Day-Ahead Wind Power Temporal Distribution Forecasting With High Resolution

Day-ahead wind power forecast (DWPF) is an indispensable component for accommodating wind energy in power systems. Traditional DWPF models focus on providing information, merely, around the average wind power for day-ahead periods. This article goes beyond the traditional approach and formulates a DWPF problem that renders information on intra-period wind variability with high resolution (second-wise), by predicting the intra-period temporal distribution of wind power for day-ahead horizons. This additional information could be of particular interest to decision-makers when high-resolution wind variability is crucial for optimal decisions, e.g., wind power reserve scheduling. First, a parametric and several entropy-based losses are tailored to this problem to acquire candidate solutions using classical approaches. Then, a differentiable loss, based on the Wasserstein distance (WD), is dedicatedly developed to overcome the inherent limitations of the tailored losses. The superiority of the proposed WD-based loss is, first, verified by comparing its predictions with those of classical losses, using on-site wind measurements. Furthermore, the superiority of the predictions obtained by the proposed WD loss, in the context of day-ahead wind power scheduling, is further verified using operational electricity market data, with profit erosion as an error measure.