International Journal of Scientific & Technology Research

IJSTR@Facebook IJSTR@Twitter IJSTR@Linkedin
Home About Us Scope Editorial Board Blog/Latest News Contact Us

IJSTR >> Volume 4 - Issue 5, May 2015 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Short Term Load Forecasting Using A Hybrid Model Based On Support Vector Regression

[Full Text]



Aliasghar Baziar, Abdollah Kavousi-Fard



Index Terms: Support Vector Regression (SVM), Krill Herd (KH) Algorithm, Short Term Load Forecasting (STLF).



Abstract: This paper proposes a new hybrid method based on support vector regression (SVR) to predict the load value of power systems accurately. The proposed method will use the SVR to overcome some deficiencies such as overfitting and complicated structure that exist in the neural network. In order to find the optimal values of the parameters, krill herd (KH) algorithm is used as the optimizer. The KH algorithm can explore the problem search space for reaching the best structure for the SVR when training. In order to check the performance and accuracy of the proposed hybrid method, the empirical load data from the Fars Regional Company are used as the test data. The simulations show the high reliable and accuracy of the proposed method.



[1] Fan, S., Chen L. and Lee, W.J. (2009), ‘Short-Term Load Forecasting Using Comprehensive Combination Based on Multimeteorological Information’, IEEE Trans. on Indus. Appl. 45(4), 1460-1466.

[2] Amjady, N. (2007), ‘Short-Term Bus Load Forecasting of Power Systems by a New Hybrid Method’, IEEE Trans. on Power Syst. 22(1) (2007) 331-341.

[3] Khosravi, A., Nahavandi, S. and Creighton, D. (2010), ‘Construction of Optimal Prediction Intervals for Load Forecasting Problems’, IEEE Trans. on Power Syst, 25(3), 1493-1503.

[4] Huang, S.-J. and Shih, K.-R. (2003), ‘Short-term load forecasting via ARMA model identification including non-Gaussian process considerations’, IEEE Trans. Power Syst. 18(2), 673–679.

[5] Papalexopoulos, A. and Hesterberg, T., (1990) ‘A regression-based approach to short-term system load forecasting’, IEEE Trans. Power Syst. 5(4), 1535–1547.

[6] Wu, H. and Lu, C. (2003), ‘A data mining approach for spatial modeling in small area load forecast’, IEEE Trans. Power Syst., 17(2), 516–521.

[7] Al-Hamadi, H. M. and Soliman, S. A. (2004), ‘Short-term electric load forecasting based on Kalman filtering algorithm with moving window weather and load model’, Elect. Power Syst. Res., 68(1), 47–59.

[8] Senjyu, T., Sakihara, H., Tamaki, Y., and Uezato, K. (2000), ‘Next day peak load forecasting using neural network with adaptive learning algorithm based on similarity’, Elect. Mach. Power Syst., 28(7), 613–624.

[9] Box, G. E. P. and Jenkins, G., ‘Time Series Analysis, Forecasting and Control’, San Francisco, CA: Holden-Day, 1970.

[10] Alamaniotis, M., Ikonomopoulos, A. (2012), ‘L.H. Tsoukalas, Evolutionary Multiobjective Optimization of Kernel-Based Very-Short-Term Load Forecasting’, IEEE Trans. Power Syst., 27(3), 1477 - 1484

[11] Kim, K.-H., Park, J.-K., Hwang, K.-J., and Kim, S.-H. (1995), ‘Implementation of hybrid short-term load forecasting system using artificial neural networks and fuzzy expert systems’, IEEE Trans. Power Syst., 10(3), 1534–1539.

[12] Yun, Z., Quan, Z., Caixin, S., Shaolan, L., Yuming, L., and Yang, S. (2008), ‘RBF Neural Network and ANFIS-Based Short-Term Load Forecasting Approach in Real-Time Price Environment’, IEEE Trans. on Power Syst, 23(3), 853-858.

[13] Liao, G.-C. and Tsao, T.-P. (2006), ‘Application of a fuzzy neural network combined with a chaos genetic algorithm and simulated annealing to short-term load forecasting’, IEEE Trans. Evol. Comput., 10(3), 330–340.

[14] Ying, L.-C. and Pan, M.-C. (2008), ‘Using adaptive network based fuzzy inference system to forecast regional electricity loads’, Energy Convers. Manage., 49(2), 205–211.

[15] Li, Q., Meng, Q., Cai, J., Yoshino, H., Mochida, A. (2009), ‘Applying support vector machine to predict hourly cooling load in the building’, Applied Energy 86, 2249–2256

[16] Wang, W., & Men, C. Q. (2008),’ Online prediction model based on support vector machine’, Neurocomputing, 71, 550–558.

[17] Chapelle, O., & Vapnik, V. (2001),’ Choosing multiple parameters for support vectors machines’, New York: AT&T Research Labs.

[18] Hong, W.C. (2009), ‘Chaotic particle swarm optimization algorithm in a support vector regression electric load forecasting model’, Energy Conversion and Management 50, 105–117

[19] Pai, P.F., Lin C.S. (2005), ‘A hybrid ARIMA and support vector machines model in stock price forecasting’, Omega, 33(6), 497–505.

[20] Pai P.F, Hong W.C. (2006), ‘Software reliability forecasting by support vector machines with simulated annealing algorithms’, J Syst Software, 79(6), 747–55.

[21] Mohandes, M.A, Halawani, T.O, Rehman, S., Hussain, A.A. (2004), ‘Support vector machines for wind speed prediction’, Renew Energy, 29(6), 939–47.

[22] Hong W.C, Pai P.F. (2007), ‘Potential assessment of the support vector regression technique in rainfall forecasting’, Water Resour Manage, 21(2), 495–513.

[23] Pai P.F, Hong W.C. (2005),’ Forecasting regional electric load based on recurrent support vector machines with genetic algorithms’, Electric Pow Syst Res,74(3), 417–25

[24] M. Rostami, A. Kavousi-Fard, and T. Niknam, Expected Cost Minimization of Smart Grids with Plug-in Hybrid Electric Vehicles Using Optimal Distribution Feeder Reconfiguration, IEEE Trans. on Industrial Informatics (2015) 11(2) 388 – 397

[25] A. Kavousi-Fard, A.Abunasri, A. Zare, R. Hoseinzadeh, Impact of Plug-in Hybrid Electric Vehicles Charging Demand on the Optimal Energy Management of Renewable Micro-Grids,78 Energy, 2014, 904-915.

[26] A. Kavousi-Fard, A. Khosravi, S. Nahavadi, A New Fuzzy Based Combined Prediction Interval for Wind Power Forecasting, IEEE Trans. on Power System (2015)

[27] A. Kavousi-Fard, T. Niknam, Optimal Distribution Feeder Reconfiguration for Reliability Improvement Considering Uncertainty, IEEE Trans. On Power Delivery, 29(3) (2014) 1344 - 1353

[28] A. Kavousi-Fard, T. Niknam, M.R. Akbari-Zadeh, B. Dehghan, Stochastic framework for reliability enhancement using optimal feeder recon figuration, Journal of Systems Engineering and Electronics Vol. 25, No. 5, August 2014, pp.901–910

[29] A. Kavousi-Fard, T. Niknam, H. Taherpoor, A. Abbasi, Multi-objective Probabilistic Reconfiguration Considering Uncertainty and Multi-Level Load Model, IET SMT, vol 9 (1), 2015, pp.44-55

[30] A. Kavousi-Fard, T. Niknam, M. Khooban, An Intelligent Stochastic Framework to Solve the Reconfiguration Problem from the Reliability view, IET SMT, 8(5), 2014, p. 245 – 259

[31] A. Kavousi-Fard, A. Abbasi and A. Baziar, A novel adaptive modified harmony search algorithm to solve multi-objective environmental/economic dispatch, Journal of Intelligent & Fuzzy Systems, 26(6) (2014), pp. 2817-2823

[32] A. Kavousi-Fard, T. Niknam, Optimal Stochastic Capacitor Placement Problem from the Reliability and Cost Views using Firefly Algorithm, IET SMT, vol. 8(5), pp. 260 – 269, 2014

[33] A. Kavousi-Fard, H. Samet, F. Marzban, A New Hybrid Modified Firefly Algorithm and Support Vector Regression Model for Accurate Short Term Load Forecasting, Expert Systems With Applications, 41(13) (2014) 6047–6056

[34] A. Kavousi-Fard, T. Niknam, M. Golmaryami, Short Term Load Forecasting of Distribution Systems by a New Hybrid Modified FA-Backpropagation Method, Journal of Intelligent and Fuzzy systems, 2014 (26) 517-522

[35] A. Kavousi-Fard, T. Niknam, A. Khosravi, Multi-Objective Probabilistic Distribution Feeder Reconfiguration Considering Wind Power Plants, International Journal of Electrical Power and Energy Systems, 2014 (55) 680-691

[36] A. Kavousi-Fard, A new fuzzy-based feature selection and hybrid TLA–ANN modeling for short-term load forecasting, Journal of Experimental & Theoretical Artificial Intelligence, 25(4) 2013, 543-557

[37] A. Kavousi-Fard, F. Kavousi-Fard, A New Hybrid Correction Method for Short Term Load Forecasting Based on ARIMA, SVR and CSA, Journal of Experimental & Theoretical Artificial Intelligence, 25(4) 2013, 559-574

[38] A. Kavousi-Fard, T. Niknam, Considering uncertainty in the multi-objective stochastic capacitor allocation problem using a novel self adaptive modification approach, Electric Power Systems Research, 103, 2013, 16-27

[39] A. Kavousi-Fard, H. Samet, Multi-objective Performance Management of the Capacitor Allocation Problem in Distributed System Based on Modified HBMO Evolutionary Algorithm, Electric Power and Component systems, 2013 ,41 (13) 1223:1247

[40] A. Kavousifard, H. Samet, Power System Load Prediction Based on MHBMO Algorithm and Neural Network, IEEE Conference on Electrical Engineering (ICEE), 2011, pp. 1-8, Iran

[41] A. Kavousi-Fard, A. Khosravi, S. Nahavandi: A novel fuzzy multi-objective framework to construct optimal prediction intervals for wind power forecast. IEEE Conference, IJCNN 2014: 1015-1019

[42] A. Baziar and A. Kavousi-Fard, An intelligent multi-objective stochastic framework to solve the distribution feeder reconfiguration considering uncertainty, Journal of Intelligent & Fuzzy Systems, 26 (2014) pp. 2215–2227

[43] R. Sedaghati, A. Kavousi-Fard, A hybrid fuzzy-PEM stochastic framework to solve the optimal operation management of distribution feeder reconfiguration considering wind turbines, Journal of Intelligent and Fuzzy Systems 26 (2014) 1711-1721.

[44] A. Baziar, A. Kavousi Fard, Consideration Effect of Uncertainty in the Optimal Energy Management of Renewable Micro-Grids including Storage Devices, Renewable Energy 59 (2013) 158-166, 2013.

[45] A. Kavousifard, H. Samet, Consideration effect of uncertainty in power system reliability indices using radial basis function network and fuzzy logic theory, Neurocomputing, 74(17) (2011) 3420-3427

[46] Fars Electrical Power Company: http://www.frec.co.ir