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

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

Open Access
|May 2016

References

  1. 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
  2. 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).
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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/.
  12. 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
  13. 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
  14. 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
  15. 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
  16. 16. Augstsprieguma tīkls. [Latvian Transmission System Operator] (2015). Demand, net exchange and production. Available at http://www.ast.lv/eng/power_system/archive.
  17. 17. Riga Actual Weather Archive. (2015). Available at http://www.meteoprog.lv/en/weather/Riga/.
  18. 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
  19. 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
  20. 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.
  21. 21. ENERCON. (2015). Enercon Wind Turbines. Product overview.
DOI: https://doi.org/10.1515/lpts-2016-0008 | Journal eISSN: 2255-8896 | Journal ISSN: 0868-8257
Language: English
Page range: 3 - 13
Published on: May 20, 2016
Published by: Institute of Physical Energetics
In partnership with: Paradigm Publishing Services
Publication frequency: 6 issues per year

© 2016 V. Radziukynas, A. Klementavičius, published by Institute of Physical Energetics
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.