With the increasing penetration of renewable energy in smart grids and the increasing
building electrical load, their accurate forecasting is essential for system design, control and …

Around the world wind energy is starting to become a major energy provider in electricity
markets, as well as participating in ancillary services markets to help maintain grid stability …

It is widely accepted today that probabilistic forecasts of wind power production constitute
valuable information that can allow both wind power producers and power system operators …

This study proposed a model for deterministic and probabilistic wind power generation
forecasting and its corresponding procedures. The main contents include numerical weather …

The crch package provides functions for maximum likelihood estimation of censored or
truncated regression models with conditional heteroscedasticity along with suitable standard …

With the recent development of easy-to-use tools for Bayesian analysis, psychologists have
started to embrace Bayesian hierarchical modeling. Bayesian hierarchical models provide …

Forecast performance of data-driven models depends on the local weather and climate
regime, which makes model selection a tedious task for forecast practitioners. Ensemble …

Abstracts Probabilistic wind power forecasting not only produces the expectation of wind
power output, but also gives quantitative information on the associated uncertainty, which is …

Capturing the uncertainty in probabilistic wind power forecasts is challenging, especially
when uncertain input variables, such as the weather, play a role. Since ensemble weather …

Accurate short‐term power forecasts are crucial for the reliable and efficient integration of
wind energy in power systems and electricity markets. Typically, forecasts for hours to days …