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

Ibrahim Jahan (), Vojtech Blazek (), Wojciech Walendziuk, Vaclav Snasel, Lukas Prokop and Stanislav Misak
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Ibrahim Jahan: ENET Centre, CEET, VSB—Technical University of Ostrava, 17. Listopadu 2172/15, 708 00 Ostrava, Czech Republic
Vojtech Blazek: ENET Centre, CEET, VSB—Technical University of Ostrava, 17. Listopadu 2172/15, 708 00 Ostrava, Czech Republic
Wojciech Walendziuk: Faculty of Electrical Engineering, Bialystok University of Technology, ul. Wiejska 45A, 15-351 Bialystok, Poland
Vaclav Snasel: Computer Science Department, Faculty of Electrical Engineering and Computer Science, VSB—Technical University of Ostrava, 708 00 Ostrava, Czech Republic
Lukas Prokop: ENET Centre, CEET, VSB—Technical University of Ostrava, 17. Listopadu 2172/15, 708 00 Ostrava, Czech Republic
Stanislav Misak: ENET Centre, CEET, VSB—Technical University of Ostrava, 17. Listopadu 2172/15, 708 00 Ostrava, Czech Republic

Energies, 2025, vol. 18, issue 17, 1-27

Abstract: 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 ( T H D u ), Current Total Harmonic Distortion ( T H D i ), Power Factor ( P F ), and Power Load ( P L ). The Root Mean Square Error (RMSE) was used to evaluate the forecasting results. BGDT accomplished better forecasting results for T H D u , T H D i , and P F . Only BODT obtained a good forecasting result for P L . The KNN ( k = 5) algorithm obtained a good result for P F prediction. The KNN ( k = 10) algorithm predicted acceptable results for U and P F . The computation time was considered, and the KNN algorithm took a shorter time than ensemble decision trees.

Keywords: power quality; off-grid system; forecasting; machine learning; smart grids (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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