Forecasting Power Quality Parameters Using Decision Tree and KNN Algorithms in a Small-Scale Off-Grid Platform
Year: 2025
Type: article
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 (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.
Source: Energies
DOI: 10.3390/en18174611
Language: English
Topic: Power Quality and Harmonics
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W4413907633