This paper provides a real life application of the recently published Anticipative Hybrid Extreme RotationForest (AHERF), which is an heterogeneous ensemble classifier that anticipates the correct fraction ofinstances from ...

The medium term hydropower scheduling (MTHS) problem involves an attempt to determine, for each time stage of the planning period, the amount of generation at each hydro plant which will maximize the expected future benefits ...

The medium term hydropower scheduling (MTHS) problem involves an attempt to determine, for each time stage of the planning period, the amount of generation at each hydro plant which will maximize the expected future benefits ...

Ensembling machine-learned models has shown to be a useful technique for improving the effectiveness of tasks such as classification, ad-hoc retrieval, and recommendation. Stacking, for instance, learns to weight and combine ...

Modelling forecast uncertainty is a difficult task in any forecasting problem. In weather forecasting a possible solution is the use of forecast ensembles, which are obtained from multiple runs of numerical weather prediction ...

Healthcare buildings are characterized by complex energy systems and high energy usage, therefore serving as the key areas for achieving energy conservation goals in the building sector. An accurate load prediction of ...

Applying the chemical shift prediction programs SHIFTX and SHIFTS to a data base of protein structures with known chemical shifts we show that the averaged chemical shifts predicted from the structural ensembles explain ...