[
[1] Directive (EU) 2018/2001 of the European Parliament and of the Council of 11 December 2018 on the promotion of the use of energy from renewable sources. Official Journal of European Union 2018: L 328/82.
]Search in Google Scholar
[
[2] Eurostat. [Online]. [Accessed 20.02.2020]. Available: https://ec.europa.eu/eurostat/
]Search in Google Scholar
[
[3] Ministry of Energy, Ministry of environment. National renewable energy action plan. Vilnius: Ministry of Energy of the Republic of Lithuania, 2010.
]Search in Google Scholar
[
[4] Blumberga A., et al. Modelling the Baltic power system till 2050. Energy Conversion and Management 2016:107:67–75. https://doi.org/10.1016/j.enconman.2015.09.00510.1016/j.enconman.2015.09.005
]Search in Google Scholar
[
[5] Official Statistics Portal [Online]. [Accessed 20.02.2020]. Available: https://osp.stat.gov.lt/informaciniai-pranesimai?eventId=200543
]Search in Google Scholar
[
[6] Lithuanian wind power association (LVEA), statistics of Lithuania. [Online]. [Accessed 20.02.2020]. Available: https://lvea.lt/en/statistics/lithuanian-statistics/
]Search in Google Scholar
[
[7] Solaun K., Cerda E. Climate change impacts on renewable energy generation. A review of quantitative projections. Renewable and Sustainable Energy Reviews 2019:116:1–16. https://doi.org/10.1016/j.rser.2019.10941510.1016/j.rser.2019.109415
]Search in Google Scholar
[
[8] Weber J., Gotzens F., Witthaut D. Impact of strong climate change on the statistics of wind power generation in Europe. Energy Procedia 2018:153:22–28. https://doi.org/10.1016/j.egypro.2018.10.00410.1016/j.egypro.2018.10.004
]Search in Google Scholar
[
[9] Carvalho D., et al. Potential impacts of climate change on European wind energy resources under the CMIP5 future climate projections. Renewable Energy 2017:101:29–40. https://doi.org/10.1016/j.renene.2016.08.03610.1016/j.renene.2016.08.036
]Search in Google Scholar
[
[10] Cosseron A., et al. Characterization of the Wind Power Resource in Europe and its Intermittency. Energy Procedia 2013:40:58–66. https://doi.org/10.1016/j.egypro.2013.08.00810.1016/j.egypro.2013.08.008
]Search in Google Scholar
[
[11] Shipkovs P., et al. Research of the wind energy resource distribution in the Baltic region. Renewable Energy 2013:49:119–123. https://doi.org/10.1016/j.renene.2012.01.05010.1016/j.renene.2012.01.050
]Search in Google Scholar
[
[12] Lithuanian Hydrometeorological Service (LHMS). Climate averages for Lithuania 1981 – 2010. 2013. [Online]. [Accessed 20.02.2020]. Available: http://www.meteo.lt/documents/20181/103901/Lietuvos_klimatas_09_25.pdf/e307f875-d20b-4a4d-aa90-c66a4dd57885
]Search in Google Scholar
[
[13] MERRA-2 [Online]. [Accessed 20.02.2020]. Available: https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/
]Search in Google Scholar
[
[14] The Intergovernmental Panel on Climate Change (IPCC). The Climate change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Geneva: IPCC, 2014.
]Search in Google Scholar
[
[15] van Vuuren D. P., et al. The representative concentration pathways: an overview. Climatic Change 2011:109:5. https://doi.org/10.1007/s10584-011-0148-z10.1007/s10584-011-0148-z
]Search in Google Scholar
[
[16] Luo M., et al. Comparing bias correction methods used in downscaling precipitation and temperature from regional climate models: a case study from the Kaidu river basin in Western China. Water 2018:10(8):1046. https://doi.org/10.3390/w1008104610.3390/w10081046
]Search in Google Scholar
[
[17] Holman, B. P., Lazaris, S. T., Splitt, M. A fetch-based statistical method to bias correct and downscale wind speed over unresolved water bodies. Weather and forecasting 2017:32:1637–1657. https://doi.org/10.1175/WAF-D-17-0016.110.1175/WAF-D-17-0016.1
]Search in Google Scholar
[
[18] Li D., et al. Statistical bias correction for simulated wind speeds over CORDEX-East Asia. Earth and Space Science 2019:10:200–211. https://doi.org/10.1029/2018EA00049310.1029/2018EA000493
]Search in Google Scholar
[
[19] Zhang T., et al. Bias-correction method for wind speed forecasting. Journal of Arid Meteorology 2017:28(4):1042–1052. https://doi.org/10.1127/metz/2019/095010.1127/metz/2019/0950
]Search in Google Scholar
[
[20] Lafon T., et al. Bias correction of daily precipitation simulated by a regional climate model: a comparison of methods. International Journal of Climatology 2013:33:1367–1381. https://doi.org/10.1002/joc.351810.1002/joc.3518
]Search in Google Scholar
[
[21] Byrne R., et al. An assessment of the mesoscale to microscale influences on wind turbine energy performance at a peri-urban coastal location from the Irish wind atlas and onsite LiDAR measurements. Sustainable Energy Technologies and Assessments 2019:36:100537. https://doi.org/10.1016/j.seta.2019.10053710.1016/j.seta.2019.100537
]Search in Google Scholar
[
[22] Manwell J., F., Mcgowan J. G., Rogers A. L. Wind energy explained. Wiltshire: John Willey & Sons. 2009.10.1002/9781119994367
]Search in Google Scholar
[
[23] Troen I., Lundtang P. E. European wind atlas. Roskilde: Risø National Laboratory. 1989.
]Search in Google Scholar
[
[24] Albani A., Ibrahim M. Z., Yong K. H. Wind shear data at two different terrain types. Data in brief 2019:25:104306. https://doi.org/10.1016/j.dib.2019.10430610.1016/j.dib.2019.104306668569931406911
]Search in Google Scholar
[
[25] R software – Rstudio [Online]. [Accessed 20.02.2020]. Available: https://rstudio.com/
]Search in Google Scholar
[
[26] Qgis project [Online]. [Accessed 20.02.2020]. Available: https://www.qgis.org/lt/site/
]Search in Google Scholar
[
[27] Rind D. The consequences of not knowing low- and high-latitude climate sensitivity. Bulletin of the American Meteorological Society 2008:89:855–864. https://doi.org/10.1175/2007BAMS2520.110.1175/2007BAMS2520.1
]Search in Google Scholar
[
[28] Nikulin G., et al. Evaluation and future projections of temperature, precipitation and wind extremes over Europe in an ensemble of regional climate simulations. Tellus A: Dynamic Meteorology and Oceanography 2011:63(1):41–55. https://doi.org/10.1111/j.1600-0870.2010.00466.x10.1111/j.1600-0870.2010.00466.x
]Search in Google Scholar
[
[29] Lorenz D. J., DeWeaver E. T. Tropopause height and zonal wind response to global warming in the IPCC scenario integrations. Journal of Geophysical Research. Atmospheres 2007:112(D10):D10119. https://doi.org/10.1029/2006JD00808710.1029/2006JD008087
]Search in Google Scholar
[
[30] Haugen J., Iversen T. Response in extremes of daily precipitation and wind from a downscaled multi-model ensemble of anthropogenic global climate change scenarios. Tellus A: Dynamic Meteorology and Oceanography 2008:60(3):411–426. https://doi.org/10.1111/j.1600-0870.2008.00315.x10.1111/j.1600-0870.2008.00315.x
]Search in Google Scholar
[
[31] Martinez C. I. P., Cotte Poveda A. The importance of science, technology and innovation in the green growth and sustainable development goals of Colombia. Environmental and Climate Technologies 2021:25:29–41. https://doi.org/10.2478/rtuect-2021-000310.2478/rtuect-2021-0003
]Search in Google Scholar
[
[32] Blumberga D., et al. Energy, bioeconomy, climate change and environment nexus. Environmental and Climate technologies 2019:23(3):370–392. https://doi.org/10.2478/rtuect-2019-010210.2478/rtuect-2019-0102
]Search in Google Scholar
