Short Term Load Forecasting Using A Hybrid Model Based On Support Vector Regression
[Full Text]
AUTHOR(S)
Aliasghar Baziar, Abdollah KavousiFard
KEYWORDS
Index Terms: Support Vector Regression (SVM), Krill Herd (KH) Algorithm, Short Term Load Forecasting (STLF).
ABSTRACT
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.
REFERENCES
[1] Fan, S., Chen L. and Lee, W.J. (2009), ‘ShortTerm Load Forecasting Using Comprehensive Combination Based on Multimeteorological Information’, IEEE Trans. on Indus. Appl. 45(4), 14601466.
[2] Amjady, N. (2007), ‘ShortTerm Bus Load Forecasting of Power Systems by a New Hybrid Method’, IEEE Trans. on Power Syst. 22(1) (2007) 331341.
[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), 14931503.
[4] Huang, S.J. and Shih, K.R. (2003), ‘Shortterm load forecasting via ARMA model identification including nonGaussian process considerations’, IEEE Trans. Power Syst. 18(2), 673–679.
[5] Papalexopoulos, A. and Hesterberg, T., (1990) ‘A regressionbased approach to shortterm 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] AlHamadi, H. M. and Soliman, S. A. (2004), ‘Shortterm 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: HoldenDay, 1970.
[10] Alamaniotis, M., Ikonomopoulos, A. (2012), ‘L.H. Tsoukalas, Evolutionary Multiobjective Optimization of KernelBased VeryShortTerm 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 shortterm 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 ANFISBased ShortTerm Load Forecasting Approach in RealTime Price Environment’, IEEE Trans. on Power Syst, 23(3), 853858.
[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 shortterm 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. KavousiFard, and T. Niknam, Expected Cost Minimization of Smart Grids with Plugin Hybrid Electric Vehicles Using Optimal Distribution Feeder Reconfiguration, IEEE Trans. on Industrial Informatics (2015) 11(2) 388 – 397
[25] A. KavousiFard, A.Abunasri, A. Zare, R. Hoseinzadeh, Impact of Plugin Hybrid Electric Vehicles Charging Demand on the Optimal Energy Management of Renewable MicroGrids,78 Energy, 2014, 904915.
[26] A. KavousiFard, A. Khosravi, S. Nahavadi, A New Fuzzy Based Combined Prediction Interval for Wind Power Forecasting, IEEE Trans. on Power System (2015)
[27] A. KavousiFard, T. Niknam, Optimal Distribution Feeder Reconfiguration for Reliability Improvement Considering Uncertainty, IEEE Trans. On Power Delivery, 29(3) (2014) 1344  1353
[28] A. KavousiFard, T. Niknam, M.R. AkbariZadeh, 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. KavousiFard, T. Niknam, H. Taherpoor, A. Abbasi, Multiobjective Probabilistic Reconfiguration Considering Uncertainty and MultiLevel Load Model, IET SMT, vol 9 (1), 2015, pp.4455
[30] A. KavousiFard, 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. KavousiFard, A. Abbasi and A. Baziar, A novel adaptive modified harmony search algorithm to solve multiobjective environmental/economic dispatch, Journal of Intelligent & Fuzzy Systems, 26(6) (2014), pp. 28172823
[32] A. KavousiFard, 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. KavousiFard, 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. KavousiFard, T. Niknam, M. Golmaryami, Short Term Load Forecasting of Distribution Systems by a New Hybrid Modified FABackpropagation Method, Journal of Intelligent and Fuzzy systems, 2014 (26) 517522
[35] A. KavousiFard, T. Niknam, A. Khosravi, MultiObjective Probabilistic Distribution Feeder Reconfiguration Considering Wind Power Plants, International Journal of Electrical Power and Energy Systems, 2014 (55) 680691
[36] A. KavousiFard, A new fuzzybased feature selection and hybrid TLA–ANN modeling for shortterm load forecasting, Journal of Experimental & Theoretical Artificial Intelligence, 25(4) 2013, 543557
[37] A. KavousiFard, F. KavousiFard, 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, 559574
[38] A. KavousiFard, T. Niknam, Considering uncertainty in the multiobjective stochastic capacitor allocation problem using a novel self adaptive modification approach, Electric Power Systems Research, 103, 2013, 1627
[39] A. KavousiFard, H. Samet, Multiobjective 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. 18, Iran
[41] A. KavousiFard, A. Khosravi, S. Nahavandi: A novel fuzzy multiobjective framework to construct optimal prediction intervals for wind power forecast. IEEE Conference, IJCNN 2014: 10151019
[42] A. Baziar and A. KavousiFard, An intelligent multiobjective stochastic framework to solve the distribution feeder reconfiguration considering uncertainty, Journal of Intelligent & Fuzzy Systems, 26 (2014) pp. 2215–2227
[43] R. Sedaghati, A. KavousiFard, A hybrid fuzzyPEM stochastic framework to solve the optimal operation management of distribution feeder reconfiguration considering wind turbines, Journal of Intelligent and Fuzzy Systems 26 (2014) 17111721.
[44] A. Baziar, A. Kavousi Fard, Consideration Effect of Uncertainty in the Optimal Energy Management of Renewable MicroGrids including Storage Devices, Renewable Energy 59 (2013) 158166, 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) 34203427
[46] Fars Electrical Power Company: http://www.frec.co.ir
