[1. Dowds, J., Hines, P., Ryan, T., Buchanan, W., Kirby, E., Apt, J., and Jaramillo, P. (2015). A review of large-scale wind integration studies. Renewable and Sustainable Energy Rev., 49, 768–794. doi:10.1016/j.rser.2015.04.134.10.1016/j.rser.2015.04.134]Search in Google Scholar
[2. Petrichenko, R., Chuvychin, V., and Sauhats, A. (2013). Coexistence of different load shedding algorithms in interconnected power system. In: 12th International Conference on Environment and Electrical Engineering, Wroclaw (Poland), art. no. 6549626, (pp. 253–258).]Search in Google Scholar
[3. Zalostiba, D. (2013). Power system blackout prevention by dangerous overload elimination and fast self-restoration. In: IEEE European Innovative Smart Grid Technologies Conference, Copenhagen (Denmark), art. no. 6695371.]Search in Google Scholar
[4. Augstsprieguma tīkls. [Latvian Transmission System Operator] (2014). Elektroenerģijas pārvades sistēmas attīstības plāns. [Development Plan of Transmission Power System]. Riga, 29 p. Available at http://www.ast.lv/files/ast_files/gadaparskzinoj/Latvijas_10GAP_2014.pdf.]Search in Google Scholar
[5. Litgrid AB (2014). Development of the Lithuanian Electric Power System and Transmission Grids. 49 p. Available at http://www.leea.lt/wp-content/uploads/2015/05/Network-development-plan-2015.pdf.]Search in Google Scholar
[6. EWEA (2014). Wind Energy Scenarios for 2020. A report by the European Wind Energy Association. 8 p. Available at http://www.ewea.org/fileadmin/files/library/publications/scenarios/EWEA-Wind-energy-scenarios-2020.pdf.]Search in Google Scholar
[7. Lee, K.Y., Cha, Y.T., and Park J.H. (1992). Short term load forecasting using an artificial neural network. IEEE Trans. PAS 7 (1), 124–131.10.1109/59.141695]Search in Google Scholar
[8. Hippert, H.S., Pedreira, C.E., and Souza, R.C. (2001). Neural networks for short term load forecasting: A review and evaluation. IEEE Trans. Power Syst.16, 44–55.10.1109/59.910780]Search in Google Scholar
[9. Marin, F.J., Garcia-Lagos, F., Joya, G., and Sandoval, F. (2002). Global model for short-term load forecasting using artificial neural networks. IEE Proc.-Gener. Transm. Distrib. 149, 121–125.10.1049/ip-gtd:20020224]Search in Google Scholar
[10. Costa, A., Crespo, A., Navarro, J., Lizcano, G., Madsen, H., and Feitosa, E. (2008). A review on the young history of the wind power short-term prediction. Renewable and Sustainable Energy Rev.12, 1725–1744.10.1016/j.rser.2007.01.015]Search in Google Scholar
[11. Milligan, M. (2003). Wind Power Plants and System Operation in the Hourly Time Domain. Austin (Texas, USA), Windpower 2003, 23 p. NREL/CP-500-33955. Available at http://www.nrel.gov/publications/.]Search in Google Scholar
[12. Cadenas, E., and Rivera, W. (2007). Wind speed forecasting in the South Coast of Oaxaca, México. Renewable Energy32, 2116–2128.10.1016/j.renene.2006.10.005]Search in Google Scholar
[13. Kavasseri, R. G., and Seetharaman, K. (2009). Day-ahead wind speed forecasting using f-ARIMA models. IEEE Tran. Renewable Energy 34, 1388–1393. DOE:10.1016/j.renene.2008.09.006.10.1016/j.renene.2008.09.006]Search in Google Scholar
[14. Shukur, O. B., and Lee M. H. (2015). Daily wind speed forecasting through hybrid KFANN model based on ARIMA. Renewable Energy76, 637–647.10.1016/j.renene.2014.11.084]Search in Google Scholar
[15. Cadenas, E., and Rivera, W. (2010). Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA–ANN model. Renewable Energy35, 2732–273810.1016/j.renene.2010.04.022]Search in Google Scholar
[16. Augstsprieguma tīkls. [Latvian Transmission System Operator] (2015). Demand, net exchange and production. Available at http://www.ast.lv/eng/power_system/archive.]Search in Google Scholar
[17. Riga Actual Weather Archive. (2015). Available at http://www.meteoprog.lv/en/weather/Riga/.]Search in Google Scholar
[18. Khwaja, A.S., Naeem., M., Anpalagan A., Venetsanopoulos, A., and Venkatesh, B. (2015). Improved short-term load forecasting using bagged neural networks. Electr. Power Syst. Res. 125, 109–115.10.1016/j.epsr.2015.03.027]Search in Google Scholar
[19. Bañuelos-Ruedas, F., Angeles-Camacho, C., and Rios-Marcuello, S. (2011). Methodologies used in the extrapolation of wind speed data at different heights and its impact in the wind energy resource assessment in a region. In: Wind Farm – Technical Regulations, Potential Estimation and Siting Assessment / Suvire G. O (Ed.): InTech, 246 p. DOI:10.5772/673.10.5772/673]Search in Google Scholar
[20. Radziukynas, V., and Klementavicius, A. (2014). Short-term wind speed forecasting with ARIMA model. In: 55th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON), Riga (Latvia), (pp. 145–149). Doi 10.1109/RTUCON.2014.6998223.]Search in Google Scholar
[21. ENERCON. (2015). Enercon Wind Turbines. Product overview.]Search in Google Scholar