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

• Published in: Energies (Basel) Vol. 18; no. 17; p. 4611
• Main Authors: Jahan, Ibrahim, Blazek, Vojtech, Walendziuk, Wojciech, Snasel, Vaclav, Prokop, Lukas, Misak, Stanislav
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2025
• Subjects: Accuracy; Algorithms; Alternative energy sources; Classification; Comparative analysis; Decision trees; Deep learning; Energy resources; forecasting; Forecasting techniques; Fourier transforms; Home appliances; Machine learning; Neural networks; off-grid system; Photovoltaic cells; power quality; Regression analysis; Renewable resources; smart grids; Weather forecasting; Wind power; New York; United States
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en18174611

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.