"energy time series"

Adaptively coping with concept drifts in energy time series forecasting using profiles

Accurate electrical load forecasts are necessary to stabilize the electricity grid, e.g., by optimally operating energy storage systems or using demand-side management. However, an implicit assumption of most load forecasting methods is that future …

Evaluating ensemble post-processing for wind power forecasts

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 predictions aim to capture the uncertainty in the weather system, they …

Forecasting energy time series with profile neural networks

Forecasting the energy demand is essential for network operators to balance the grid, in particular with the increasing share of renewable energy sources. Neural networks, especially deep neural networks, have shown promising results in recent …