[1]C. Wang, G. Grozev and S. Seo, “Decomposition and statistical analysis for regional electricity demand forecasting,” Energy, vol. 41, no. 1, pp. 313-325, May 2012.
[2]M. R. AlRashidi and K. M. EL-Naggar, “Long-term electric load forecasting based on particle swarm optimization,” Applied Energy, vol. 87, no. 1, pp. 320–326, January 2010.
[3]K. B. Song, Y. S. Baek, D.H. Hong and G. Jang, “Short-term load forecasting for the holidays using fuzzy linear regression method,” IEEE Transactions on Power Systems, vol. 20, no. 1, pp. 96-101, February 2005.
[4]T. Senjyu, , H. Takara, K. Uezato and T. Funabashi, “One-hour-ahead load forecasting using neural network,”IEEE Transactions On Power Systems, vol. 17, no. 1, pp. 113-118, February 2002.
[5]A. Goia, C. May and G. Fusai, “Functional clustering and linear regression for peak load forecasting,” International Journal of Forecasting, vol. 26, no. 4, pp. 700-711, October 2010.
[6]S. E. Papadakis, J. B. Theocharis and A. G. Bakirtzis, “A load curve based fuzzy modeling technique for short-term load forecasting,” Fuzzy Sets and Systems, vol. 135, no. 2, pp. 279–303, April 2003.
[7]A. M. Al-Kandari, S.A. Soliman and M. E. El-Hawary, “Fuzzy short-term electric load forecasting,” Electrical Power and Energy Systems, vol. 26, no. 2, pp. 111–122, February 2004.
[8]M. R. Amin-Naseri and A. R. Soroush, “Combined use of unsupervised and supervised learning for daily peak load forecasting,” Energy Conversion and Management, vol. 49, no. 6, pp. 1302-1308, June 2008.
[9]M. Moazzami, A. Khodabakhshian and R. Hooshmand, “A new hybrid day-ahead peak load forecasting method for Iran’s national grid,” Applied Energy, vol. 101, pp. 489–501, January 2013.
[10]A. Deihimi, O. Orang and H. Showkati, “Short-term electric load and temperature forecasting using wavelet echo state networks with neural reconstruction,” Energy, vol. 57, no. 1, pp. 382-401, August 2013.
[11]D. J. Pedregal and J. R. Trapero, “Mid-term hourly electricity forecasting based on a multi-rate approach,” Energy Conversion and Management, vol. 51, no. 1, pp. 105–111, January 2010.
[12]E. Gonzalez-Romera, M. A. Jaramillo-Morn and D. Carmona-Fernandez, “Monthly electric energy demand forecasting with neural networks and fourier series,” Energy Conversion and Management, vol. 49, no. 11, pp. 3135–3142, November 2008.
[13]N. Amjady and F. Keynia, “Mid-term load forecasting of power systems by a new prediction method,” Energy Conversion and Management, vol. 49, no. 10, pp. 2678-2687, October 2008.
[14]U. Basaran Filik, O. Nezih Gerek and M. Kurban, “A novel modeling approach for hourly forecasting of long-term electric energy demand,” Energy Conversion and Management, vol. 52, no.1, pp. 199–211, January 2011.
[15]T. Chen and Y. C. Wang, “Long-term load forecasting by a collaborative fuzzy-neural approach,” Electrical Power and Energy Systems, vol. 43, no. 1, pp. 454–464, December 2012.
[16]H. Mori and E. Kurata, “Graphical modeling for selecting input variable of short-term load forecasting,” IEEE
Power Tech Conference, Lausanne, Switzerland, July 2007.
[17]R. J. Hyndman and S. Fan, “Density forecasting for long-term peak electricity demand,” IEEE Transactions on Power Systems, vol. 25, no. 2, pp. 1142-1153, May 2010.
[18]H. M. Al-Hamadi and S. A. Soliman, “Long-term/mid-term electric load forecasting based on short-term correlation and annual growth,” Electric Power Systems Research, vol. 74, no. 3, pp. 353-361, June 2005.
[19]N. X. Jia, R. Yokoyama, Y. C. Zhou and Z. Y. Gao, “A flexible long-term load forecasting approach based on new dynamic simulation theory-GSIM,” Electrical Power and Energy Systems, vol. 23, no. 7, pp. 549-556, October 2001.
[20]A. Azadeh, S. F. Ghaderi and S. Sohrabkhani, “Annual electricity consumption forecasting by neural network in high energy consuming industrial sectors,” Energy Conversion and Management, vol. 49, no. 8, pp. 2272–2278, August 2008.
[21]Y. Aslan, S. Yavasca and C. Yasar, “Long-term electric peak load forecasting of Kutahya using different approaches,” International Journal on Technical and Physical Problems of Engineering (IJTPE),vol. 3, no. 2, pp. 87-91, June 2011.
[22]L. Ghelardoni, A. Ghio and D. Anguita, “Energy load forecasting using empirical mode decomposition and support vector regression,” IEEE Transactions on Smart Grid, vol. 4, no. 1, pp. 549-556, March 2013.
[23]T. Chen, “A collaborative fuzzy-neural approach for long-term load forecasting in Taiwan,” Computers & Industrial Engineering, vol. 63, no. 3, pp. 663–670, November 2012.
[25]A. J. Rocha Reis, and A. P. Alves da Silva, “Feature extraction via multiresolution analysis for short-term load forecasting,” IEEE Transactions on Power Systems, vol. 20, no. 1, pp. 189-198, February 2005.
[26]D. Benaouda, F. Murtagh, J. L. Starck and O. Renaud, “Wavelet-based nonlinear multiscale decomposition model for electricity load forecasting,” Neurocomputing, vol. 70, no. 1-3, pp. 139–154, December 2006.
[27]طوفانی، پریوش، مساعدی، ابوالفضل، فاخریفرد، احمد، «پیش بینی بارندگی با استفاده از نظریه موجک»، نشریه آب و خاک ، جلد25، شماره 5، آذر- دی 1390.
[28]B. Li and X. Chen, “Wavelet-based numerical analysis: A review and classification,” Finite Elements in Analysis and Design, vol. 81, pp. 14–31, April 2014.
[29]C. Guan, P. B. Luh, L.D. Michel, Y. Wang, and P. B. Friedland, “Very short-term load forecasting: wavelet neural networks with data pre-filtering,” IEEE Transactions on Power Systems, vol. 28, no. I, pp. 30-41, February 2013.
[30]N. Amjady and F. Keynia, “Short-term load forecasting of power systems by combination of wavelet transform and neuro-evolutionary algorithm,” Energy, vol. 34, no. 1, pp. 46-57, January 2009.
[31]P. Bunnoon, K. Chalermyanont and C. Limsakul, “Mid-term load forecasting: level suitably of wavelet and neural network based on factor selection,” Energy Procedia, vol. 14, pp. 438-444, 2012.
[32]جلیلوند، ابوالفضل، حسینی، سید هادی، صادقی، فرشته، «پیش بینی بلندمدت بار الکتریکی پستهای فوق توزیع استان زنجان با استفاده از الگوریتم بهینه سازی تراکم ذرات»، بیست و هشتمین کنفرانس بین المللی برق، پژوهشگاه نیرو، 11 الی 13 آبان 1392.
[33]N. Dongxiao, Z. Yunyun and L. Jinpeng, “The application of time series seasonal multiplicative model and GARCH error amending model on forecasting the monthly peak load,” International Forum on Computer Science-Technology and Applications, Chongqing, China, December 2009.
)