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

arXiv:2102.13273v5 Announce Type: replace-cross
Abstract: 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 …

abstract application arxiv cs.lg cs.sy decision demand demand forecasting dynamic eess.sy feedback forecasting framework loop making math.oc optimization paper prediction processes stat.me stat.ml type