Mid-term interval load forecasting using multi-output support vector regression with a memetic algorithm for feature selection

Energy

Volumn 84, Issue , 2015, Pages 419-431

  Hu, Zhongyi ^a   Bao, Yukun ^a   Chiong, Raymond ^b   Xiong, Tao ^a  

^a HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^b UNIVERSITY OF NEWCASTLE   (Australia)

Author keywords

Feature selection; Firefly algorithm; Interval load forecasting; Memetic algorithms; Multi output support vector regression

Indexed keywords

BIOLUMINESCENCE; ELECTRIC LOAD MANAGEMENT; ELECTRIC POWER PLANT LOADS; ELECTRIC POWER SYSTEM PLANNING; FEATURE EXTRACTION; FORECASTING; GENETIC ALGORITHMS; SUPPORT VECTOR REGRESSION;

COMPREHENSIVE ASSESSMENT; ELECTRICITY DEMANDS; FIREFLY ALGORITHMS; INTERVAL LOAD; MEMETIC ALGORITHMS; MODELING AND FORECAST; MULTI-OUTPUT SUPPORT VECTOR REGRESSIONS; POWER SYSTEM PLANNING AND OPERATION;

ELECTRIC LOAD FORECASTING;

ALGORITHM; DEMAND-SIDE MANAGEMENT; ELECTRICITY GENERATION; ENERGY PLANNING; REGRESSION ANALYSIS; SIMULATION;

AUSTRALIA; NORTH AMERICA;

EID: 84928419830     PISSN: 03605442     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.energy.2015.03.054     Document Type: Article

References (58)

1. Amjady N., Daraeepour A. Midterm demand prediction of electrical power systems using a new hybrid forecast technique. IEEE Trans Power Syst 2011, 26(2):755-767.
2. Abu-Shikhah N., Elkarmi F. Medium-term electric load forecasting using singular value decomposition. Energy 2011, 36(7):4259-4271.
3. Amjady N., Keynia F. Mid-term load forecasting of power systems by a new prediction method. Energy Convers Manage 2008, 49(10):2678-2687.
4. Ghiassi M., Zimbra D.K., Saidane H. Medium term system load forecasting with a dynamic artificial neural network model. Electr Power Syst Res 2006, 76(5):302-316.
5. Saab S., Badr E., Nasr G. Univariate modeling and forecasting of energy consumption: the case of electricity in Lebanon. Energy 2001, 26(1):1-14.
6. Douglas A.P., Breipohl A.M., Lee F.N., Adapa R. The impacts of temperature forecast uncertainty on Bayesian load forecasting. IEEE Trans Power Syst 1998, 13(4):1507-1513.
7. Nagi J., Yap K.S., Nagi F., Tiong S.K., Ahmed S.K. Acomputational intelligence scheme for the prediction of the daily peak load. Appl Soft Comput 2011, 11(8):4773-4788.
8. Goia A., May C., Fusai G. Functional clustering and linear regression for peak load forecasting. Int J Forecast 2010, 26(4):700-711.
9. Amarawickrama H.A., Hunt L.C. Electricity demand for Sri Lanka: a time series analysis. Energy 2008, 33(5):724-739.
10. Mao H.I., Zeng X.J., Leng G., Zhai Y.J., Keane J.A. Short-term and midterm load forecasting using a bilevel optimization model. IEEE Trans Power Syst 2009, 24(2):1080-1090.
11. Tsekouras G.J., Hatziargyriou N.D., Dialynas E.N. An optimized adaptive neural network for annual midterm energy forecasting. IEEE Trans Power Syst 2006, 21(1):385-391.
12. Ghelardoni L., Ghio A., Anguita D. Energy load forecasting using empirical mode decomposition and support vector regression. IEEE Trans Smart Grid 2013, 4(1):549-556.
13. Chen B.J., Chang M.W., Lin C.J. Load forecasting using support vector machines: a study on EUNITE competition 2001. IEEE Trans Power Syst 2004, 19(4):1821-1830.
14. Hahn H., Meyer-Nieberg S., Pickl S. Electric load forecasting methods: tools for decision making. Eur J Oper Res 2009, 199(3):902-907.
15. Hernandez L., Baladron C., Aguiar J.M., Carro B., Sanchez-Esguevillas A.J., Lloret J., et al. Asurvey on electric power demand forecasting: future trends in smart grids, microgrids and smart buildings. IEEE Commun Surv Tut 2014, 16(3):1460-1495.
16. Maia A.L.S., de Carvalho F.A.T. Holt's exponential smoothing and neural network models for forecasting interval-valued time series. Int J Forecast 2011, 27(3):740-759.
17. García-Ascanio C., Maté C. Electric power demand forecasting using interval time series: a comparison between VAR and iMLP. Energy Policy 2010, 38(2):715-725.
18. Vaccaro A., Cañizares C.A., Bhattacharya K. Arange arithmetic-based optimization model for power flow analysis under interval uncertainty. IEEE Trans Power Syst 2013, 28:1179-1186.
19. Pereira L.E.S., da Costa V.M., Rosa A.L.S. Interval arithmetic in current injection power flow analysis. Int J Electr Power Energy Syst 2012, 43:1106-1113.
20. Wu P., Cheng H., Xing J. The interval minimum load cutting problem in the process of transmission network expansion planning considering uncertainty in demand. IEEE Trans Power Syst 2008, 23:1497-1506.
21. Arroyo J., Espínola R., Maté C. Different approaches to forecast interval time series: a comparison in finance. Comput Econ 2011, 37(2):169-191.
22. Cheung Y.-W. An empirical model of daily highs and lows. Int J Finance Econ 2007, 12(1):1-20.
23. Cheung Y.-L., Cheung Y.-W., Wan A.T.K. Ahigh-low model of daily stock price ranges. JForecast 2009, 28(2):103-119.
24. Yang W., Han A., Cai K., Wang S. ACIX model with interval dummy variables and its application in forecasting interval-valued crude oil prices. Proc Comput Sci 2012, 9:1273-1282.
25. Arroyo J, Muñoz San Roque A, Maté C, Sarabia A. Exponential smoothing methods for interval time series. Conference exponential smoothing methods for interval time series. p. 231-240.
26. Roque A.M.S., Maté C., Arroyo J., Sarabia Á. iMLP: applying multi-layer perceptrons to interval-valued data. Neural Process Lett 2007, 25(2):157-169.
27. Papadakis S.E., Theocharis J.B., Kiartzis S.J., Bakirtzis A.G. Anovel approach to short-term load forecasting using fuzzy neural networks. IEEE Trans Power Syst 1998, 13:480-492.
28. Liang R.-H., Cheng C.-C. Short-term load forecasting by a neuro-fuzzy based approach. Int J Electr Power Energy Syst 2002, 24:103-111.
29. Pérez-Cruz F., Camps-Valls G., Soria-Olivas E., Pérez-Ruixo J., Figueiras-Vidal A., Artés-Rodríguez A. Multi-dimensional function approximation and regression estimation. Artif Neural Networks ICANN 2002, 757-762.
30. Tuia D., Verrelst J., Alonso L., Pérez-Cruz F., Camps-Valls G. Multioutput support vector regression for remote sensing biophysical parameter estimation. Geosci Remote Sens Lett IEEE 2011, 99:804-808.
31. Sanchez-Fernandez M., de-Prado-Cumplido M., Arenas-García J., Pérez-Cruz F. SVM multiregression for nonlinear channel estimation in multiple-input multiple-output systems. Signal Process IEEE Trans 2004, 52(8):2298-2307.
32. Bao Y., Xiong T., Hu Z. Multi-step-ahead time series prediction using multiple-output support vector regression. Neurocomputing 2014, 129:482-493.
33. Xiong T., Bao Y., Hu Z. Multiple-output support vector regression with a firefly algorithm for interval-valued stock price index forecasting. Knowledge Based Syst 2014, 55:87-100.
34. Hinojosa V.H., Hoese A. Short-term load forecasting using fuzzy inductive reasoning and evolutionary algorithms. IEEE Trans Power Syst 2010, 25(1):565-574.
35. Chen Y., Luh P.B., Guan C., Zhao Y., Michel L.D., Coolbeth M.A., et al. Short-term load forecasting: similar day-based wavelet neural networks. IEEE Trans Power Syst 2010, 25(1):322-330.
36. Niu D-x, Shi H-f, Wu D.D. Short-term load forecasting using bayesian neural networks learned by hybrid Monte Carlo algorithm. Appl Soft Comput 2012, 12(6):1822-1827.
37. Selakov A., Cvijetinović D., Milović L., Mellon S., Bekut D. Hybrid PSO-SVM method for short-term load forecasting during periods with significant temperature variations in city of Burbank. Appl Soft Comput 2014, 16(0):80-88.
38. Ceperic E., Ceperic V., Baric A. Astrategy for short-term load forecasting by support vector regression machines. IEEE Trans Power Syst 2013, 28(4):4356-4364.
39. Ben Taieb S., Hyndman R.J. Agradient boosting approach to the Kaggle load forecasting competition. Int J Forecast 2014, 30(2):382-394.
40. Vapnik V.N. The nature of statistical learning theory 1995, Springer, New York.
41. Vapnik V.N. Statistical learning theory 1998, John Wiley & Sons, Inc, New York.
42. Moscato P. On evolution search optimization genetic algorithms and martial arts: towards memetic algorithms 1989, Caltech Concurrent Computation Program, C3P Report 826 http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.27.9474.
43. Neri F., Cotta C. Memetic algorithms and memetic computing optimization: a literature review. Swarm Evol Comput 2012, 2:1-14.
44. Chen X., Ong Y.-S., Lim M.-H., Tan K.-C. Amulti-facet survey on memetic computation. IEEE Trans Evol Comput 2011, 15(5):591-607.
45. Yang X.S. Firefly algorithms for multimodal optimization. Stochastic algorithms: foundations and applications, proceedings 2009, 169-178. Springer, Berlin. O. Watanabe, T. Zeugmann (Eds.).
46. Yang X.S., Hosseini S.S.S., Gandomi A.H. Firefly algorithm for solving non-convex economic dispatch problems with valve loading effect. Appl Soft Comput 2012, 12(3):1180-1186.
47. Chandrasekaran K., Simon S.P. Optimal deviation based firefly algorithm tuned fuzzy design for multi-objective UCP. IEEE Trans Power Syst 2013, 28(1):460-471.
48. Blum C., Chiong R., Clerc M., De Jong K., Michalewicz Z., Neri F., et al. Evolutionary optimization. Variants of evolutionary algorithms for real-world applications 2012, 1-29. Springer, Berlin/Heidelberg.
49. Wang C-h, Grozev G., Seo S. Decomposition and statistical analysis for regional electricity demand forecasting. Energy 2012, 41(1):313-325.
50. Hooshmand R.A., Amooshahi H., Parastegari M. Ahybrid intelligent algorithm based short-term load forecasting approach. Int J Electr Power Energy Syst 2013, 45(1):313-324.
51. Ben Taieb S., Bontempi G., Atiya A.F., Sorjamaa A. Areview and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition. Expert Syst Appl 2012, 39(8):7067-7083.
52. Xiong T., Bao Y., Hu Z. Beyond one-step-ahead forecasting: evaluation of alternative multi-step-ahead forecasting models for crude oil prices. Energy Econ 2013, 40:405-415.
53. Chang C.-C., Lin C.-J. LIBSVM: a library for support vector machines 2001, Department of Computer Science, National Taiwan University, Software available at:. http://www.csie.ntu.edu.tw/%7Ecjlin/libsvm.
54. Guyon I., Elisseeff Ae An introduction to variable and feature selection. JMach Learn Res 2003, 3:1157-1182.
55. Demšar J. Statistical comparisons of classifiers over multiple data sets. JMach Learn Res 2006, 7:1-30.
56. Weise T., Chiong R. An alternative way of presenting statistical test results when evaluating the performance of stochastic approaches. Neurocomputing 2015, 147:235-238.
57. Fan S., Chen L.N. Short-term load forecasting based on an adaptive hybrid method. IEEE Trans Power Syst 2006, 21(1):392-401.
58. Billard L., Diday E. Symbolic data analysis: conceptual statistics and data mining 2006, John Wiley & Sons Ltd, England.