Conceptos Básicos
Hierarchical structures improve wind power forecast accuracy.
Resumen
The content discusses the importance of renewable energy generation, particularly wind energy forecasting. It explores the challenges in forecasting wind energy due to its variability and uncertainty. The use of hierarchical forecasting through reconciliation is highlighted as a method to enhance forecast accuracy for short-term periods. Various models and techniques are compared to determine the best approach for high-frequency wind data forecasting.
Abstract:
- Renewable energy crucial for decarbonization.
- Hierarchical forecasting improves wind energy forecasts.
- Cross-temporal reconciliation enhances forecast accuracy.
Introduction:
- Historical measurements used for time series forecasting.
- Hierarchies improve forecast accuracies across locations.
- Challenges in wind energy forecasting due to variability.
Background:
- Traditional focus on individual time series models.
- Different methods like physical, statistical, and deep learning used for wind energy forecasts.
- Importance of very short-term forecasts for decision-making.
Hierarchical Forecasting Methods:
- Reconciliation methods include bottom-up, top-down, middle-out, and combination approaches.
- Trace minimization algorithm improves forecast accuracies.
Data and Experimental Setup:
- Two datasets used with 3 levels in the hierarchy.
- Features extracted for linear and machine learning regression models.
- Base forecasts performed using naive, linear regression, and gradient boosting methods.
Estadísticas
Recent advances in hierarchical forecasting have demonstrated a significant increase in the quality of wind energy forecasts for short-term periods.
Hierarchical forecasting ensures coherency of forecasts and has been empirically shown to improve wind energy forecasts.
Recent studies leverage cross-sectional hierarchical time series to show that forecast accuracy can be improved by reconciliation methods.