1 citations · 2 across the 3 of their papers we have counts for
5 papers
Explaining predictive models using Shapley values and non-parametric vine copulas
Kjersti Aas, Thomas Nagler, Martin Jullum +1
The original development of Shapley values for prediction explanation relied on the assumption that the features being described were independent. If the features in reality are de…
Probabilistic Forecasting of Temporal Trajectories of Regional Power Production - Part 2: Photovoltaic Solar
Thordis Thorarinsdottir, Anders Løland, Alex Lenkoski
We propose a fully probabilistic prediction model for spatially aggregated solar photovoltaic (PV) power production at an hourly time scale with lead times up to several days using…
Probabilistic Forecasting of Temporal Trajectories of Regional Power Production - Part 1: Wind
Thordis Thorarinsdottir, Anders Løland, Alex Lenkoski
Renewable energy sources provide a constantly increasing contribution to the total energy production worldwide. However, the power generation from these sources is highly variable…
Explaining individual predictions when features are dependent: More accurate approximations to Shapley values
Kjersti Aas, Martin Jullum, Anders Løland
Explaining complex or seemingly simple machine learning models is an important practical problem. We want to explain individual predictions from a complex machine learning model by…
Using published bid/ask curves to error dress spot electricity price forecasts
Gunnhildur H. Steinbakk, Alex Lenkoski, Ragnar Bang Huseby +2
Accurate forecasts of electricity spot prices are essential to the daily operational and planning decisions made by power producers and distributors. Typically, point forecasts of…