This document has not been cited yet.

1. Almeshaiei, E., Soltan, H. 2011. A methodology for electric power load forecasting. Alexandria Engineering Journal 50: 137–144. doi: 10.1016/j.aej.2011.01.015.
   Paper not yet in RePEc: Add citation now
   
2. Amjady, N. 2001. Short-term hourly load forecasting using time-series modeling with peak load estimation capability. IEEE Transactions on Power Systems 16(3): 498–503. doi: 10.1109/59.962429.
   Paper not yet in RePEc: Add citation now
   
3. Bremnes, J. B. 2004. Probabilistic wind power forecasts using local quantile regression.
   Paper not yet in RePEc: Add citation now
   
4. Chen, C., Wei, Y. 2005. Computational issues for quantile regression. Sankhyā: The Indian Journal of Statistics 67(2): 399–417. Retrieved from http://www.jstor.org/stable/25053439.
   Paper not yet in RePEc: Add citation now
   
5. Dash, P. K., Liew, A. C., Rahman, S. 1995. Comparison of Fuzzy Neural Networks for the generation of daily average and peak load profiles. International Journal of System Science 26: 2091–2106. doi: 10.1080/00207729508929156.
   Paper not yet in RePEc: Add citation now
   
6. Engle, R. F., Brown, S. J., Stern, G. 1988. A comparison of adaptive structural forecasting methods for electricity sales. Journal of Forecasting 7: 149–172. doi: 10.1002/for.3980070302.
   Paper not yet in RePEc: Add citation now
   
7. García-Ascanio, C., Maté, C. 2010. Electric power demand forecasting using interval time series: A comparison between VAR and iMLP. Energy Policy 38(2): 715–725. doi: 10.1016/j.enpol.2009.10.007.
   Paper not yet in RePEc: Add citation now
   
8. Gibbons, C., Faruqui, A. 2014. Quantile regression for peak demand forecasting. Available at http://ssrn.com/abstract=2485657.
   Paper not yet in RePEc: Add citation now
   
9. Haida, T., Muto, S. 1994. Regression based peak load forecasting using a transformation technique. IEEE Transaction on Power Systems 9 (4): 1788–1794. doi: 10.1109/59.331433.
   Paper not yet in RePEc: Add citation now
   
10. Hao, L., Naiman, Q. D. 2007. Quantile Regression, SAGE publications: California.
    Paper not yet in RePEc: Add citation now
    
11. Hong, T. 2010. Short term electric load forecasting. PhD dissertation, North Carolina State University.
    Paper not yet in RePEc: Add citation now
    

12. Hong, T., Fan, S. 2016. Probabilistic electric load forecasting: A tutorial review.

13. Hong, T., Maciejowska, K., Nowotarski, N., Weron, R. 2014. Probabilistic load forecasting via Quantile Regression Averaging of independent expert forecasts. Hugo Steinhaus Center, Wrocław University of Technology http://www.im.pwr.wroc.pl/~hugo/RePEc/wuu/wpaper/HSC_14_10.pdf Juban, R., Ohlsson, H., Maasoumya, M., Poirier, L., Kolter J.Z. 2016. A multiple quantile regression approach to the wind, solar, and price tracks of GEFCom2014. International Journal of Forecasting 32: 1094–1102. doi: 10.1016/j.ijforecast.2015.12.002.

14. Koenker, R., Bassett, G. 1978. Regression quantiles. Econometrica 46(1): 33–50.

15. Liu, B., Nowotarski, J., Hong, T., Weron, R. 2015. Probabilistic load forecasting via quantile regression averaging on sister forecasts. IEEE Transactions on Smart Grid (in press) McSharry, P. E., Bouwman S., Bloemhof, G. 2005. Probabilistic forecast of the magnitude and timing of peak electricity demand. IEEE Transaction on Power Systems 20(2): 1166– 1172. doi: 10.1109/TPWRS.2005.846071.

16. Munoz, A., Sanchez-Ubeda, E. F., Cruz, A., Marin, J. 2010. Short-term forecasting in power systems: a guided tour. Energy Systems 2: 129–160. doi: 10.1007/978-3-64212686 -4_5.
    Paper not yet in RePEc: Add citation now
    

17. Ohtsuka, Y., Oga, T., Kakamu, K. 2010. Forecasting electricity demand in Japan: A Bayesian spatial autoregressive ARMA Approach. Computational Statistics & Data Analysis 54(11): 2721–2735.

18. Sadownik, R. Barbosa, E. P. 1999. Short-term forecasting of industrial electricity consumption in Brazil. Journal of Forecasting 18: 215–224. doi: 10.1002/(SICI)1099-131X(199905)18:3<215::AID-FOR719>3.0.CO;2-B.
    Paper not yet in RePEc: Add citation now
    

19. Soares, L. J., Medeiros, M. C. 2008. Modeling and forecasting short term electricity load: A comparison of methods with an application to Brazilian Data. International Journal of Forecasting 24:630–644. doi: 10.1016/j.ijforecast.2008.08.003.

20. Taylor, J. W., McSharry, P. E. 2007. Short-term load forecasting methods: An evaluation based on European data. IEEE Transaction on Power Systems 20(2): 1166–1172. doi: 10.1109/TPWRS.2007.907583.
    Paper not yet in RePEc: Add citation now
    

21. Weron, R. 2007. Modeling and forecasting electricity loads and prices: A statistical approach. Wiley: Chichester.

22. Xiong T., Bao, Y., Hu, Z. 2014. Interval forecasting of electricity demand: A novel bivariate EMD-based support vector regression modeling framework. International Journal of Electrical Power and Energy Systems. doi: 10.1016/j.ijepes.2014.06.01. Accepted
    Paper not yet in RePEc: Add citation now
    

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    In: Renewable and Sustainable Energy Reviews.
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    In: International Journal of Forecasting.
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    In: International Journal of Forecasting.
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    In: Energy.
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    In: European Journal of Operational Research.
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    In: Applied Energy.
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    In: Applied Energy.
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    Full description at Econpapers || Download paper

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    In: Applied Energy.
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    Full description at Econpapers || Download paper

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    In: Applied Energy.
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    Full description at Econpapers || Download paper

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    In: International Journal of Energy Economics and Policy.
    RePEc:eco:journ2:2018-05-16.

    Full description at Econpapers || Download paper

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    In: HSC Research Reports.
    RePEc:wuu:wpaper:hsc1702.

    Full description at Econpapers || Download paper

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    In: Discussion Papers in Economics and Business.
    RePEc:osk:wpaper:1728.

    Full description at Econpapers || Download paper

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    In: Sustainability.
    RePEc:gam:jsusta:v:9:y:2017:i:5:p:795-:d:98155.

    Full description at Econpapers || Download paper

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    In: Energy.
    RePEc:eee:energy:v:134:y:2017:i:c:p:902-918.

    Full description at Econpapers || Download paper

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    In: Applied Energy.
    RePEc:eee:appene:v:193:y:2017:i:c:p:369-380.

    Full description at Econpapers || Download paper

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    In: HSC Research Reports.
    RePEc:wuu:wpaper:hsc1607.

    Full description at Econpapers || Download paper

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    In: Discussion Papers in Economics and Business.
    RePEc:osk:wpaper:1622.

    Full description at Econpapers || Download paper

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    In: Energies.
    RePEc:gam:jeners:v:9:y:2016:i:8:p:635-:d:76042.

    Full description at Econpapers || Download paper

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    In: International Journal of Forecasting.
    RePEc:eee:intfor:v:32:y:2016:i:3:p:896-913.

    Full description at Econpapers || Download paper

47. Electric load forecasting with recency effect: A big data approach. (2016). Hong, Tao ; Wang, PU ; Liu, Bidong .
    In: International Journal of Forecasting.
    RePEc:eee:intfor:v:32:y:2016:i:3:p:585-597.

    Full description at Econpapers || Download paper

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    In: International Journal of Forecasting.
    RePEc:eee:intfor:v:32:y:2016:i:3:p:1051-1056.

    Full description at Econpapers || Download paper

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    In: International Journal of Forecasting.
    RePEc:eee:intfor:v:32:y:2016:i:3:p:1012-1016.

    Full description at Econpapers || Download paper

50. Electric load forecasting with recency effect: A big data approach. (2015). Hong, Tao ; Wang, PU ; Liu, Bidong .
    In: HSC Research Reports.
    RePEc:wuu:wpaper:hsc1508.

    Full description at Econpapers || Download paper

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