Power quality statistical predictions based on differential, deep and probabilistic learning us-ing off-grid and meteo data in 24-hour horizon

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F21%3A10248193" target="_blank" >RIV/61989100:27240/21:10248193 - isvavai.cz</a>

Výsledek na webu

<a href="https://onlinelibrary.wiley.com/doi/10.1002/er.7431" target="_blank" >https://onlinelibrary.wiley.com/doi/10.1002/er.7431</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Power quality statistical predictions based on differential, deep and probabilistic learning us-ing off-grid and meteo data in 24-hour horizon

Popis výsledku v původním jazyce

Off-grid systems using Renewable Energy (RE) are dependent on the stochastic power supply, which results in a high level of uncertainty, noise and variability in the operational conditions. Power Quality (PQ) norms specify allowable variations in relevant parameters of grid systems, necessary to maintain in certain limits to guarantee their fault-tolerant states. 24-hour PQ-prediction is vital in planning of power consumption and utilization in smart off-grid houses based on RE. PQ-data for all possible combinations of grid-attached household appliances and variable out-side conditions cannot be measured completely or described exactly by physical equations. PQ-predictions on daily bases using Artificial Intelligence (AI) models are requisite because atmospheric fluctuations and anomalies in local weather primarily influence the induced power and potential operation mode in real off-grids. Load specifics and possible collisions of the switched-on power consumers together with alterations in RE production can lead to additional disturbances in PQ and the consequent instability of the autonomous system. An automatic selection algorithm can preliminary suggest or correct several eventual energy consumption variants based on load time-shifting, to allow efficient utilization of the predicted Photo-Voltaic Power (PVP). PQ models can after examine feasible daily load scenarios, offered by the system or re-defined by the users, scheduling selected equipment in the optimal switch-intervals to ensure primarily the system stability. Users will have several practical options to choose or combine the best ones to meet their demand in the optimal PQ and load planning strategy. This 2-step self-determination and verification procedure can help in effective operation of smart-grids in consolidation of their future state, balancing its power demands with local RE potential. The AI statistical models were evolved with the pre-assessed lengths of daily data periods. After that, they are applied to the last unseen data series, used in testing, to predict one-step sequences of the target PQ-parameters in the trained all-day horizon. Parametric C++ application software with applied PQ and weather data is available for free to allow reproducibility of the results.

Název v anglickém jazyce

Power quality statistical predictions based on differential, deep and probabilistic learning us-ing off-grid and meteo data in 24-hour horizon

Popis výsledku anglicky

Off-grid systems using Renewable Energy (RE) are dependent on the stochastic power supply, which results in a high level of uncertainty, noise and variability in the operational conditions. Power Quality (PQ) norms specify allowable variations in relevant parameters of grid systems, necessary to maintain in certain limits to guarantee their fault-tolerant states. 24-hour PQ-prediction is vital in planning of power consumption and utilization in smart off-grid houses based on RE. PQ-data for all possible combinations of grid-attached household appliances and variable out-side conditions cannot be measured completely or described exactly by physical equations. PQ-predictions on daily bases using Artificial Intelligence (AI) models are requisite because atmospheric fluctuations and anomalies in local weather primarily influence the induced power and potential operation mode in real off-grids. Load specifics and possible collisions of the switched-on power consumers together with alterations in RE production can lead to additional disturbances in PQ and the consequent instability of the autonomous system. An automatic selection algorithm can preliminary suggest or correct several eventual energy consumption variants based on load time-shifting, to allow efficient utilization of the predicted Photo-Voltaic Power (PVP). PQ models can after examine feasible daily load scenarios, offered by the system or re-defined by the users, scheduling selected equipment in the optimal switch-intervals to ensure primarily the system stability. Users will have several practical options to choose or combine the best ones to meet their demand in the optimal PQ and load planning strategy. This 2-step self-determination and verification procedure can help in effective operation of smart-grids in consolidation of their future state, balancing its power demands with local RE potential. The AI statistical models were evolved with the pre-assessed lengths of daily data periods. After that, they are applied to the last unseen data series, used in testing, to predict one-step sequences of the target PQ-parameters in the trained all-day horizon. Parametric C++ application software with applied PQ and weather data is available for free to allow reproducibility of the results.

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

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

International Journal of Energy Research

ISSN

0363-907X

e-ISSN

1099-114X

Svazek periodika

46

Číslo periodika v rámci svazku

8

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

15

Strana od-do

1-15

Kód UT WoS článku

000712589200001

EID výsledku v databázi Scopus

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