Hybridization of seasonal chaotic cloud simulated annealing algorithm in a SVR-based load forecasting model

Neurocomputing

Volumn 151, Issue P3, 2015, Pages 1362-1373

  Geng, Jing ^a   Huang, Min Liang ^b   Li, Ming Wei ^a   Hong, Wei Chiang ^b  

^a HARBIN ENGINEERING UNIVERSITY   (China)

^b ORIENTAL INSTITUTE OF TECHNOLOGY   (Taiwan)

Author keywords

Cat mapping; Chaotic cloud simulated annealing algorithm (CCSA); Cloud theory; Electric load forecasting; Seasonal mechanism; Support vector regression (SVR)

Indexed keywords

ECONOMICS; ELECTRIC POWER PLANT LOADS; FORECASTING; MAPPING; SIMULATED ANNEALING; SUPPORT VECTOR REGRESSION;

CAT MAPPINGS; CHAOTIC SIMULATED ANNEALING; CLOUD THEORY; FORECASTING PERFORMANCE; LOAD FORECASTING MODEL; SIMULATED ANNEALING ALGORITHMS; SUPPORT VECTOR REGRESSION (SVR); SUPPORT VECTOR REGRESSION MODELS;

ELECTRIC LOAD FORECASTING;

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; CHAOTIC CLOUD SIMULATED ANNEALING ALGORITHM; FORECASTING; MATHEMATICAL COMPUTING; PROCESS DEVELOPMENT; PROCESS MODEL; SEASONAL VARIATION; SUPPORT VECTOR MACHINE; SYSTEM ANALYSIS;

EID: 84918801543     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2014.10.055     Document Type: Article

References (54)

1. Bunn D.W., Farmer E.D. Comparative models for electrical load forecasting. Int. J. Forecast. 1986, 2:241-242.
2. Hobbs B.F., Chankong V., Loparo K.A., Maratukulam D.J. Analysis of the value for unit commitment of improved load forecasts. IEEE Trans. Power Syst. 1999, 14:1342-1348.
3. Zhao W., Wang J., Lu H. Combining forecasts of electricity consumption in China with time-varying weights updated by a high-order Markov chain model. Omega 2014, 45:80-91.
4. Fan S., Chen L. Short-term load forecasting based on an adaptive hybrid method. IEEE Trans. Power Syst. 2006, 21:392-401.
5. Hippert HS., Pedreira CE., Souza RC. Neural networks for short-term load forecasting: a review and evaluation. IEEE Trans. Power Syst. 2001, 16:44-55.
6. Huang SJ., Shih KR. Short-term load forecasting via ARMA model identification including non-Gaussian process considerations. IEEE Trans. Power Syst. 2003, 18:673-679.
7. Park JH., Park YM., Lee KY. Composite modeling for adaptive short-term load forecasting. IEEE Trans. Power Syst. 1991, 6(2):450-457.
8. Douglas AP., Breipohl AM., Lee FN., Adapa R. The impact of temperature forecast uncertainty on Bayesian load forecasting. IEEE Trans. Power Syst. 1998, 13:1507-1513.
9. Brown R.G. Introduction to Random Signal Analysis and Kalman Filtering 1983, John Wiley & Sons, Inc, New York, NY.
10. Charytoniuk W., Chen M., Olinda P. Nonparametric regression based short-term load forecasting. IEEE Trans. Power Syst. 1998, 13:725-730.
11. Hsu C.C., Chen C.Y. Regional load forecasting in Taiwan-application of artificial neural networks. Energy Convers. Manage. 2003, 44(12):1941-1949.
12. Ekonomou L. Greek long-term energy consumption prediction using artificial neural networks. Energy 2010, 35(2):512-517.
13. Yun Z., Quan Z., Caixin S., Shaolan L., Yuming L., Yang S. RBF neural network and anfis-based short-term load forecasting approach in real-time price environment. IEEE Trans. Power Syst. 2008, 23:853-858.
14. Liao G.C., Tsao T.P. Application of a fuzzy neural network combined with a chaos genetic algorithm and simulated annealing to short term load forecasting. IEEE Trans. Evol. Comput. 2006, 10:330-340.
15. Hanmandlu M., Chauhan B.K. Load forecasting using hybrid models. IEEE Trans. Power Syst. 2011, 26:20-29.
16. Suykens J.A.K., Vandewalle J., De Moor B. Optimal control by least squares support vector machines. Neural Networks 2001, 14:23-35.
17. Sankar R., Sapankevych N.I. Time series prediction using support vector machines: a survey. IEEE Commun. Mag. 2009, 4:24-38.
18. Hahn H., Meyer-Nieberg S., Pickl S. Electric load forecasting methods: tools for decision making. Eur. J. Oper. Res. 2009, 199:902-907.
19. Drucker H., Burges CCJ., Kaufman L., Smola A., Vapnik V. Support vector regression machines. Adv. Neural Inform. Process. Syst. 1997, 9:155-161.
20. Hung W.M., Hong W.C. Application of SVR with improved ant colony optimization algorithms in exchange rate forecasting. Control Cybern. 2009, 38:863-891.
21. Hong W.C. Traffic flow forecasting by seasonal SVR with chaotic simulated annealing algorithm. Neurocomputing 2011, 74:2096-2107.
22. Pai P.F., Hong W.C. A recurrent support vector regression model in rainfall forecasting. Hydrol. Process. 2007, 21:819-827.
23. Hong W.C. Chaotic particle swarm optimization algorithm in a support vector regression electric load forecasting model. Energy Convers. Manage. 2009, 50:105-117.
24. Hong WC. Application of chaotic ant swarm optimization in electric load forecasting. Energy Policy 2010, 38:5830-5839.
25. Hong W.C. Electric load forecasting by seasonal recurrent SVR with chaotic artificial bee colony algorithm. Energy 2011, 36:5568-5578.
26. Chen G., Mao Y., Chui C.K. A symmetric image encryption scheme based on 3D chaotic cat maps. Chaos Solitons Fractals 2004, 21:749-761.
27. Lv P., Yuan L., Zhang J. Cloud theory-based simulated annealing algorithm and application. Eng. Appl. Artif. Intell. 2009, 22:742-749.
28. Li D., Meng H., Shi X. Membership clouds and membership cloud generators. J. Comput. Res. Dev. 1995, 32:15-20.
29. Li D., Cheung D., Shi XM., Ng V. Uncertainty reasoning based on cloud models in controllers. Comput. Math. Appl. 1998, 35:99-123.
30. Zhang F., Fan Y., Shen C., Li D. Intelligent control based on membership cloud generators. Acta Aeronaut. Astronaut. 1999, 20:89-92.
31. Wang S., Li D., Shi W., Li D., Wang Z. Cloud model-based spatial data mining. Geogr. Inf. Sci. 2003, 9:60-70.
32. Haijun W, Yu D. Spatial clustering method based on cloud model, in: Proceedings of the Fourth: Proceedings of International Conference on Fuzzy Systems and Knowledge Discovery 2007;7:272-276.
33. Zhu Y., Dai C., Chen W. Adaptive probabilities of crossover and mutation in genetic algorithms based on cloud generators. J. Comput. Inf. Syst. 2005, 1:67l-678.
34. Dagum E.B. Modelling, forecasting and seasonally adjusting economic time series with the X-11 ARIMA method. J. R. Stat. Soc. Ser. D: Stat. 1987, 27:203-216.
35. Kenny P.B., Durbin J. Local trend estimation and seasonal adjustment of economic and social time series. J. R. Stat. Soc. Ser. A: Gen. 1982, 145:1-41.
36. Wang J., Zhu W., Zhang W., Sun D. A trend fixed on firstly and seasonal adjustment model combined with the ε-SVR for short-term forecasting of electricity demand. Energy Policy 2009, 37:4901-4909.
37. Xiao Z., Ye SJ., Zhong B., Sun CX. BP neural network with rough set for short term load forecasting. Expert Syst. Appl. 2009, 36:273-279.
38. Vapnik V. The Nature of Statistic Learning Theory 1995, Springer-Verlag, New York, NY.
39. Amari S., Wu S. Improving support vector machine classifiers by modifying kernel functions. Neural Networks 1999, 12:783-789.
40. Di K., Li D., Li D. Cloud theory and its applications in spatial data mining knowledge discovery. J. Image Graph 1999, 4:930-935.
41. Li D., Du Y. Artificial Intelligence with Uncertainty 2005, Chapman & Hall, New York, NY.
42. Wang L., Zheng DZ., Lin QS. Survey on chaotic optimization methods. Comput. Technol. Autom. 2001, 20:1-5.
43. Chen L., Aihara K. Chaotic simulated annealing by a neural network model with transient chaos. Neural Networks 1995, 8:915-930.
44. Aarts E.H.L., J.H.M Korst. Simulated Annealing and Boltzmann Machines 1989, Wiley, New York, NY.
45. H.R Anderson, J.P. McGeehan, Optimizing microcell base station locations using simulated annealing techniques, in: Proceedings of 44th IEEE Vehicular Technology Conference, 1994;2:858-862.
46. Chen G., Dong X. From Chaos to Order: Methodologies, Perspectives and Applications 1998, World Scientific, Singapore.
47. http://mathworld.wolfram.com/ArnoldsCatMap.html.
48. Kirkpatrick S., Gelatt C.D., Vecchi M.P. Optimization by simulated annealing. Science 1983, 220:671-680.
49. Van Laarhoven P.J.M., Aarts E.H.L. Simulated Annealing: Theory and Applications 1987, Kluwer Academic Publishers, Dordrecht.
50. Martens K., Chang Y.C., Taylor S. A comparison of seasonal adjustment methods when forecasting intraday volatility. J. Finance Res. 2002, 25:283-299.
51. Taylor S.J., Xu X. The incremental volatility information in one million foreign exchange quotations. J. Empir. Finance 1997, 4:317-340.
52. Andersen T.G., Bollerslev T. D.M-dollar volatility: intraday activity patterns, macroeconomic announcements and longer run dependencies. J. Finance 1998, 53:219-265.
53. Deo R., Hurvich C., Lu Y. Forecasting realized volatility using a long- memory stochastic volatility model: estimation, prediction and seasonal adjustment. J. Econ. 2006, 131:29-58.
54. http://www.nyiso.com/public/markets_operations/index.jsp.