The smoother the better? A comparison of six post-processing methods to improve short-term offshore wind power forecasts in the Baltic Sea

Christoffer Hallgren,Stefan Ivanell,Heiner Körnich,Ville Vakkari,Erik Sahlée

doi:10.5194/wes-6-1205-2021

Abstract

Abstract. With a rapidly increasing capacity of electricity generation from wind power, the demand for accurate power production forecasts is growing. To date, most wind power installations have been onshore and thus most studies on production forecasts have focused on onshore conditions. However, as offshore wind power is becoming increasingly popular it is also important to assess forecast quality in offshore locations. In this study, forecasts from the high-resolution numerical weather prediction model AROME was used to analyze power production forecast performance for an offshore site in the Baltic Sea. To improve the AROME forecasts, six post-processing methods were investigated and their individual performance analyzed in general as well as for different wind speed ranges, boundary layer stratifications, synoptic situations and in low-level jet conditions. In general, AROME performed well in forecasting the power production, but applying smoothing or using a random forest algorithm increased forecast skill. Smoothing the forecast improved the performance at all wind speeds, all stratifications and for all synoptic weather classes, and the random forest method increased the forecast skill during low-level jets. To achieve the best performance, we recommend selecting which method to use based on the forecasted weather conditions. Combining forecasts from neighboring grid points, combining the recent forecast with the forecast from yesterday or applying linear regression to correct the forecast based on earlier performance were not fruitful methods to increase the overall forecast quality.

Highlights

With a growing concern about a future climate crisis and a continuously increasing demand for electrical power, a greater penetration of renewable energy sources in the power supply system becomes crucial to meet the climate goals (e.g., Sims, 2004; Quaschning, 2019)
An overview of the lidar wind speed at 90 m hub height is presented in Fig. 3a together with the theoretical power production if a SWT-3.6-120 would have been placed at the site and assuming that the power production was following the power curve perfectly (Fig. 3b)
Note that the high values of centered root mean square error (CRMSE) for February 2018 might be due to the small number of data points in this month as there were a lot of missing observations

Summary

Introduction

With a growing concern about a future climate crisis and a continuously increasing demand for electrical power, a greater penetration of renewable energy sources in the power supply system becomes crucial to meet the climate goals (e.g., Sims, 2004; Quaschning, 2019). As wind power production is highly dependent on the weather, a deep understanding of the climate for a site is needed when assessing the optimal location for a new wind farm, taking both the average and extreme conditions into account. Once the farm is in operational use, the meteorological focus shifts from site climatology to weather forecasting, to be able to predict the instantaneous power production. Accurate forecasts, especially for the short perspective (minutes to hours) and for longer timescales (weeks to seasons), are requested by the grid operators, power production companies and traders on the electricity market to balance the power in Published by Copernicus Publications on behalf of the European Academy of Wind Energy e.V. C. Hallgren et al.: A comparison of post-processing methods for offshore wind power forecasts the grid, to plan ahead and to maximize the revenue (e.g., Foley et al, 2012; Heppelmann et al, 2017; Lledó et al, 2019)

Results

Discussion

Conclusion