Forecast uncertainty
- class rtctools.optimization.control_tree_mixin.ControlTreeMixin(*args, **kwargs)[source]
Bases:
OptimizationProblem
Adds a stochastic control tree to your optimization problem.
- control_tree_options() Dict[str, List[str] | List[float] | int] [source]
Returns a dictionary of options controlling the creation of a k-ary stochastic tree.
Option
Type
Default value
forecast_variables
list
of stringsAll constant inputs
branching_times
list
of floatsself.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)[source]
Bases:
OptimizationProblem
Uses default discretization logic for planning variables, but uses dedicated per-ensemble-member decision variables for other, non-planning control variables.