Power Quality Parameters Forecasting Based on SOM Maps with KNN Algorithm and Decision Tree

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27730%2F23%3A10253805" target="_blank" >RIV/61989100:27730/23:10253805 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27240/23:10253805

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/10149269" target="_blank" >https://ieeexplore.ieee.org/document/10149269</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/EPE58302.2023.10149269" target="_blank" >10.1109/EPE58302.2023.10149269</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Power Quality Parameters Forecasting Based on SOM Maps with KNN Algorithm and Decision Tree

Popis výsledku v původním jazyce

This study tested four forecasting models combined with 3x3 SOM maps for predicting power quality parameters (PQPs) named decision tree (DT), KNN algorithm, bagging decision tree (BGDT), and boosting decision tree (BODT). The input variables used are weather conditions (air temperature, wind speed, air pressure, Ultraviolet, solar irradiance) with states of four types of home appliances (AC heating, light, fridge, TV) represented by one decimal number. Target Outputs are Power Voltage (U), total harmonic distortion of voltage (THDu), total harmonic distortion of current (THDi), power factor (PF), and power load (PL). The experiments were carried out in two stages: in the first stage, clustering dataset using self-organizing maps (SOM), 3x3 SOM in total nine hexagon nodes was used. In the second stage, inside each node builds four forecasting models: decision tree (DT), K-Nearest Neighbor(KNN) algorithm, bagging decision tree (BGDT), and boosting decision tree (BODT). Root Mean Square Error (RMSE) was used for evaluating the performance of studied models.

Název v anglickém jazyce

Power Quality Parameters Forecasting Based on SOM Maps with KNN Algorithm and Decision Tree

Popis výsledku anglicky

This study tested four forecasting models combined with 3x3 SOM maps for predicting power quality parameters (PQPs) named decision tree (DT), KNN algorithm, bagging decision tree (BGDT), and boosting decision tree (BODT). The input variables used are weather conditions (air temperature, wind speed, air pressure, Ultraviolet, solar irradiance) with states of four types of home appliances (AC heating, light, fridge, TV) represented by one decimal number. Target Outputs are Power Voltage (U), total harmonic distortion of voltage (THDu), total harmonic distortion of current (THDi), power factor (PF), and power load (PL). The experiments were carried out in two stages: in the first stage, clustering dataset using self-organizing maps (SOM), 3x3 SOM in total nine hexagon nodes was used. In the second stage, inside each node builds four forecasting models: decision tree (DT), K-Nearest Neighbor(KNN) algorithm, bagging decision tree (BGDT), and boosting decision tree (BODT). Root Mean Square Error (RMSE) was used for evaluating the performance of studied models.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20200 - Electrical engineering, Electronic engineering, Information engineering

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proccedings of the 2023 23rd International Scientific Conference on Electric Power Engineering (EPE)

ISBN

979-8-3503-3594-1

ISSN

2376-5623

e-ISSN

2376-5631

Počet stran výsledku

6

Strana od-do

1-6

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Brno

Datum konání akce

24. 5. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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