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Ceperic E., Ceperic V., Baric A., Nov. 2013, A strategy for short-term load forecasting by support vector regression machines, IEEE Trans. Power Syst., Vol. 28, No. 4, pp. 4356-4364DOI
Park Y. S., Ji P. S., Sep, 2014, Development of Daily Peak Power Demand Forecasting Algorithm with Hybrid Type composed of AR and Neuro-Fuzzy Model, Trans. KIEE., Vol. 63P, No. 3, pp. 189-194DOI
Hahn H., Meyer-Nieberg S., Pickl S., Dec. 2009, Electric load forecasting methods: Tools for decision making, Eur. J. Oper. Res., Vol. 199, No. 3, pp. 902-907DOI
Raza M., Khosravi A., 2015, A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings, Renew. Sustain. Energy Rev., Vol. 50, pp. 1352-1372DOI
Hong T., Wilson J., Xie J., Jan. 2014, Long term probabilistic load forecasting and normalization with hourly information, IEEE Trans. Smart Grid, Vol. 5, No. 1, pp. 456-462DOI
Singhal D., Swarup K., Mar. 2011, Electricity price forecasting using artificial neural networks, Int. J. Elect. Power Energy Syst., Vol. 33, No. 3, pp. 550-555DOI
Karsaz A., Mashhadi H. R., Mirsalehi M. M., Jun. 2010, Market clearing price and load forecasting using cooperative co-evolutionary approach, Int. J. Elect. Power Energy Syst., Vol. 32, No. 5, pp. 408-415DOI
Hernandez L., Baladron C., Aguiar J. M., Calavia L., Carro B., Sanchez-Esguevillas A., Sanjuan J., Gonzalez L., Lloret J., 2016, Improved short-term load forecasting based on two-stage predictions with artificial neural networks in a microgrid environment, Energies, Vol. 6, pp. 4489-4507DOI
Omer F. E., 2016, Forecasting electricity load by a novel recurrent extreme learning machines approach, Int. J. Elect. Power Energy Syst., Vol. 78, pp. 429-435DOI
Amjady N., Keynia F., Jan. 2009, Short-term load forecasting of power systems by combination of wavelet transform and neuro-evolutionary algorithm, Energy, Vol. 34, No. 1, pp. 46-57DOI
Ekonomou L., Christodoulou C., Mladenov V., 2016, A Short-Term Load Forecasting Method Using Artificial Neural Networks and Wavelet Analysis, Int. J. Power Syst., Vol. 1, pp. 64-68Google Search
Chen B. J., Chang M. W., Lin C. J., Nov. 2004, Load forecasting using support vector machines: A study on eunite competition 2001, IEEE Trans. Power Syst., Vol. 19, No. 4, pp. 1821-1830DOI
Huang G. B., Chen L., Oct. 2008, Enhanced random search based incremental extreme learning machine, Neuro-computing, Vol. 71, No. 16-18, pp. 3460-3468DOI
Xie T., Yu H., Hewlett J., Rózycki P., Wilamowski B., Apr. 2012, Fast and efficient second-order method for training radial basis function networks, IEEE Trans. Neural Netw. Learn. Syst., Vol. 23, No. 4, pp. 609-619DOI
Yu H., Reiner P., Xie T., Bartczak T., Wilamowski B., Oct. 2014, An incremental design of radial basis function networks, IEEE Trans. Neural Netw. Learn. Syst., Vol. 25, No. 10, pp. 1793-1802DOI
Hippert H. S., Pedreira C. E., Souza R. C., Feb. 2001, Neural networks for short-term load forecasting: a review and evaluation, IEEE Trans. Power syst., Vol. 16, No. 1, pp. 44-55DOI
Koo B., Kim H., Lee H., Park J. H., AUG, 2015, Short-term Electric Load Forecasting for Summer Season using Temperature Data, Trans. KIEE., Vol. 64, No. 8, pp. 1137-1144DOI