عنوان مقاله [English]
Estimation of daily load in distribution companies which is performed to present the results to the DMS, is necessary. Daily load forecasting of power systems has traditionally been considered. Because load patterns are influenced by several factors such as climate, economy and society, it is difficult to predict the load exactly. That's why in recent years the use of intelligent algorithms to predict it, is growing. In this project, the short-term load forecasting is performed in a hybrid approach. Due to the different behavior in different days, various methods have been used to predict the load. With studying different methods of load prediction, finally, finally exponential smoothing algorithm was used to predict the exact load in the weekdays.
 N. Amjady, "Short-term hourly load forecasting using time-series modeling with peak load estimation capability", IEEE Trans. on Power Systems, Vol. 16, No.4, Nov. 2001.
 O.A.S. Carpinteriro, A.P. Alves Da Silva, "A hierarchical neural model in short-term load forecasting", Conference Publications,Vol. 6, No. 3, Jul.2000.
 H.G. Huang, R.C. Hwang, J.G. Hsieh, "A new artificial intelligent peak power load forecaster based on non-fixed neural networks", Electrical Power Energy Systems, Vol. 24, Mar. 2002.
 L.M. Saini, M.K. Soni, "Artificial neural networks based peak load forecasting using conjugate gradient methods", IEEE Trans. on Power Systems, Vol. 17, No. 3, Aug. 2002.
 F. Zhang, X. Zhou, "Gray-regression variable weight combination model for load forecasting", Conference, 2008.
 K. Bin Song, S. Kwan Ha, J. Wook Park, "Hybrid load forecasting method with analysis of temperature sensitivities" IEEE Trans. on Power Systems, Vol. 21, No. 2, May. 2006.
 K. Siwek, S. Osowski, "Regularization of neural networks for improved load forecasting in the power system", IEEE Trans., Vol. 149, No. 3, May. 2002.
 X. Changhao, W. Jian, M.M. Karen, "Short, medium and long term load forecasting model and virtual load forecaster based on radial basis function neural networks", Int. J. Electr. Power Energy Systems, Vol. 32, No. 7, Sep. 2010.
 C. Ying, P.B. Luh, G. Che, Z. Yige, L.D. Michel, M.A. Coolbeth, P.B. Friedland, S.J. Rourke, "Short-term load forecasting: similar day-based wavelet neural networks", IEEE Trans. on Power Systems., Vol. 25, No. 1, 2010.
 D.U. Noel, "Forecasting peak system load using a combined timeseries and econometric model", Applied Energy, Vol. 4, No. 3, Jul. 1978.
 A.M. Al-Kandari, S.A. Soliman, M.E. El-Hawary, "Fuzzy shortterm electric load forecasting", Int. J. of Elect. Power Energy Systems., Vol. 26, No. 2, Feb. 2004.
 M.R. AlRashidi, K.M. EL-Naggar, "Long term electric load forecasting based on particle swarm optimization", Applied Energy., Vol. 78, No. 1, Jan. 2010.
 M. Gonzalo, B.G. Georgios, "Robust PCA as bilinear decomposition with Outlier-sparsity regularization", IEEE Trans. Signal Processing, Vol. 60, No.10, Oct. 2012.
 J.T. Tou, R.C. Gonzalez, "Pattern recognition principles", 4th Edition, Massachusetts, Addision-Wesley Publishing Company,1981.
 M.P. Moghaddam, A.A. Yaghooti, "Load estimation in distribution network with limited real-time data", 10th Conference on Electrical Power Distribution Networks, Iran 2005.
 M.P. Moghaddam, A.A. Yaghooti, M.R. Haghifam, V. Joharie Majd, "Load estimation in distribution network with limited real-time data by Inverse Principal Component Analysis", 13th Conference on Electrical Power Distribution Networks, Iran 2008.
 W. Jie, N.M. Karen, "Weighted least squares methods for load estimation in distribution networks", IEEE Trans. on Power Systems, Vol. 18, No. 4, Nov.2003.
 K. Bin Song, S. Kwan Ha, J. Wook Park, "Short-term load forecasting for holidays using fuzzy linear regression method", IEEE Trans. on Power Systems, Vol. 20, No. 1, Feb. 2005.
 D. Hun Hong, S. Lee, H. Young Do, "Fuzzy linear regression analysis for fuzzy input-output data using shape-preserving operations", Fuzzy Sets and Systems, Sep. 2009.