# Solar Irradiance Forecasting Using Dynamic Ensemble Selection
## 2 Related Works  
Considering the volatility of irradiation data and the importance of obtaining accurate results in power grid systems, several models have been employed to improve the accuracy of forecasts. In general, forecasting models for solar irradiance are classified into physical models, empirical, statistical, and machine learning models [27]; however, hybridization between these classes of models is also possible and tends to improve the results when compared with single model approaches [28; 29]. Physical models, such as numerical weather prediction (NWP), are complex structures that consider several aspects of the environment and the irradiance data [30]. In contrast, empirical models are less complex and often employ linear and nonlinear regressions to perform estimates [31]. However, empirical models may present limited accuracy. Statistical models, such as the autoregressive integrated and moving average model  
(ARIMA) perform temporal mappings over past data and produce forecasts, and have been used in several irradiance forecasting applications. [PERSON] et al. [32] employed a multiplicative seasonal ARIMA model to forecast monthly average isolation data considering different sky conditions. [PERSON] et al. [33] employed an ARIMA to predict global irradiation. In this work, the model is used in conjunction with MLP networks, and the proposed approach is compared with single models. Machine learning models are not only capable of performing nonlinear mappings in the data but also present some flexibility regarding noisy data and tend to achieve an improved performance since real-world data often composed of nonlinear patterns. The employment of SVM models in irradiation tasks has achieved promising results. [PERSON] and [PERSON] [34] used the SVM models, considering exogenous variables, such as sunshine ratio, maximum and minimum air temperature, relative humidity, and atmospheric water vapor pressure. Moreover, [PERSON] et al. [35] employed an optimization method for several models, such as SVM and MLP, and then performed experimental comparisons. The results indicated that SVM with the proposed optimization achieved the best results. Random forests (RFs) are part of the ensemble class of models, where several regression trees are employed and combined to achieve the final result. This approach is interesting to solar radiation forecasting since the data may present high volatility, and it mitigates the chance of selecting an inappropriate model. [PERSON] et al. performed several comparisons among machine learning models, such as classification and regression trees (CART) and random forests, using data from India, and the RF model obtained the best results. [PERSON] et al. [36] compared several methods using exogenous data and concluded that the RF method achieved the smallest errors. Another algorithm employed in irradiation forecasting tasks is gradient boosting (GB), which is based on a tree-based ensemble learning technique. [PERSON] et al. [37] employed LightGBM to perform multi-step ahead forecasting in irradiation data. Moreover, [PERSON] et al. [38] used an extreme gradient boosting algorithm and SVM to predict daily global solar radiation using temperature and precipitation in humid subtropical climates. The work of [PERSON] et al. [39] was one of the pioneering works that applied neural networks when considering solar irradiance components. In this investigation, the authors predicted infrared, ultraviolet, and global insolation using an MLP trained by the Levenberg-Marquardt algorithm. The database addressed was from Egypt. They also applied the trained network considering a database from Helwan and Aswan. In both cases, the accuracy was up to 90%. MLP was also applied to generate synthetic daily solar radiation series using five exogenous variables as inputs: daily clear sky global radiation, cloud cover, temperature, water vapor, and ozone. They proposed that the model can generate good predictions for locations in which there are no ground measurements.