  While the space of renewable energy forecasting has received significant
attention in the last decade, literature has primarily focused on machine
learning models that train on only one objective at a time. A host of
classification (and regression) tasks in energy markets lead to highly
imbalanced training data. Say, to balance reserves, it is natural for market
regulators to have a choice to be more/less averse to false negatives (can lead
to poor operating efficiency and costs) than to false positives (can lead to
market shortfall). Besides accuracy, other metrics like algorithmic bias, RMBE
(in regression problems), inferencing time, and model sparsity are also very
crucial. This paper is amongst the firsts in the field of renewable energy
forecasting that attempts to present a Pareto frontier of solutions
(tradeoffs), that answers the question on handling multiple objectives by means
of using the XGBoost model (Gradient Boosted Trees). Our proposed algorithm
relies on using a sequence of weighted (uniform meshes) single objective model
training routines. Real world data examples from the Amherst (Massachusetts,
United States) solar energy prediction panels with both triobjective (focus on
accuracy) and biojective (focus on fairness/bias) classification instances are
considered. Numerical experiments appear promising and clear advantages over
single objective methods are seen by observing the spread and variety of
solutions (model configurations).
