Regression Tree Ensembles For Wind Energy And Solar Radiation Prediction
Neurocomputing, 2019
Abstract
This study explores the use of ensemble models for wind energy and solar radiation prediction, specifically Random Forest Regression (RFR), Gradient Boosted Regression (GBR), and Extreme Gradient Boosting (XGB).
The investigation shows that these ensemble methods outperform Support Vector Regression (SVR) in predicting energy for individual wind farms. Moreover, GBR and XGB show promise in forecasting wind energy on a larger geographical scale. For solar radiation prediction, both gradient-based ensemble methods showed improvements when compared to SVR.
The investigation shows that these ensemble methods outperform Support Vector Regression (SVR) in predicting energy for individual wind farms. Moreover, GBR and XGB show promise in forecasting wind energy on a larger geographical scale. For solar radiation prediction, both gradient-based ensemble methods showed improvements when compared to SVR.
The study highlights how ensemble models enhance energy forecasts, promoting better energy management and investment strategies in the renewable sector.
Where does it apply?
Utility companies can utilize them for improving energy management and grid operations through accurate wind energy and solar radiation predictions.
Renewable energy developers can use these models to predict the efficiency of potential sites for wind and solar farms. Policy makers can leverage these models for informed policymaking regarding renewable energy implementation. Research institutions can use the methods for further research and development in the renewable energy forecast field.
Lastly, investors can use these accurate predictions to guide investment strategies in renewable energy infrastructure.
Renewable energy developers can use these models to predict the efficiency of potential sites for wind and solar farms. Policy makers can leverage these models for informed policymaking regarding renewable energy implementation. Research institutions can use the methods for further research and development in the renewable energy forecast field.
Lastly, investors can use these accurate predictions to guide investment strategies in renewable energy infrastructure.
Why does it matter?
This research is relevant as it demonstrates how ensemble models can enhance predictive accuracy in global and local wind energy prediction, as well as solar radiation. Accurate energy predictions can lead to better planning and use of renewable resources, which is essential for efficient energy management.
The findings can help utility companies, renewable energy developers, and policy makers make more informed decisions. Moreover, the ability to forecast wind energy on larger geographical scales could be a significant step forward in the broader implementation of renewable energy sources.
Better predictions could also aid investment strategies in the renewable energy sector. It ultimately contributes to the ongoing efforts towards energy sustainability and combating climate change.
The findings can help utility companies, renewable energy developers, and policy makers make more informed decisions. Moreover, the ability to forecast wind energy on larger geographical scales could be a significant step forward in the broader implementation of renewable energy sources.
Better predictions could also aid investment strategies in the renewable energy sector. It ultimately contributes to the ongoing efforts towards energy sustainability and combating climate change.
Regression Tree Ensembles For Wind Energy And Solar Radiation Prediction
Neurocomputing, 2019
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