ABSTRACT Accurate, time‐resolved installed capacity data are crucial for forecasting and analysing wind‐power production. The time series of installed capacity is often only approximately known in regions with rapid wind power development. Public wind power databases may only be updated yearly and installation dates may not be differentiated within wind farms. Errors in the magnitude or timing of added capacity can significantly impact the analysis of a production time series. The cumulative maximum of the power production can be used as a robust approach to estimate installed capacity but requires monotonically increasing capacity and relies on frequent high wind events. Limited information exists in the literature on advanced techniques to determine the installed capacity time series from measured data for a region. Here, we present a method to find the most probable time series of installed capacity for some measured wind power production, using a simulation of capacity factor time series and a quadratic optimization. Results show a 27.2% reduction in normalized mean absolute error when quantifying the installed capacity after a new wind farm is connected. It is also shown that the day‐ahead forecast mean absolute error and root mean square error are reduced by 2.0% and 2.3%, respectively, when using the method to normalize production data before training of a forecasting model, compared to using the cumulative maximum for normalization. An advantage of the proposed method is that it can be used without the assumption of monotonically increasing installed capacity. It is also computationally cheap and thus suitable for usage in automated forecasting pipelines.
Viotti et al. (Thu,) studied this question.