  Better forecasts may not lead to better decision-making. To address this
challenge, decision-oriented learning (DOL) has been proposed as a new branch
of machine learning that replaces traditional statistical loss with a decision
loss to form an end-to-end model. Applications of DOL in power systems have
been developed in recent years. For renewable-rich power systems, uncertainties
propagate through sequential tasks, where traditional statistical-based
approaches focus on minimizing statistical errors at intermediate stages but
may fail to provide optimal decisions at the final stage. This paper first
elaborates on the mismatch between more accurate forecasts and more optimal
decisions in the power system caused by statistical-based learning (SBL) and
explains how DOL resolves this problem. Secondly, this paper extensively
reviews DOL techniques and their applications in power systems while
highlighting their pros and cons in relation to SBL. Finally, this paper
identifies the challenges to adopt DOL in the energy sector and presents future
research directions.
