Short term wind energy resource prediction using WRF model for a location in western part of Turkey

Abstract

Wind energy is a rapidly growing industry in Turkey. Wind power potential studies revealed that the most promising region for electricity generation is the western part of Turkey. Wind speed forecasting is necessary for power systems because of the intermittent nature of wind. Thus, accurate forecasting of wind power is recognized as a major contribution to reliable wind power integration. This paper assesses the performance of the weather research forecasting (WRF) model for wind speed and wind direction predictions up to 72 h ahead. The wind speeds and wind directions are evaluated based on the mean absolute error (MAE). Evaluations were also performed seasonally. Moreover, in order to improve the WRF simulations, a multi-input–single output artificial neural network (ANN) approach is applied to both wind speeds of the WRF model and wind power estimates, which are estimated from the wind speeds of the WRF model by using a power curve for the Soma wind power plant. Traditional error metrics were used for validations using wind tower mast data installed nearby the wind farm. The results from up to 72 h forecast horizon show that the WRF model slightly overpredicts the wind speeds. Wind speed predictions by the WRF model are found highly depending on the season, location, and wind direction. The model is also able to reproduce wind directions except for low wind speeds. Large MAEs are found for the winds less than 5 m/s. The performance of the WRF model for wind power prediction decreases with the increasing runtime. Root mean square error and normalized root mean square error (nRMSE) in wind powers range in between 123–261 kW and 13%–32% without performing the ANN approach, respectively. The improvement of the ANN depends on the forecast horizon, season, and location of turbine groups, as well as its application on either the wind speed outputs of the WRF model or wind power estimations. The ANN significantly improves the WRF at large forecast horizons for wind power estimations, for which it gives better results in the summer and reaches 29% improvement for summer on average for nRMSE. On the other hand, ANN adjusts the wind speed outputs of the model better than that of wind power estimations. For instance, the nRMSE is approximately 13% for 24 h winter wind speed simulations of the WRF for the turbine groups G1 and G4, after ANN adjustment. The ANN improves the results better for turbine group 1, because of less complexity of this group in the direction of prevailing wind. The evaluation of the ANN suggests that the approach can be used for improving the performance of the wind power forecast for this power plant.