Evaluating electrical power yield of photovoltaic solar cells with k-Nearest neighbors: A machine learning statistical analysis approach

Abstract

The increasing demand for sustainable and renewable energy solutions reflects the critical importance of advancing photovoltaic (PV) technology and its operational efficiency. In response, this study introduces a novel application of the k-Nearest Neighbor (k-NN) algorithm to assess the reliability and applicability of solar panel simulation data which aimed to classify current states of partial shading, open, and short circuit conditions, alongside regression-based analysis for predicting specific operating parameters. In this research, a published dataset that involved various PV module configurations under different environmental conditions was tested and evaluated. The k-NN technique was applied to both classify the operational status and predict performance metrics of the modules. The diagnosis model demonstrated an accuracy of 99.2 % and an F1 score of 99.2 %, indicating a high degree of reliability in identifying the operational states of PV modules. Concurrently, the regression model exhibited a Root Mean Square Error (RMSE) of 0.036 and an R2 value of unity that showcased its effectiveness in predicting the operational parameters based on the simulation data. The concluded results are further enriching the effectiveness of simulation-based data generation to be endorsed and implemented before jumping into real experimental applications, in addition to highlighting the potential applicability of k-NN and machine learning for PV cells productivity statistical analysis.