The paper analyses the performance results of the recently developed short-term forecasting suit for the Latvian power system. The system load and wind power are forecasted using ANN and ARIMA models, respectively, and the forecasting accuracy is evaluated in terms of errors, mean absolute errors and mean absolute percentage errors. The investigation of influence of additional input variables on load forecasting errors is performed. The interplay of hourly loads and wind power forecasting errors is also evaluated for the Latvian power system with historical loads (the year 2011) and planned wind power capacities (the year 2023).
If the inline PDF is not rendering correctly, you can download the PDF file here.
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.
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. 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.
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.
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.
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. 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.
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. Cadenas, E., and Rivera, W. (2007). Wind speed forecasting in the South Coast of Oaxaca, México. Renewable Energy32, 2116–2128.
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.
14. Shukur, O. B., and Lee M. H. (2015). Daily wind speed forecasting through hybrid KFANN model based on ARIMA. Renewable Energy76, 637–647.
15. Cadenas, E., and Rivera, W. (2010). Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA–ANN model. Renewable Energy35, 2732–2738
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.
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.
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.