A Hybrid One-step-ahead Time Series Model Based on GA-SVR and EMD for Forecasting Electricity Loads

Mu-Chiun Shiu¹, Liang-Ying Wei This email address is being protected from spambots. You need JavaScript enabled to view it.², Jing-Wei Liu³, Deng-Yang Huang², Chien-Chih Tu² and Kuo-Hsiung Liao²

¹Department of Information Management, National Yunlin University of Science and Technology, Yunlin, Taiwan 640, R.O.C.
²Department of Information Management, Yuanpei University of Medical Technology, Hsinchu, Taiwan 30015, R.O.C.
³Department of Sport Information and Communication, National Taiwan University of Sport, Taichung, Taiwan 404, R.O.C.

REFERENCES

1. [1] Cheng, C. H. and Wei, L. Y., “One Step-ahead ANFIS TimeSeries Model for Forecasting ElectricityLoads,” Optimization and Engineering, Vol. 11, pp. 303317 (2010). doi: 10.1007/s11081-009-9091-5
2. [2] Nowak, M.P.,Schultz,R.andWestphalen,A.,“AStochastic Integer Programming Model for Incorporating Day-ahead Trading of into Hydro-thermal Unit Commitment,”Optim Eng,Vol.6,pp.163176(2005).doi: 10.1007/s11081-005-6794-0
3. [3] Rnberg, R. N. and Misch, W. R., “A Two-stage Planning Model for Power Scheduling in a Hydro-thermal System under Uncertainty,” Optim Eng, Vol. 3, pp. 355378 (2002).
4. [4] Bunn, D. W. and Farmer, E. D., Comparative Models for Electrical Load Forecasting, New York: John Wiley and Sons (1985).
5. [5] Yang, Y., Chen, Y., Wang, Y., Li, C. and Li, L., “Modelling a Combined Method Based on ANFIS and Neural Network Improved by DE Algorithm: a Case Study for Short-term Electricity Demand Forecasting,” Applied Soft Computing, Vol. 49, pp. 663675 (2016). doi: 10.1016/j.asoc.2016.07.053
6. [6] Koprinska, I., Rana, M. and Agelidis, V. G., “Correlation and Instance Based Feature Selection for Electricity Load Forecasting,” Knowledge-Based Systems, Vol. 82, pp. 2940 (2015). doi: 10.1016/j.knosys. 2015.02.017
7. [7] Friedrich, L., Armstrong, P. and Afshari, A., “Midterm Forecasting of Urban Electricity Load to Isolate Air-conditioning Impact,” Energy and Buildings, Vol 80, pp. 7280 (2014). doi: 10.1016/j.enbuild.2014.05. 011
8. [8] Huang, S. J. and Shih, K. R., “Short-term Load Forecasting via ARMA Model Identification Including Non-Gaussian Process Considerations,” IEEE Trans Power Syst1, Vol. 18, No. 2, pp. 673679 (2003). doi: 10.1109/TPWRS.2003.811010
9. [9] Haida, T. and Muto, S., “Regression Based Peak Load Forecasting Using a Transformation Technique,” IEEE Trans Power Syst, Vol. 9, pp. 17881794 (1994). doi: 10.1109/59.331433
10. [10] Pino, R., Parreno, J., Gomez, A. and Priore, P., “Forecasting Next-day Price of Electricity in the Spanish Energy Market Using Artificial Neural Networks,” Engineering Applications of Artificial Intelligence, Vol. 21, pp. 5362 (2008). doi: 10.1016/j.engappai. 2007.02.001
11. [11] Fan,S.,Mao,C.andChen,L.,“PeakLoadForecasting Using the Self Organizing Map,” Advances in Neural Network, ISNN 2005. Springer, Part III, pp. 640647 (2005). doi: 10.1007/11427469_102
12. [12] Hippert, H. S., Pedreira, C. E. and Castro, R., “Neural Networks for Short-term Load Forecasting: a Review and Evaluation,” IEEE Trans Power Syst, Vol. 16, No. 1, pp. 4455 (2001). doi: 10.1109/59.910780
13. [13] Hsu, C. C. and Chen, C. Y., “Regional Load Forecasting in Taiwan- Applications of Artificial Neural Networks,” Energ Convers Manage, Vol. 44, No. 12, pp. 19411949 (2003). doi: 10.1016/S0196-8904(02) 00225-X
14. [14] Pai, P. F., “Hybrid Ellipsoidal Fuzzy Systems in Forecasting Regional Electricity Loads,” Energ Convers Manage, Vol. 47, No. 1516, pp. 22832289 (2006). doi: 10.1016/ j.enconman.2005.11.017
15. [15] Vapnik, V., The Nature of Statistical Learning Theory, New York: Springer-Verlag (1995). doi: 10.1007/ 978-1-4757-2440-0_2
16. [16] Pai, P. F. and Hong, W. C., “Forecasting Regional Electricity Load Based on Recurrent Support Vector Machines with Genetic Algorithms,” Electric Power Syst Res, Vol. 74, No. 3, pp. 417425 (2005). doi: 10.1016/ j.epsr.2005.01.006
17. [17] Ertugrul, Ö. F., “Forecasting Electricity Load by a Novel Recurrent Extreme Learning Machines Approach,” Electrical Power and Energy Systems, No. 78, pp. 429435 (2016). doi: 10.1016/j.ijepes.2015.12.006
18. [18] Ghasemi, A., Shayeghi, H., Moradzadeh, M. and Nooshyar, M., “A Novel Hybrid Algorithm for ElectricityPrice and Load Forecasting in SmartGrids with Demand-side Management,” Applied Energy, Vol. 177, pp. 4059 (2016). doi: 10.1016/j.apenergy.2016.05. 083
19. [19] Hassan, S., Khosravi, A., Jaafar, J. and Khanesar, M. A., “A Systematic Design of Interval Type-2 Fuzzy Logic System Using Extreme Learning Machine for Electricity Load Demand Forecasting,” International Journal of Electrical Power & Energy Systems, Vol. 82, pp. 110 (2016). doi: 10.1016/j.ijepes.2016.03.001
20. [20] Takeda, H., Tamura, Y. and Sato, S., “Using the Ensemble Kalman Filter for ElectricityLoad Forecasting and Analysis,” Energy, Vol. 104, pp. 184198 (2016). doi: 10.1016/j.energy.2016.03.070
21. [21] Clements, A. E. and Hurn, A. S., “Forecasting Dayahead Electricity Load Using a Multiple Equation Time Series Approach,” European Journal of Operational Research, Vol. 251, No. 2, pp. 522530 (2016). doi: 10.1016/j.ejor.2015.12.030
22. [22] Niu, D., Wang, Y. and Wu, D. D., “Power Load Forecasting Using Support Vector Machine and Ant Colony Optimization,” Expert Systems with Applications, Vol. 37, No. 3, pp. 25312539 (2010). doi: 10.1016/ j.eswa.2009.08.019
23. [23] Box, G. and Jenkins, G., Time Series Analysis: Forecasting and Control, Palgrave Macmillan, London, pp. 161215 (1976). doi: 10.1057/9781137291264_6
24. [24] Benaouda,D.andMurtagh,F.,“ElectricityLoadForecast Using Neural Network Trained from Wavelettransformed Data,” IEEE International Conference on Engineering of Intelligent Systems (2006). doi: 10. 1109/ICEIS.2006.1703163
25. [25] Enders, W., Applied Econometric Time Series, Wiley (2004). doi: 10.1198/tech.2004.s813
26. [26] Kim, K. J., “Financial Time Series Forecasting Using Support Vector Machines,” Neurocomputing, Vol. 55, No. 12, pp. 307319 (2003). doi: 10.1016/S09252312(03)00372-2
27. [27] Cao, L., “Support Vector Machines Experts for Time Series Forecasting,” Neurocomputing, Vol. 51, pp. 321 339(2003).doi:10.1016/S0925-2312(02)00577-5
28. [28] Chen, W. H. and Shih, J. Y., “AStudy of Taiwan’s Issuer Credit Rating SystemsUsing Support Vector Machines,” Expert Systems with Applications, Vol. 30, No. 3, pp. 427435 (2006). doi: 10.1016/j.eswa.2005. 10.003
29. [29] Drucker, H., Burges, C. J. C., Kaufman, L., Smola, A. and Vapnik, V., Support Vector Regression Machines, Advances in Neural Information Processing Systems, 9 Cambridge MA: MITpress (1997).
30. [30] Huang, W., Nakamori, Y. and Wang, S. Y., “Forecasting Stock Market Movement Direction with Support Vector Machine,” Computers and Operations Research, Vol. 32, No. 10, pp. 25132522 (2005). doi: 10.1016/ j.cor.2004.03.016
    
    
31. [31] Pai, P. F. and Lin, C. S., “A Hybrid ARIMA and Support Vector Machines Model in Stock Price Forecasting,” Omega, Vol. 33, No. 6, pp. 497505 (2005). doi: 10. 1016/j.omega.2004.07.024
32. [32] Wang,J.,Houb, R.,Wang,C.andShen,L.,“Improved v -Support Vector Regression Model Based on Variable Selection and Brain Storm Optimization for Stock Price Forecasting,” Applied Soft Computing, Vol. 49, pp. 164–178 (2016). doi: 10.1016/j.asoc.2016.07.024
33. [33] Wei, L. Y., “A Hybrid Model Based on ANFIS and Adaptive Expectation Genetic Algorithm to Forecast TAIEX,” Economic Modelling, Vol. 33, pp. 893899 (2013). doi: 10.1016/j.econmod.2013.06.009
34. [34] Holland, J. H., Adaptation in Nature and Artificial Systems, University of Michigan Press (1975). doi: 10.1145/1216504.1216510
35. [35] Chang, F. J., Chiang, Y. M. and Chang, L. C., “Multistep-ahead Neural Networks for Flood Forecasting,” Hydrological Sciences-Journal-des Sciences Hydrologiques, Vol. 52, pp. 114130 (2007). doi: 10.1623/ hysj.52.1.114
36. [36] Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H.H.andZheng, Q.,“The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Nonstationary Time Series Analysis,” in: Proceedings of the Royal Society of London Seriesa-Mathematical Physical and Engineering Sciences,SeriesA,Vol.454, pp. 903995 (1998). doi: 10.1098/rspa.1998.0193
37. [37] Vincent, H. T., Hu, S.-L. J. and Hou, Z., “Damage Detection Using Empirical Mode Decomposition Method and a Comparison with Wavelet Analysis,” in: Proceedings of the Second International Workshop on Structural Health Monitoring, Stanford, pp. 891900 (1999).
38. [38] Huang, N. E., Shen, Z. and Long, S. R., “ANew View of Nonlinear Water Waves: the Hilbert Spectrum,” Annu. Rev. Fluid Mech., Vol. 31 pp. 417457 (1999). doi: 10.1146/annurev.fluid.31.1.417
39. [39] Yu, D. J., Cheng, J. S. and Yang, Y., “Application of EMD Method and Hilbert Spectrum to the Fault Diagnosis of Roller Bearings,” Mech. Syst. Signal Process, Vol. 19, No. 2, pp. 259270 (2005). doi: 10.1016/ S0888-3270(03)00099-2
40. [40] Kazema, A., Sharifia, E., Hussainb, F. K., Saberic, M. and Hussaind, O. K., “Support VectorRegression with Chaos-basedFireflyAlgorithmforStockMarketPrice Forecasting,” Applied Soft Computing, Vol. 13, No. 2, pp. 947958 (2013). doi: 10.1016/j.asoc.2012.09.024
41. [41] Allen, F. and Karalainen, R., “Using Genetic Algorithms to Find Technical Trading Rules,” J Financ Econ, Vol. 51, pp. 245271 (1999).
42. [42] Kim, M. J., Min, S. H. and Han, I., “An Evolutionary Approach to the Combination of Multiple Classifiers to PredictaStock PriceIndex,” Expert Syst. Appl, Vol. 31, No. 2, pp. 241247 (2006). doi: 10.1016/j.eswa. 2005.09.020
43. [43] Cheng, C. H., Chen, T. L. and Wei, L. Y., “A Hybrid Model Based on Rough Sets Theory and Genetic AlgorithmsforStockPriceForecasting,”InformSciences, Vol. 180, No. 9, pp. 16101629 (2010). doi: 10.1016/ j.ins.2010.01.014
44. [44] E. Goldberg, D., Genetic Algorithms in Search, Optimization and Mach Learn, Reading, MA: Addison Wesley (1989).
45. [45] Taylor, J. W. and Buizza, R., “Using Weather Ensemble Predictions in Electricity Demand Forecasting,” Int J Forecasting, Vol. 19, pp. 5770 (2003). doi: 10. 1016/S0169-2070(01)00123-6
46. [46] Poland Dataset, Electricityload values of Poland from 1990’s. <http://research.ics.aalto.fi/eiml/datasets.shtml> (accessed 3.01.2017).
47. [47] Yaslan, Y. and Bican, B., “Empirical Mode Decomposition Based Denoising Method with Support Vector Regression for Time Series Prediction: a Case Study for Electricity Load Forecasting,” Measurement, Vol. 103, pp. 5261 (2017). doi: 10.1016/j.measurement. 2017.02.007