• 대한전기학회
Mobile QR Code QR CODE : The Transactions of the Korean Institute of Electrical Engineers
  • COPE
  • kcse
  • 한국과학기술단체총연합회
  • 한국학술지인용색인
  • Scopus
  • crossref
  • orcid

References

1 
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
2 
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
3 
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
4 
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
5 
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
6 
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
7 
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
8 
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
9 
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
10 
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
11 
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
12 
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
13 
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
14 
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
15 
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
16 
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
17 
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