Forecasting Univariate Solar Irradiance using Machine learning models: A case study of two Andean Cities

The integration of solar energy into power systems is essential for the future sustainability of power systems, particularly for isolated systems, such as microgrids, where establishing a primary transmission network is difficult. Therefore, the development of prediction methods becomes crucial to enable accurate forecasting of solar energy generation, facilitating efficient planning and operation of these systems and ensuring their long-term viability. This study proposes distinct forecasting models for solar irradiance forecasting: an autoregressive (AR) model, a Random Forest model, and a Long Short-Term Memory (LSTM) neural network. The methodology involves preprocessing the historical solar irradiance data and performing feature engineering to extract relevant input features. The architectural design, hyperparameter tuning, and training procedures of each model are discussed in detail. The findings indicate that the LSTM model exhibits enhanced performance compared to the AR model, while maintaining similar predictive accuracy to the Random Forest model in forecasting global solar irradiance. Both models yield a mean absolute percentage error of roughly 25%, with the LSTM exhibiting the lower error rate. Moreover, the LSTM model showcases an advancement over the AR model, resulting in a reduction of approximately 10 W/m2 for both root mean square error and mean absolute error. This finding highlights the effectiveness of LSTM networks in capturing long-term dependencies for accurate solar irradiance forecasting. Furthermore, an analysis of the models' interpretability is conducted, offering valuable insights into the key factors that contribute to the shaping of solar irradiance patterns. These insights hold practical significance for the optimization of renewable energy systems.