Forecast uncertainty

class rtctools.optimization.control_tree_mixin.ControlTreeMixin(*args, **kwargs)

Bases: OptimizationProblem

Adds a stochastic control tree to your optimization problem.

control_tree_options() → dict[str, list[str] | list[float] | int]

Returns a dictionary of options controlling the creation of a k-ary stochastic tree.

Option	Type	Default value
forecast_variables	list of strings	All constant inputs
branching_times	list of floats	self.times()
k	int	2

A k-ary tree is generated, branching at every interior branching time. Ensemble members are clustered to paths through the tree based on average distance over all forecast variables.

Returns:

A dictionary of control tree generation options.

class rtctools.optimization.planning_mixin.PlanningMixin(**kwargs)

Bases: OptimizationProblem

Uses default discretization logic for planning variables, but uses dedicated per-ensemble-member decision variables for other, non-planning control variables.