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Forecasting and decision-making are generally modeled as two sequential stepswith no feedback, following an open-loop approach. In this paper, we presentapplication-driven learning, a new closed-loop framework in which the processesof forecasting and decision-making are merged and co-optimized through abilevel optimization problem. We present our methodology in a general formatand prove that the solution converges to the best estimator in terms of theexpected cost of the selected application. Then, we propose two solutionmethods: an exact method based on the KKT conditions of the second-levelproblem and a scalable heuristic approach suitable for decomposition methods.The proposed methodology is applied to the relevant problem of defining dynamicreserve requirements and conditional load forecasts, offering an alternativeapproach to current \emph{ad hoc} procedures implemented in industry practices.We benchmark our methodology with the standard sequential least-squaresforecast and dispatch planning process. We apply the proposed methodology to anillustrative system and to a wide range of instances, from dozens of buses tolarge-scale realistic systems with thousands of buses. Our results show thatthe proposed methodology is scalable and yields consistently better performancethan the standard open-loop approach.

Application-Driven Learning: A Closed-Loop Prediction and Optimization Approach Applied to Dynamic Reserves and Demand Forecasting