TY - JOUR
T1 - Forecasting Univariate Solar Irradiance using Machine learning models
T2 - A case study of two Andean Cities
AU - Díaz-Bedoya, Daniel
AU - González-Rodríguez, Mario
AU - Clairand, Jean Michel
AU - Serrano-Guerrero, Xavier
AU - Escrivá-Escrivá, Guillermo
N1 - Publisher Copyright:
© 2023 Elsevier Ltd
PY - 2023/11/15
Y1 - 2023/11/15
N2 - 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.
AB - 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.
KW - Deep learning
KW - Forecasting
KW - Random Forest
KW - Recurrent Neural Networks
KW - Solar energy
UR - http://www.scopus.com/inward/record.url?scp=85172690054&partnerID=8YFLogxK
U2 - 10.1016/j.enconman.2023.117618
DO - 10.1016/j.enconman.2023.117618
M3 - Article
AN - SCOPUS:85172690054
SN - 0196-8904
VL - 296
JO - Energy Conversion and Management
JF - Energy Conversion and Management
M1 - 117618
ER -