access icon openaccess Exploring wind power prognosis data on Nord Pool: the case of Sweden and Denmark

A good understanding of forecast errors is imperative for greater penetration of wind power, as it can facilitate planning and operation tasks. Oftentimes, public data is used for system studies without questioning or verifying its origin. In this study, the authors propose a methodology to verify public data with the example of wind power prognosis published by Nord Pool. They focus on Swedish data and identify a significant bias that increases over the forecast horizon. In order to explore the origin of this bias, they first compare against Danish forecast and then describe the underlying structure behind the submission processes of this data. Based on the balance settlement structure, they reveal that Swedish ‘wind power prognoses’ on Nord Pool are in fact rather wind production plans than technical forecasts. They conclude with the recommendation for improved communication and transparency with respect to the terminology of public data on Nord Pool. They stress the importance for the research community to check publicly available input data before further use. Furthermore, the root-mean-square error and the spatio-temporal correlation between the errors in the bidding areas at different horizons are presented. Even with this compromised data, a stronger correlation is identified in neighbouring areas.

Inspec keywords: wind power; data analysis; production planning; regression analysis; wind power plants; weather forecasting; mean square error methods; power markets

Other keywords: forecast horizon; system studies; good understanding; wind power prognosis data; technical forecasts; Swedish data; Danish forecast; compromised data; publicly available input data; operation tasks; planning; wind production plans; wind power prognoses; Nord Pool; forecast errors; public data; balance settlement structure

Subjects: Wind power plants; Other topics in statistics; Interpolation and function approximation (numerical analysis); Power system management, operation and economics

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