Suchergebnisse

This study evaluates the performance of statistical models applied to the output of numerical models for short-term (1–24 h) hourly wind forecasts at three locations in the Basque Country. The target variables are horizontal wind components and the maximum wind gust at 3 h intervals. Statistical approaches such as persistence, analogues, linear regression, and random forest (RF) are used. The verification statistics used are coefficient of determination (R2) and root mean square error (RMSE). Statistical models use three inputs: (1) Local wind observations; (2) extended EOFs (empirical orthogonal functions) derived from past local observations and ERA-Interim variables in a previous 24-h period covering a domain around the area of study; and (3) wind forecasts provided by ERA-Interim. Results indicate that, for horizons less than 1–4 h, persistence is the best model. For longer predictions, RF provides the best forecasts. For horizontal components at 4–24 h horizons, RF slightly outperformed ERA-Interim wind forecasts. For gust, RF performs better than ERA-Interim for all the horizons. Persistence is the most influential factor for 2–5 h. Beyond this horizon, predictors from the ERA-Interim wind forecasts led the contribution. Hybrid numerical–statistical methods can be used to improve short-term wind forecasts. ; This work was supported by the Spanish Government, MINECO project CGL2016-76561-R (MINECO/EU ERDF), and the University of the Basque Country (project GIU17/02).

Hywind-Scotland is a wind farm in Scotland that for many reasons is at the leading edge of technology and is located at a paradigmatic study area for offshore wind energy assessment. The objective of this paper is to compute the Capacity Factor ( CF ) changes and instantaneous power generation changes due to seasonal and hourly fluctuations in air density. For that reason, the novel ERA5 reanalysis is used as a source of temperature, pressure, and wind speed data. Seasonal results for winter show that CF values increase by 3% due to low temperatures and denser air, with economical profit consequences of tens of thousands (US$). Hourly results show variations of 7% in air density and of 26% in power generation via FAST simulations, emphasizing the need to include air density in short-term wind energy studying. ; This work was financially supported by the Spanish Government through the MINECO project CGL2016-76561-R, (MINECO/ERDF, UE) and the University of the Basque Country (UPV/EHU, GIU 17/002). ERA5 hindcast data were downloaded at no cost from the Copernicus Climate Data Store. All the calculations and plots were made using R: https://www.r-project.org.

ROSEO-BIWT is a new Building-Integrated Wind Turbine (BIWT) intended for installation on the edge of buildings. It consists of a Savonius wind turbine and guiding vanes to accelerate the usual horizontal wind, together with the vertical upward air stream on the wall. This edge effect improves the performance of the wind turbine, and its architectural integration is also beneficial. The hypothetical performance and design configuration were studied for a university building in Eibar city using wind data from the ERA5 reanalysis (European Centre for Medium-Range Weather Forecasts' reanalysis), an anemometer to calibrate the data, and the actual small-scale behavior in a wind tunnel. The data acquired by the anemometer show high correlations with the ERA5 data in the direction parallel to the valley, and the calibration is therefore valid. According to the results, a wind speed augmentation factor of three due to the edge effect and concentration vanes would lead to a increase in working hours at the rated power, resulting annually in more than 2000 h. ; The research leading to these results was carried out in the framework of the Programme Campus Bizia Lab EHU (Campus Living Lab) with a financial grant from the Office of Sustainability of the Vice-Chancellorship for Innovation, Social Outreach and Cultural Activities of the University of the Basque Country (UPV/EHU). This programme is supported by the Basque Government. We acknowledge also the availability given by the School of Engineering of Gipuzkoa-Eibar in the University of Basque Country, the EDP-Renewable awards in which we obtained the main award in September 2017, the Youth Enterprise Grant of UPV/EHU, and the project GIU17/02 of EHU/UPV. All computations and representations of this work were developed using the programming language R

A constant value of air density based on its annual average value at a given location is commonly used for the computation of the annual energy production in wind industry. Thus, the correction required in the estimation of daily, monthly or seasonal wind energy production, due to the use of air density, is ordinarily omitted in existing literature. The general method, based on the implementation of the wind speed's Weibull distribution over the power curve of the turbine, omits it if the power curve is not corrected according to the air density of the site. In this study, the seasonal variation of air density was shown to be highly relevant for the computation of offshore wind energy potential around the Iberian Peninsula. If the temperature, pressure, and moisture are taken into account, the wind power density and turbine capacity factor corrections derived from these variations are also significant. In order to demonstrate this, the advanced Weather Research and Forecasting mesoscale Model (WRF) using data assimilation was executed in the study area to obtain a spatial representation of these corrections. According to the results, the wind power density, estimated by taking into account the air density correction, exhibits a difference of 8% between summer and winter, compared with that estimated without the density correction. This implies that seasonal capacity factor estimation corrections of up to 1% in percentage points are necessary for wind turbines mainly for summer and winter, due to air density changes. ; This work has been funded by the Spanish Government's MINECO project CGL2016-76561-R (AEI/FEDER EU) and the University of the Basque Country (UPV/EHU funded project GIU17/02). The ECMWF ERA-Interim data used in this study have been obtained from the ECMWF-MARS Data Server. The authors wish to express their gratitude to the Spanish Port Authorities (Puertos del Estado) for being kind enough to provide data for this study. The computational resources used in the project were provided by I2BASQUE. The authors thank the creators of the WRF/ARW and WRFDA systems for making them freely available to the community. NOAA_OI_SST_V2 data provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, through their web-site at http://www.esrl.noaa.gov/psd/ were used in this paper. National Centres for Environmental Prediction/National Weather Service/NOAA/U.S. Department of Commerce. 2008, updated daily. NCEP ADP Global Upper Air and Surface Weather Observations (PREPBUFR format), May 1997—continuing. Research Data Archive at the National Centre for Atmospheric Research, Computational and Information Systems Laboratory. http://rda.ucar.edu/datasets/ds337.0/ were used. All the calculations have been carried out in the framework of R Core Team (2016). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/

The literature typically considers constant annual average air density when computing the wind energy potential of a given location. In this work, the recent reanalysis ERA5 is used to obtain global seasonal estimates of wind energy production that include seasonally varying air density. Thus, errors due to the use of a constant air density are quantified. First, seasonal air density changes are studied at the global scale. Then, wind power density errors due to seasonal air density changes are computed. Finally, winter and summer energy production errors due to neglecting the changes in air density are computed by implementing the power curve of the National Renewable Energy Laboratorys 5 MW turbine. Results show relevant deviations for three variables (air density, wind power density, and energy production), mainly in the middle-high latitudes (Hudson Bay, Siberia, Patagonia, Australia, etc.). Locations with variations from −6% to 6% are identified from summers to winters in the Northern Hemisphere. Additionally, simulations with the aeroelastic code FAST for the studied turbine show that instantaneous power production can be affected by greater than 20% below the rated wind speed if a day with realistically high or low air density values is compared for the same turbulent wind speed. ; This work was funded by the Spanish Government's MINECO project CGL2016-76561-R (AEI/FEDER EU) and the University of the Basque Country (UPV/EHU-funded project GIU17/02). The ECMWFERA-5 data used in this study were obtained from the Copernicus Climate Data Store. All the calculations were carried out in the framework of R Core Team (2016). More can be learnt about R, alanguage and an environment for statistical computing, at the website of the R Foundation for Statistical Computing, Vienna,Austria (https://www.R-project.org/).

In this article, offshore wind energy potential is measured around the Iberian Mediterranean coast and the Balearic Islands using the WRF meteorological model without 3DVAR data assimilation (the N simulation) and with 3DVAR data assimilation (the D simulation). Both simulations have been checked against the observations of six buoys and a spatially distributed analysis of wind based on satellite data (second version of Cross-Calibrated Multi-Platform, CCMPv2), and compared with ERA-Interim (ERAI). Three statistical indicators have been used: Pearson's correlation, root mean square error and the ratio of standard deviations. The simulation with data assimilation provides the best fit, and it is as good as ERAI, in many cases at a 95% confidence level. Although ERAI is the best model, in the spatially distributed evaluation versus CCMPv2 the D simulation has more consistent indicators than ERAI near the buoys. Additionally, our simulation's spatial resolution is five times higher than ERAI. Finally, regarding the estimation of wind energy potential, we have represented the annual and seasonal capacity factor maps over the study area, and our results have identified two areas of high potential to the north of Menorca and at Cabo Begur, where the wind energy potential has been estimated for three turbines at different heights according to the simulation with data assimilation. ; This work has been funded by the Spanish Government's MINECO project CGL2016-76561-R (MINECO/FEDER EU), the University of theBasque Country (project GIU14/03) and the Basque Government (Elkartek 2017 INFORMAR project). SJGR is supported by a FPIPredoctoral Research Grant (MINECO, BES-2014-069977). The ECMWFERA-Interim data used in this study have been obtained from the ECMWF-MARS Data Server thanks to agreements with ECMWF and AEMET. The authors would like to express their gratitude to the Spanish Port Authorities (Puertos del Estado) for kindly providing data for thisstudy. The computational resources used in the project were providedby I2BASQUE. The authors thank the creators of the WRF/ARW and WRFDA systems for making them freely available to the community. NOAA_OI_SST_V2 data provided by the NOAA/OAR/ESRL PSD,Boulder, Colorado, USA, through their web-site athttp://www.esrl.noaa.gov/psd/was used in this paper. National Centers for Environmental Prediction/National Weather Service/NOAA/U.S.Department of Commerce. 2008, updated daily. NCEP ADP GlobalUpper Air and Surface Weather Observations (PREPBUFR format), May1997–Continuing. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory.http://rda.ucar.edu/datasets/ds337.0/were used. All thecalculations have been carried out in the framework of R Core Team(2016). R: A language and environment for statistical computing. RFoundation for Statistical Computing, Vienna, Austria. URLhttps://www.R-project.org/.

A new diagram is proposed for the verification of vector quantities generated by multiple models against a set of observations. It has been designed with the objective, as in the Taylor diagram, of providing a visual diagnostic tool which allows an easy comparison of simulations by multiple models against a reference dataset. However, the Sailor diagram extends this ability to two-dimensional quantities such as currents, wind, horizontal fluxes of water vapour and other geophysical variables by adding features which allow us to evaluate directional properties of the data as well. The diagram is based on the analysis of the two-dimensional structure of the mean squared error matrix between model and observations. This matrix is separated in a part corresponding to the bias and the relative rotation of the two orthogonal directions (empirical orthogonal functions; EOFs) which best describe the vector data. Since there is no truncation of the retained EOFs, these orthogonal directions explain the total variability of the original dataset. We test the performance of this new diagram to identify the differences amongst the reference dataset and a series of model outputs by using some synthetic datasets and real-world examples with time series of variables such as wind, current and vertically integrated moisture transport. An alternative setup for spatially varying time-fixed fields is shown in the last examples, in which the spatial average of surface wind in the Northern and Southern Hemisphere according to different reanalyses and realizations from ensembles of CMIP5 models are compared. The Sailor diagrams presented here show that it is a tool which helps in identifying errors due to the bias or the orientation of the simulated vector time series or fields. The R implementation of the diagram presented together with this paper allows us also to easily retrieve the individual diagnostics of the different components of the mean squared error and additional diagnostics which can be presented in tabular form. ; This research has been supported by the Spanish Government's MINECO grant and ERDF (grant no. CGL2016- 76561-R) and the UPV/EHU (grant no. GIU17/02).