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Life Sciences, Vienna" } ], "person_or_org": { "family_name": "Kloeckl", "given_name": "Claude", "name": "Kloeckl, Claude", "type": "personal" } }, { "affiliations": [ { "name": "University of Natural Resources and Life Sciences, Vienna" } ], "person_or_org": { "family_name": "Regner", "given_name": "Peter", "name": "Regner, Peter", "type": "personal" } }, { "affiliations": [ { "name": "University of Natural Resources and Life Sciences, Vienna" } ], "person_or_org": { "family_name": "Baumgartner", "given_name": "Johann", "name": "Baumgartner, Johann", "type": "personal" } }, { "affiliations": [ { "name": "University of Natural Resources and Life Sciences, Vienna" } ], "person_or_org": { "family_name": "Schmidt", "given_name": "Johannes", "identifiers": [ { "identifier": "0000-0003-3533-349X", "scheme": "orcid" } ], "name": "Schmidt, Johannes", "type": "personal" } } ], "description": "
Simulated and bias corrected wind power generation time series data sets for Brazil, its North-East and South, seven states and seven wind parks.
\n\nThe data sources, generation and validation of the datasets are described in the article "Assessing the Global Wind Atlas and local measurements for bias correction of wind power generation simulated from MERRA-2 in Brazil", preprint available on arXiv: arxiv.org/abs/1904.13083, final version DOI: 10.1016/j.energy.2019.116212
\n\nCode for generating the datasets is available at github.com/KatharinaGruber/BrazilWindpower_biascorr
\n\n\n\n
The files "comp_*" contain comparisons of simulated and observed wind power generation time series with daily resolution for all regions.
\n\n"comp_noc.RData" is for comparison of interpolation methods and contains time series generated with Nearest Neighbour interpolation (NN), Bilinear Interpolation (BLI) and Inverse Distance Weighting (IDW).
\n\n"comp_wmsa.RData" is for comparison of wind speed mean approximation methods and contains time series generated with Nearest Neighbour interpolation (NN - no correction applied), mean approximation with measured data (IN) and mean approximation with the Global Wind Atlas (GWA).
\n\n"comp_wsc.RData" is for comparison of spatiotemporal wind speed correction methods and contains time series generated with mean approximation with the Global Wind Atlas (wmsa) and combined mean approximation with the Global Wind Atlas and hourly and monthly mean approximation with measured data (wschm).
\n\n\n\n
The files "statpowlist_*" contain hourly simulated wind power generation time series for three interpolation methods (NN, BLI, IDW), two mean approximation methods (wsmaIN - measured data (INMET), wsmaWA - Global Wind Atlas) as well as for spatiotemporal (hourly and monthly) wind speed bias correction (wschm) for each wind park available in The Wind Power dataset.
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