Comparação do desempenho dos algoritmos RF e KNN na previsão da energia gerada por quatro tecnologias fotovoltaicas - Azevedo dos Santos, José Airton; Brun Polo, Aldino Normelio (Latin American Journal of Energy Research, 2025)
Abstract: This research aims to evaluate and compare the forecasting performance of photovoltaic energy generation using the Random Forest (RF) and K-Nearest Neighbors (KNN) algorithms, based on a dataset with limited observations. The dataset, obtained from a solarimetric station located at the Federal Technological University of Paraná, includes historical series of electricity generation from monocrystalline silicon (m-Si), polycrystalline silicon (p-Si), copper indium gallium diselenide (CIGS), and cadmium telluride (CdTe) technologies, covering the period from January 2020 to December 2023. To assess the performance of the RF and KNN models, the Mean Absolute Percentage Error (MAPE) metric was used. The results showed that, for a 4-month forecasting horizon, the Random Forest model outperformed the KNN model across all technologies.
Keywords: machine learning; photovoltaics; predictive models