In this paper, we propose a day-ahead scheduling method under uncertain renewable energy generation based on a machine learning approach. An aggregator, which has renewable energy generation devices, needs to schedule the energy production and consumption (prosumption) in a situation where the renewable power generation amount is not exactly predicted in day-ahead scheduling. If imbalance, defined as the difference between a day-ahead schedule and an actual prosumption profile, occurs, the aggregator is required to pay imbalance penalty costs. As a scheduling method to avoid paying imbalance penalty costs, we propose a scheduling model by machine learning based on the results of past transactions. In particular, we show that the problem of finding the parameters of the scheduling model is reduced to a convex program by introducing a parametric black-box scheduling model which is linear with respect to the parameters. Furthermore, we also show that the problem of finding the parameters is reduced to a linear program if the cost functions are convex and piecewise affine. In addition, it is also shown that the problem of finding the parameters is reduced to a quadratic program if the L2 regularization term is introduced. Finally, we show the efficiency of the proposed method through a numerical example.
