  Forecasting and decision-making are generally modeled as two sequential steps
with no feedback, following an open-loop approach. In this paper, we present
application-driven learning, a new closed-loop framework in which the processes
of forecasting and decision-making are merged and co-optimized through a
bilevel optimization problem. We present our methodology in a general format
and prove that the solution converges to the best estimator in terms of the
expected cost of the selected application. Then, we propose two solution
methods: an exact method based on the KKT conditions of the second-level
problem and a scalable heuristic approach suitable for decomposition methods.
The proposed methodology is applied to the relevant problem of defining dynamic
reserve requirements and conditional load forecasts, offering an alternative
approach to current ad hoc procedures implemented in industry practices. We
benchmark our methodology with the standard sequential least-squares forecast
and dispatch planning process. We apply the proposed methodology to an
illustrative system and to a wide range of instances, from dozens of buses to
large-scale realistic systems with thousands of buses. Our results show that
the proposed methodology is scalable and yields consistently better performance
than the standard open-loop approach.
