Hybridizing DEMD and quantum PSO with SVR in electric load forecasting

Energies

Volumn 9, Issue 3, 2016, Pages

  Peng, Li Ling ^a   Fan, Guo Feng ^a   Huang, Min Liang ^b   Hong, Wei Chiang ^b,c  

^a PINGDINGSHAN UNIVERSITY   (China)

^b ORIENTAL INSTITUTE OF TECHNOLOGY   (Taiwan)

^c NANJING TECH UNIVERSITY   (China)

Author keywords

Auto regression; Differential empirical mode decomposition; Electric load forecasting; Particle swarm optimization; Quantum theory; Support vector regression

Indexed keywords

ELECTRIC LOAD DISPATCHING; ELECTRIC POWER PLANT LOADS; ELECTRIC UTILITIES; ENERGY TRANSFER; FORECASTING; PARTICLE SWARM OPTIMIZATION (PSO); QUANTUM THEORY; REGRESSION ANALYSIS; SCHEDULING; SIGNAL PROCESSING;

AUTO REGRESSION; EMPIRICAL MODE DECOMPOSITION; FORECASTING PERFORMANCE; INDEPENDENT SYSTEM OPERATORS; INTRINSIC MODE FUNCTIONS; QUANTUM PARTICLE SWARM OPTIMIZATION ALGORITHM; SEARCHING PERFORMANCE; SUPPORT VECTOR REGRESSION (SVR);

ELECTRIC LOAD FORECASTING;

EID: 84962856844     PISSN: None     EISSN: 19961073     Source Type: Journal    
DOI: 10.3390/en9030221     Document Type: Article

References (48)

1. Bernard, J.T.; Bolduc, D.; Yameogo, N.D.; Rahman, S. A pseudo-panel data model of household electricity demand. Resour. Energy Econ. 2010, 33, 315-325.
2. Bianco, V.; Manca, O.; Nardini, S. Electricity consumption forecasting in Italy using linear regression models. Energy 2009, 34, 1413-1421.
3. Zhou, P.; Ang, B.W.; Poh, K.L. A trigonometric grey prediction approach to forecasting electricity demand. Energy 2006, 31, 2839-2847.
4. Afshar, K.; Bigdeli, N. Data analysis and short term load forecasting in Iran electricity market using singular spectral analysis (SSA). Energy 2011, 36, 2620-2627.
5. Kumar, U.; Jain, V.K. Time series models (Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India. Energy 2010, 35, 1709-1716.
6. Bianco, V.; Scarpa, F.; Tagliafico, L.A. Long term outlook of primary energy consumption of the Italian thermoelectric sector: Impact of fuel and carbon prices. Energy 2015, 87, 153-164.
7. Topalli, A.K.; Erkmen, I. A hybrid learning for neural networks applied to short term load forecasting. Neurocomputing 2003, 51, 495-500.
8. Kandil, N.; Wamkeue, R.; Saad, M.; Georges, S. An efficient approach for short term load forecasting using artificial neural networks. Int. J. Electr. Power Energy Syst. 2006, 28, 525-530.
9. Beccali, M.; Cellura, M.; Brano, V.L.; Marvuglia, A. Forecasting daily urban electric load profiles using artificial neural networks. Energy Convers. Manag. 2004, 45, 2879-2900.
10. Topalli, A.K.; Cellura, M.; Erkmen, I.; Topalli, I. Intelligent short-term load forecasting in Turkey. Electr. Power Energy Syst. 2006, 28, 437-447.
11. Pai, P.F.; Hong, W.C. Forecasting regional electricity load based on recurrent support vector machines with genetic algorithms. Electr. Power Syst. Res. 2005, 74, 417-425.
12. Hong, W.C. Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artificial bee colony algorithm. Energy 2011, 36, 5568-5578.
13. Yesilbudak, M.; Sagiroglu, S.; Colak, I. A new approach to very short term wind speed prediction using k-nearest neighbor classification. Energy Convers Manag. 2013, 69, 77-86.
14. Peng, H.; Liu, F.; Yang, X. A hybrid strategy of short term wind power prediction. Renew. Energy 2013, 50, 590-595.
15. An, X.; Jiang, D.; Liu, C.; Zhao, M.Wind farm power prediction based on wavelet decomposition and chaotic time series. Expert Syst Appl. 2011, 38, 11280-11285.
16. Lei, Y.; Lin, J.; He, Z.; Zuo, M.J. A review on empirical mode decomposition in fault diagnosis of rotating machinery. Mech. Syst. Signal. Process. 2013, 35, 108-126.
17. Wong, P.; Xu, Q.; Vong, C.; Wong, H. Rate-dependent hysteresis modeling and control of a piezostage using online support vector machine and relevance vector machine. IEEE Trans. Ind. Electron. 2012, 59, 1988-2001.
18. Wang, Z.; Liu, L. Sensitivity prediction of sensor based on relevance vector machine. J. Inf. Comput. Sci. 2012, 9, 2589-2597.
19. Hong, W.C. Intelligent Energy Demand Forecasting; Springer: London, UK, 2013.
20. Huang, B.; Kunoth, A. An optimization based empirical mode decomposition scheme. J. Comput. Appl. Math. 2013, 240, 174-183.
21. Fan, G.; Qing, S.;Wang, H.; Shi, Z.; Hong, W.C.; Dai, L. Study on apparent kinetic prediction model of the smelting reduction based on the time series. Math. Probl. Eng. 2012, 2012.
22. Bhusana, P.; Chris, T. Improving prediction of exchange rates using Differential EMD. Expert Syst. Appl. 2013, 40, 377-384.
23. An, X.; Jiang, D.; Zhao, M.; Liu, C. Short-term prediction of wind power using EMD and chaotic theory. Commun. Nonlinear Sci. Numer. Simul. 2012, 17, 1036-1042.
24. Huang, N.E.; Shen, Z. A new View of nonliner water waves: The Hilbert spectrum. Rev. Fluid Mech. 1999, 31, 417-457.
25. Fan, G.; Peng, L.-L.; Hong, W.-C.; Sun, F. Electric load forecasting by the SVR model with differential empirical mode decomposition and auto regression. Neurocomputing 2016, 173, 958-970.
26. Wu, P.F.; Gao, L.Q.; Zou, D.X.; Li, S. An improved particle swarm optimization algorithm for reliability problems. ISA. Trans. 2011, 50, 71-81.
27. Alfi, A. Particle swarm optimization algorithm with dynamic inertia weight for online parameter identification applied to Lorenz chaotic system. Int. J. Innov. Comput. Inf. 2012, 8, 1191-1203.
28. Alfi, A.; Modares, H. System identification and control using adaptive particle swarm optimization. Appl. Math. Model. 2011, 35, 1210-1221.
29. Alfi, A. PSO with adaptive mutation and inertia weight and its application in parameter estimation of dynamic systems. Acta Autom. Sin. 2011, 37, 541-549.
30. Wu, X.J.; Huang, Q.; Zhu, X.J. Thermal modeling of a solid oxide fuel cell and micro gas turbine hybrid power system based on modified LS-SVM. Int. J. Hydrogen Energy. 2011, 36, 885-892.
31. Liu, X.Y.; Shao, C.; Ma, H.F.; Liu, R.X. Optimal earth pressure balance control for shield tunneling based on LS-SVM and PSO. Autom. Constr. 2011, 29, 321-327.
32. Sun, J.; Feng, B.; Xu, W.B. Particle swarm optimization with particles having quantum behavior. In Proceedings of the IEEE Proceedings of Congress on Evolutionary Computation, New Orleans, LA, USA, 19-23 June 2004; pp. 325-331.
33. Sun, J.; Xu, W.B.; Feng, B. A global search strategy of quantum-behaved particle swarm optimization. In Proceedings of the Cybernetics and Intelligent Systems Proceedings of the 2004 IEEE Conference, Singapore, 4-9 May 2004; pp. 111-116.
34. Vapnik, V. The Nature of Statistical Learning Theory; Springer-Verlag: New York, NY, USA, 1995.
35. Bergh, F.V.D. An Analysis of Particle Swarm Optimizers. Ph.D. Thesis, University of Pretoria, Pretoria, South Africa, 2001.
36. Sun, J.; Fang, W.; Wu, X.J.; Palade, V.; Xu, W.B. Quantum-behaved particle swarm optimization: Analysis of individual particle behavior and parameter selection. Evol. Comput. 2012, 20, 349-393.
37. Sun, J.; Fang, W.; Palade, V.;Wu, X.J.; Xu, W.B. Quantum-behaved particle swarm optimization with gaussian distributed local attractor point. Appl. Math. Comput. 2011, 218, 3763-3775.
38. Sun, T.H. Applying particle swarm optimization algorithm to roundness measurement. Expert Syst. Appl. 2009, 36, 3428-3438.
39. Xi, M.; Sun, J.; Xu, W. An improved quantum-behaved particle swarm optimization algorithm with weighted mean best position. Appl. Math. Comput. 2008, 205, 751-759.
40. Coelho, L.S. An efficient particle swarm approach for mixed-integer programming in reliability-Redundancy optimization applications. Reliab. Eng. Syst. Saf. 2009, 94, 830-837.
41. Fang, Y.; Zhao, Y.D.; Ke, M.; Zhao, X.; Iu, H.H.;Wong, K.P. Quantum inspired particle swarm optimization for power system operations considering wind power uncertainty and carbon tax in Australia. IEEE Trans. Ind. Inform. 2012, 8, 880-888.
42. Ko, C.N.; Jau, Y.M.; Jeng, J.T. Parameter estimation of chaotic dynamical systems using quantum-behaved particle swarm optimization based on hybrid evolution. J. Inf. Sci. Eng. 2015, 31, 675-689.
43. Ko, C.N.; Chang, Y.P.; Wu, C.J. An orthogonal-array-based particle swarm optimizer with nonlinear time-varying evolution. Appl. Math. Comput. 2007, 191, 272-279.
44. Ratnaweera, A.; Halgamuge, S.K.; Watson, H.C. Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Transactions on Evol. Comput. 2004, 8, 240-255.
45. Gao, J. Asymptotic properties of some estimators for partly linear stationary autoregressive models. Commun. Statist. Theory Meth. 1995, 24, 2011-2026.
46. Taylor, J.W. Short-term load forecasting with exponentially weighted methods. IEEE Trans. Power Syst. 2012, 27, 458-464.
47. Che, J.; Wang, J.; Wang, G. An adaptive fuzzy combination model based on self-organizing map and support vector regression for electric load forecasting. Energy 2012, 37, 657-664.
48. Lowry, R. Concepts & Applications of Inferential Statistics; Vassar College: New York, NY, USA, 2011.