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Algorithms that involve both forecasting and optimization are at the core ofsolutions to many difficult real-world problems, such as in supply chains(inventory optimization), traffic, and in the transition towards carbon-freeenergy generation in battery/load/production scheduling in sustainable energysystems. Typically, in these scenarios we want to solve an optimization problemthat depends on unknown future values, which therefore need to be forecast. Asboth forecasting and optimization are difficult problems in their own right,relatively few research has been done in this area. This paper presents thefindings of the ``IEEE-CIS Technical Challenge on Predict+Optimize forRenewable Energy Scheduling," held in 2021. We present a comparison andevaluation of the seven highest-ranked solutions in the competition, to provideresearchers with a benchmark problem and to establish the state of the art forthis benchmark, with the aim to foster and facilitate research in this area.The competition used data from the Monash Microgrid, as well as weather dataand energy market data. It then focused on two main challenges: forecastingrenewable energy production and demand, and obtaining an optimal schedule forthe activities (lectures) and on-site batteries that lead to the lowest cost ofenergy. The most accurate forecasts were obtained by gradient-boosted tree andrandom forest models, and optimization was mostly performed using mixed integerlinear and quadratic programming. The winning method predicted differentscenarios and optimized over all scenarios jointly using a sample averageapproximation method.

Comparison and Evaluation of Methods for a Predict+Optimize Problem in Renewable Energy