Forecasting Power Quality Parameters Using Decision Tree and KNN Algorithms in a Small-Scale Off-Grid Platform

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10258543" target="_blank" >RIV/61989100:27240/25:10258543 - isvavai.cz</a>

Alternative codes found

RIV/61989100:27730/25:10258543

Result on the web

<a href="https://www.mdpi.com/1996-1073/18/17/4611" target="_blank" >https://www.mdpi.com/1996-1073/18/17/4611</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/en18174611" target="_blank" >10.3390/en18174611</a>

Result language

angličtina

Original language name

Forecasting Power Quality Parameters Using Decision Tree and KNN Algorithms in a Small-Scale Off-Grid Platform

Original language description

This article presents the results of a performance comparison of four forecasting methods for prediction of electric power quality parameters (PQPs) in small-scale off-grid environments. Forecasting PQPs is crucial in supporting smart grid control and planning strategies by enabling better management, enhancing system reliability, and optimizing the integration of distributed energy resources. The following methods were compared: Bagging Decision Tree (BGDT), Boosting Decision Tree (BODT), and the K-Nearest Neighbor (KNN) algorithm with k-5 and k-10 nearest neighbors considered by the algorithm when making a prediction. The main goal of this study is to find a relation between the input variables (weather conditions, first and second back steps of PQPs, and consumed power of home appliances) and the power quality parameters as target outputs. The studied PQPs are the amplitude of power voltage (U), Voltage Total Harmonic Distortion (THDu), Current Total Harmonic Distortion (THDi), Power Factor (PF), and Power Load (PL). The Root Mean Square Error (RMSE) was used to evaluate the forecasting results. BGDT accomplished better forecasting results for THDu, THDi, and PF. Only BODT obtained a good forecasting result for PL. The KNN (k = 5) algorithm obtained a good result for PF prediction. The KNN (k = 10) algorithm predicted acceptable results for U and PF. The computation time was considered, and the KNN algorithm took a shorter time than ensemble decision trees.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20200 - Electrical engineering, Electronic engineering, Information engineering

Project

<a href="/en/project/EH23_021%2F0008759" target="_blank" >EH23_021/0008759: Increasing the resilience of power grids in the context of decarbonisation, decentralisation and sustainable socio-economic development</a><br>

Continuities

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

Publication year

2025

Confidentiality

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

Name of the periodical

Energies

ISSN

1996-1073

e-ISSN

1996-1073

Volume of the periodical

18

Issue of the periodical within the volume

17

Country of publishing house

CH - SWITZERLAND

Number of pages

27

Pages from-to

1-27

UT code for WoS article

001569687300001

EID of the result in the Scopus database

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