Use of Machine Learning for Solar Power Generation Prediction in the Field of Alternative Renewable Energy Sources

This study focused on the application of supervised learning in the field of renewable energy, specifically for predicting daily solar irradiance in Neiva, department of Huila, Colombia. To this end, decision tree and artificial neural network (DT and ANN, respectively) models were trained and tested using the online tool Google Colab. The main objective was based on the need to optimize energy planning processes at local and regional levels, motivated by the increase in demand for the integration of non-conventional energy sources and the spatial–temporal variability in solar resources in the country. A dataset consisting of 366 daily records for the year 2024 was obtained from the NASA POWER database at the geographic coordinates (2.930079, −75.255650) and used for training and evaluating the proposed models. Statistical and cleaning techniques were used, including the treatment of outliers using the moving-window median for the latter. Metrics, such as mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2), were used to evaluate the models. Data inclusion and exclusion criteria were applied to ensure the quality and validity of the observations. Model performance was evaluated using a randomized Hold-Out validation strategy (90% training and 10% testing), which was repeated across multiple iterations. The performance metrics reported corresponded to the 10th iteration of the validation process after outlier treatment. Under this configuration, the DT model achieved a higher predictive performance (R2 = 0.8882) compared with the ANN model (R2 = 0.7679), demonstrating its effectiveness as a reliable approach for estimating daily solar irradiance under the studied conditions. This result was also confirmed by the decreased MAE and RMSE for the DT model, which indicated that this model performed better in predicting the real values than the ANN model. Finally, the added value of the study is to consolidate national evidence and open access tools to facilitate the development of sustainable energy policies in intermediate cities such as Neiva.