Short-Term Forecasting of Loads and Wind Power for Latvian Power System: Accuracy and Capacity of the Developed Tools

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.2015.04.134Search 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), 124–131.10.1109/59.141695Search 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, 44–55.10.1109/59.910780Search 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:20020224Search 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.2007.01.015Search 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.2006.10.005Search 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.2008.09.006Search 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.2014.11.084Search 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.2010.04.022Search 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.2015.03.027Search 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:10.5772/673.10.5772/673Search 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