EMHIRES is the first publically available European wind power generation dataset derived from meteorological sources that is available up to NUTS-2 level. It was generated applying an innovative methodology capturing local geographical information to generate meteorologically derived wind power time series at high temporal and spatial resolution. This allows for a better understanding of the wind resource at the precise location of wind farms.
Additional or ongoing publications:
- Gonzalez-Aparicio, I. and Zucker A (2015) Impact of wind power uncertainty forecasting on the market integration of wind energy in Spain. Applied Energy Journal, Vol 159, 334-349. https://doi.org/10.1016/j.apenergy.2015.08.104
- Monforti, F; Gonzalez-Aparicio I (2016) Building a robust and complete database of wind farms in Europe for continental wind power simulations. Data consistency and sensitivity analysis. Energies [Submitted]
- Gonzalez-Aparicio, I; Monforti, I; Zucker, A; Careri, F; Huld, T; Volker, P; Badger, J (2016) Impact of wind speed spatial resolution on power generation hourly time series at different aggregation levels [Submitted]
Andreas Zucker
; Fabio Monforti Ferrario; Francesco Careri; Iratxe Gonzalez Aparicio; Jake Badger; Thomas Huld
Gonzalez Aparicio, Iratxe; Zucker, Andreas; Careri, Francesco; Monforti Ferrario, Fabio; Huld, Thomas; Badger, Jake (2026): Wind hourly generation time series at country, NUTS 1, NUTS 2 level and bidding zones. European Commission, Joint Research Centre [Dataset] doi: 10.2905/JRC.ZVJAVHG PID: http://data.europa.eu/89h/jrc-emhires-wind-generation-time-series
hourly time seriespower generationSETISwind energy
The dataset releases four different files about the wind power generation hourly time series during 30 years (1986-2015), accounting for the existing wind fleet at the end of 2015 for country, NUTS 1 and 2 and bidding zone
EMHIRES is the first publically available European wind power generation dataset derived from meteorological sources that is available on NUTS-2 level. It was generated applying an innovative methodology capturing local geographical information to generate meteorologically derived wind power time series at high temporal and spatial resolution. This allows for a better understanding of the wind resource at the precise location of wind farms.
The ongoing growth of RES-E requires power system modellers to adapt both methodologies and datasets, in particular time series for electricity generation from wind and PV. Meteorological models are increasingly used for this purpose. This report provides on overview on the methodologies available and the approaches pursued by recent RES-E integration studies. Based on this review, recommendations for best practice are identified.
| From date | To date |
|---|---|
| 1986-01-01 | 2015-12-31 |
