A Derivative Tracking Model for Wind Power Forecast Error

Abstract

Reliable wind power generation forecasting is crucial for applications such as the allocation of energy reserves, optimization for electricity price, and operation scheduling of conventional power plants. We propose a data-driven model based on parametric Stochastic Differential Equations (SDEs) to capture the real asymmetric dynamics of wind power forecast errors. Our SDE framework features time-derivative tracking of the forecast, time-varying mean-reversion parameter, and an improved state-dependent diffusion term. The methodology we developed allows the simulation of future wind power production paths and to obtain sharp empirical confidence bands. All the procedures are agnostic of the forecasting technology, and they enable comparisons between different forecast providers. We apply the model to historical Uruguayan wind power production data and forecasts between April and December 2019.