Short-term wind power prediction based on Markov chain and numerical weather prediction models: A case study of Fakken wind farm

Abstract

Rising energy demands and a growing focus on sustainable development have made electricity production from wind energy an attractive alternative to fossil fuels. However the natural variability of wind makes it challenging to implement wind energy into the electrical grid. Accurate and reliable wind power predictions are seen as a key element for an increased penetration of wind energy. This study presents a set of statistical power prediction models using the concept of Markov chains, based on various input parameters, such as wind speed, direction and power output. The models have been trained and tested using numerical weather predictions and historical data obtained from a meteorological station and wind turbine at Fakken wind farm in the time period 2. May 2013 - 31. March 2014. Several of the models were found to have lower NRMSE than the currently used persistent model (19.08 %), with the best performing model having a NRMSE of 16.84 %. This 2.25 % lower NRMSE corresponds to approximately 3 100 000 kWh of the anually electricity production from Fakken wind farm. A statistical analysis of Fakken wind farm showed the majority of winds occurring from the straits between Arnøya and Lenangsøyra to the southeast and between Reinøya and Lenangsøyra to the south. Winds were also commonly seen from southwest and to the northwest, while eastern and northeastern winds were rarely observed. Westerly winds were found to be much more tubulent than other directions, with a generally lower power output observed. This is most likely due to the occurerence of mountain waves for winds crossing the mountain range to the west.