RE and storage sources. System efficiency and failure-free operations are planned considering the first estimate of its day-ahead PVP supply and secondary PQ verification of the provided load utilization schemes, under various state charges and RE production limits. This is a novelty with a notable incremental improvement in the published combinatorial optimization algorithms, only scheduling applicable load components, and reassessing the allowable power-day resources. Off-grid optimal operations can be determined and regulated by an adequate early morning PQ evaluation in RE utilization. A C++ parametric software, using differential learning to evolve PQ-models, historical PQ &amp; meteo-data sets are at disposal to enable additional comparisons with the presented models. (C) 2022 The Franklin Institute

Název v anglickém jazyce

Power quality 24-hour prediction using differential, deep and statistics machine learning based on weather data in an off-grid

Popis výsledku anglicky

Prediction of Power Quality (PQ) on a daily basis is inevitable in planning Renewable Energy (RE) supply and scheduling the assumed power load in smart off-grid autonomous systems. Various combinations of the attached household appliances lead to specific operating states, related to the charge and load switching-time in different out-side conditions. Complexity and irregularities require modelling with the use of Artificial Intelligence (AI) to represent the uncertainty in load transitions and RE power oscillations. These specifics cannot be numerically solved with respect to unexpected states in detached micro-grid systems showing great variability. Recent Differential Learning, developed by the author, using a novel designed neuro-computing approach, enables one to model high non-linear and indefinable chaotic physical systems. The optimal training periods of day-set records were initially pre-assessed, based on AI input-output statistics. This model initialization allows operable day-ahead PQ-predictions in processing the last 24-hour series in one sequence. The proposed 2-level PQ-management evaluates initial load scheduling plans, based on available RE and storage sources. System efficiency and failure-free operations are planned considering the first estimate of its day-ahead PVP supply and secondary PQ verification of the provided load utilization schemes, under various state charges and RE production limits. This is a novelty with a notable incremental improvement in the published combinatorial optimization algorithms, only scheduling applicable load components, and reassessing the allowable power-day resources. Off-grid optimal operations can be determined and regulated by an adequate early morning PQ evaluation in RE utilization. A C++ parametric software, using differential learning to evolve PQ-models, historical PQ &amp; meteo-data sets are at disposal to enable additional comparisons with the presented models. (C) 2022 The Franklin Institute

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

2023

Kód důvěrnosti údajů

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

Název periodika

Journal of the Franklin Institute - Engineering and Applied Mathematics

ISSN

0016-0032

e-ISSN

—

Svazek periodika

360

Číslo periodika v rámci svazku

17

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

25

Strana od-do

13712-13736

Kód UT WoS článku

000000000000000

EID výsledku v databázi Scopus

2-s2.0-85135356206