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| dc.contributor | Barcelona Supercomputing Center |
|---|---|
| dc.contributor.author | Soret, Albert |
| dc.contributor.author | Torralba, Verónica |
| dc.contributor.author | Cortesi, Nicola |
| dc.contributor.author | Christel, I. |
| dc.contributor.author | Palma, Ll. |
| dc.contributor.author | Manrique-Suñén, A. |
| dc.contributor.author | Lledó, Llorenç |
| dc.contributor.author | González-Reviriego, Nube |
| dc.contributor.author | Doblas-Reyes, Francisco J. |
| dc.date | 2019-05 |
| dc.identifier.citation | Soret, A. [et al.]. Sub-seasonal to seasonal climate predictions for wind energy forecasting. "Journal of Physics: Conference Series", Maig 2019, vol. 1222. |
| dc.identifier.citation | 1742-6588 |
| dc.identifier.citation | 10.1088/1742-6596/1222/1/012009 |
| dc.identifier.uri | http://hdl.handle.net/2117/133649 |
| dc.description.abstract | Both renewable energy supply and electricity demand are strongly influenced by meteorological conditions and their evolution over time in terms of climate variability and climate change. However, knowledge of power output and demand forecasting beyond a few days remains poor. Current methodologies assume that long-term resource availability is constant, ignoring the fact that future wind resources could be significantly different from the past wind energy conditions. Such uncertainties create risks that affect investment in wind energy projects at the operational stage where energy yields affect cash flow and the balance of the grid. Here we assess whether sub-seasonal to seasonal climate predictions (S2S) can skilfully predict wind speed in Europe. To illustrate S2S potential applications, two periods with an unusual climate behaviour affecting the energy market will be presented. We find that wind speed forecasted using S2S exhibits predictability some weeks and months in advance in important regions for the energy sector such as the North Sea. If S2S are incorporated into planning activities for energy traders, energy producers, plant operators, plant investors, they could help improve management climate variability related risks. |
| dc.description.abstract | We thank the S2S4E (GA776787), NEWA (PCIN-2014-012-C07-07), ERA4CS-INDECIS (GA690462) and ERA4CS-MEDSCOPE (GA690462) projects funding for allowing us to carry out this research. We acknowledge use of the s2dverification (http://cran.r-project.org/web/packages/s2dverification) and Specs-Verification (http://cran.r-project.org/web/packages/SpecsVerification)R-language-based software packages.We also acknowledge the ECMWF for the provision of the ECMWF SEAS5 and the Monthly Prediction Systemsand the ERA-Interim reanalysis datasets. |
| dc.description.abstract | Peer Reviewed |
| dc.language.iso | eng |
| dc.publisher | IOP Publishing |
| dc.relation | https://iopscience.iop.org/article/10.1088/1742-6596/1222/1/012009 |
| dc.relation | info:eu-repo/grantAgreement/EC/H2020/776787/EU/Sub-seasonal to Seasonal climate forecasting for Energy/S2S4E |
| dc.relation | info:eu-repo/grantAgreement/EC/H2020/690462/EU/European Research Area for Climate Services/ERA4CS |
| dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Spain |
| dc.rights | info:eu-repo/semantics/openAccess |
| dc.rights | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
| dc.subject | Àrees temàtiques de la UPC::Energies |
| dc.subject | Wind energy |
| dc.subject | Seasonal climate predictions |
| dc.subject | Wind energy |
| dc.subject | Energy forecasting |
| dc.subject | Energia eòlica |
| dc.title | Sub-seasonal to seasonal climate predictions for wind energy forecasting |
| dc.type | info:eu-repo/semantics/publishedVersion |
| dc.type | info:eu-repo/semantics/article |
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