Systematic Comparison of Feature Selection Methods for Solar Energy Forecasting

Motaki, Saloua El; El-Fengour, Abdelhak

doi:10.1201/9781003172772-12

Cited by 3 publications

(2 citation statements)

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“…We focus on the Boruta algorithm as it is a standard variable selection technique applied by many practitioners. This method exhibits superior performance (as a tree based wrapper method) when used on a stepwise basis, facilitating the selection of features that can improve the accuracy of the regression (El Motaki & El Fengour, 2021; Sanchez‐Pinto et al, 2018).…”

Section: A Horse Race Of Predicting Sovereign Spreadsmentioning

confidence: 99%

Forecasting sovereign risk in the Euro area via machine learning

Belly

Boeckelmann

Graciano

et al. 2023

Journal of Forecasting

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We test the usefulness of machine learning (ML) for the valuation and pricing of sovereign risk in the Euro area along two important dimensions: i) its predictive accuracy compared with traditional econometric methods, and ii) its assessment of the main economic factors underlying market perceptions of sovereign risk.We find that ML techniques can capture the dynamics inherent in the market valuation of country risk far more efficiently than traditional econometric models, both in the cross-section and in the time series. Moreover, we show that public sentiment about financial news, redenomination fears and the degree of hawkishness/dovishness expressed in the ECB president's speeches are major contributors to sovereign bond spreads. We also confirm that macroeconomic and global financial factors affect sovereign risk assessment and the corresponding formation of sovereign spreads.

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Section: A Horse Race Of Predicting Sovereign Spreadsmentioning

confidence: 99%

Forecasting sovereign risk in the Euro area via machine learning

Belly

Boeckelmann

Graciano

et al. 2023

Journal of Forecasting

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“…This type of model has the disadvantage of overfitting, which can be corrected thanks to regularization techniques. For example, the authors of [6] mention that for large volumes of data in which there are redundant data with noise or with outliers, the selection of features is a good option for improving the regression or classification of the results. In the same way, for the prediction of daily solar energy [7], the authors propose a feature selection approach based on two factors: the selection and grouping of features based on relevance and redundancy and a hybrid classification and regression prediction algorithm.…”

Section: Introductionmentioning

confidence: 99%

Multiparameter Regression of a Photovoltaic System by Applying Hybrid Methods with Variable Selection and Stacking Ensembles under Extreme Conditions of Altitudes Higher than 3800 Meters above Sea Level

Cruz,

Romero,

Vera

et al. 2023

Energies

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The production of solar energy at altitudes higher than 3800 m above sea level is not constant because the relevant factors are highly varied and complex due to extreme solar radiation, climatic variations, and hostile environments. Therefore, it is necessary to create efficient prediction models to forecast solar production even before implementing photovoltaic systems. In this study, stacking techniques using ElasticNet and XGBoost were applied in order to develop regression models that could collect a maximum number of features, using the LASSO, Ridge, ElasticNet, and Bayesian models as a base. A sequential feature selector (SFS) was used to reduce the computational cost and optimize the algorithm. The models were implemented with data from a string photovoltaic (PV) system in Puno, Peru, during April and August 2021, using 15 atmospheric and photovoltaic system variables in accordance with the European standard IEC 61724-20170. The results indicate that ElasticNet reduced the MAE by 30.15% compared to the base model, and that the XGBoost error was reduced by 30.16% using hyperparameter optimization through modified random forest research. It is concluded that the proposed models reduce the error of the prediction system, especially the stacking model using XGBoost with hyperparameter optimization.

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