%0 Journal Article %1 lled2019seasonal %A Lledó, Ll. %A Torralba, V. %A Soret, A. %A Ramon, J. %A Doblas-Reyes, F.J. %D 2019 %J Renewable Energy %K energy renewables seasonal skill turbines wind windfarm %R https://doi.org/10.1016/j.renene.2019.04.135 %T Seasonal forecasts of wind power generation %U http://www.sciencedirect.com/science/article/pii/S0960148119306196 %X The energy sector is highly dependent on climate variability for electricity generation, maintenance activities and demand. In recent years, a few climate services have appeared that provide tailored information for the energy sector. In particular, seasonal climate predictions of wind speed have proven useful to the wind power industry. However, most of the service users are ultimately interested in forecasts of electricity generation instead of wind. Although power generation depends on many factors other than wind conditions, the capacity factor is a suitable indicator to quantify the impact of wind variability on production. In this paper a methodology to produce seasonal predictions of capacity factor for a range of turbine classes is proposed for the first time. The strengths and weaknesses of the method are discussed and the forecast quality is evaluated for an application example over Europe.

@article{lled2019seasonal, abstract = {The energy sector is highly dependent on climate variability for electricity generation, maintenance activities and demand. In recent years, a few climate services have appeared that provide tailored information for the energy sector. In particular, seasonal climate predictions of wind speed have proven useful to the wind power industry. However, most of the service users are ultimately interested in forecasts of electricity generation instead of wind. Although power generation depends on many factors other than wind conditions, the capacity factor is a suitable indicator to quantify the impact of wind variability on production. In this paper a methodology to produce seasonal predictions of capacity factor for a range of turbine classes is proposed for the first time. The strengths and weaknesses of the method are discussed and the forecast quality is evaluated for an application example over Europe.}, added-at = {2019-05-09T16:21:47.000+0200}, author = {Lledó, Ll. and Torralba, V. and Soret, A. and Ramon, J. and Doblas-Reyes, F.J.}, biburl = {https://www.bibsonomy.org/bibtex/2c5ae283b68068e838836aaf0ec3a09fb/pbett}, description = {Seasonal forecasts of wind power generation - ScienceDirect}, doi = {https://doi.org/10.1016/j.renene.2019.04.135}, interhash = {32cf06a7d6069e01484552086f7588c2}, intrahash = {c5ae283b68068e838836aaf0ec3a09fb}, issn = {0960-1481}, journal = {Renewable Energy}, keywords = {energy renewables seasonal skill turbines wind windfarm}, timestamp = {2019-05-09T16:21:47.000+0200}, title = {Seasonal forecasts of wind power generation}, url = {http://www.sciencedirect.com/science/article/pii/S0960148119306196}, year = 2019 }