A novel machine learning approach for estimation of electricity demand: An empirical evidence from Thailand

Energy Conversion and Management

Volumn 74, Issue , 2013, Pages 548-555

  Mostafavi, Elham Sadat ^a,b   Mostafavi, Seyyed Iman ^c   Jaafari, Arefeh ^c   Hosseinpour, Fariba ^c  

^a ISFAHAN UNIVERSITY OF TECHNOLOGY   (Iran)

^b MONTANA STATE UNIVERSITY   (United States)

^c ISLAMIC AZAD UNIVERSITY   (Iran)

Author keywords

Electricity demand; Genetic programming; Hybrid method; Prediction; Simulated annealing

Indexed keywords

ACCURATE PREDICTION; ELECTRICITY DEMANDS; GROSS DOMESTIC PRODUCTS; HYBRID APPROACH; HYBRID METHOD; INDUSTRIAL PRODUCT; MACHINE LEARNING APPROACHES; PREDICTION EQUATIONS;

FORECASTING; GENETIC PROGRAMMING; NEURAL NETWORKS; SIMULATED ANNEALING;

ELECTRICITY;

EID: 84882289494     PISSN: 01968904     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.enconman.2013.06.031     Document Type: Article

References (55)

1. A. Azadeh, M. Saberi, S.F. Ghaderi, A. Gitiforouz, and V. Ebrahimipour Improved estimation of electricity demand function by integration of fuzzy system and data mining approach Energy Convers Manage 49 2008 2165 2177
2. K. Kandananond Forecasting electricity demand in Thailand with an artificial neural network approach Energies 4 2011 1246 1257
3. G.E.P. Box, and G.M. Jenkins Time series analysis: forecasting and control 1970 Holden-Day San Francisco, CA, USA 5 10
4. Cho MY, Hwang JC, Che S. Customer short term load forecasting by using ARIMA transfer function model. In: Proceedings of the international conference on energy management and power delivery. Singapore: EMPD'95; 20-23 November 1995. p. 317-22.
5. R.E. Abdel-Aal, and A.Z. Al-Garni Forecasting monthly electric energy consumption in eastern Saudi Arabia using univariate time-series analysis Energy 22 1997 1059 1069
6. V.S. Ediger, and S. Akar ARIMA forecasting of primary energy demand by fuel in Turkey Energy Policy 35 2007 1701 1708 (Pubitemid 44858159)
7. Suhartono, and A.J. Endharta Short term electricity load demand forecasting in Indonesia by using double seasonal recurrent neural networks Int J Math Mod Meth Appl Sci 3 2009 171 178
8. N. Mohamed, M.H. Ahmad, Z. Ismail, and Suhartono Double seasonal ARIMA model for forecasting load demand Matematika 26 2010 217 231
9. J.W. Taylor Short-term electricity demand forecasting using double seasonal exponential smoothing J Oper Res Soc 54 2003 799 805
10. J.W. Taylor Triple seasonal methods for short-term electricity demand forecasting Eur J Oper Res 204 2010 139 152
11. J.W. Taylor, L.M. de Menezes, and P.E. Mc Sharry A comparison o univariate methods for forecasting electricity demand up to a day ahead Int J Forecast 22 2006 1 16 (Pubitemid 43174787)
12. A.H. Alavi, and A.H. Gandomi A robust data mining approach for formulation of geotechnical engineering systems Eng Computations 28 3 2011 242 274
13. T. Mitchell Does machine learning really work? AI Mag 18 3 1997 11 20 (Pubitemid 127684648)
14. H. Tien Pao Forecasting electricity market pricing using artificial neural networks Energy Convers Manage 48 2007 907 912
15. A. O'ztopal Artificial neural network approach to spatial estimation of wind velocity data Energy Convers Manage 2006 395 406
16. M. Gölcü Artificial neural network based modeling of performance characteristics of deep well pumps with splitter blade Energy Convers Manage 2006 3333 3343 (Pubitemid 44051688)
17. M. Beccali, M. Cellura, Brano V. Lo, and A. Marvuglia Forecasting daily urban electric load profiles using artificial neural networks Energy Convers Manage 45 2004 2879 2900
18. A. Azadeh, S.F. Ghaderi, and S. Sohrabkhani Annual electricity consumption forecasting by neural network in high energy consuming industrial sectors Energy Convers Manage 49 2008 2272 2278
19. R. Gareta, L.M. Romeo, and A. Gil Forecasting of electricity prices with neural networks Energy Convers Manage 47 2006 1770 1778
20. C.C. Hsu, and C.Y. Chen Regional load forecasting in Taiwan-applications of artificial neural networks Energy Convers Manage 44 2003 1941 1949
21. J.V. Ringwood, D. Bofelli, and F.T. Murray Forecasting electricity demand on short, medium and long time scales using neural networks J Intell Robot Syst 31 2001 129 147 (Pubitemid 33099002)
22. J.P.S. Catalao, S.J.P.S. Mariano, V.M.F. Mendes, and L.A.F.M. Ferreira Short-term electricity prices forecasting in a competitive market: a neural network approach Electr Power Syst Res 77 2007 1297 1304 (Pubitemid 46813211)
23. A. Azadeh, S.F. Ghaderi, S. Tarverdian, and M. Saberi Intergraion of artificial neural networks and genetic algorithm to predict electrical energy consumption Appl Math Comput 186 2007 1731 1741 (Pubitemid 46509532)
24. A. Azadeh, S.F. Ghaderi, and S. Sohrabkhani A simulated-based neural network algorithm for forecasting electrical energy consumption in Iran Energy Policy 36 2008 2637 2644
25. A. Azadeh, S.F. Ghaderi, and S. Sohrabkhani Forecasting electrical consumption by integration of neural network, time series and ANOVA Appl Math Comput 186 2007 1753 1761 (Pubitemid 46497380)
26. W.-C. Hong Electric load forecasting by support vector model Appl Math Model 33 2009 2444 2454
27. S. Fan, and L. Chen Short-term load forecasting based on an adaptive hybrid method IEEE Trans Power Syst 21 2006 392 401 (Pubitemid 43204739)
28. S. Fan, L. Chen, and W.J. Lee Machine learning based switching model for electricity load forecasting Energy Convers Manage 49 2008 1331 1344
29. E.S. Mostafavi, S. Saeedi, R. Sarvar, H. Izadi Moud, and S.M. Mousavi A hybrid computational approach to estimate solar global radiation: an empirical evidence from Iran Energy 49 2013 204 210
30. J.R. Koza Genetic programming, on the programming of computers by means of natural selection 1992 MIT Press Cambridge
31. A.H. Alavi, A.H. Gandomi, and A. Mollahasani A genetic programming-based approach for performance characteristics assessment of stabilized soil. Variants of evolutionary algorithms for real-world applications 2011 Springer-Verlag Berlin 343 375 [Chapter 9]
32. A.H. Alavi, M. Ameri, A.H. Gandomi, and M.R. Mirzahosseini Formulation of flow number of asphalt mixes using a hybrid computational method Constr Build Mater 25 3 2011 1338 1355
33. D.G. Lee, B.W. Lee, and S.H. Chang Genetic programming model for long-term forecasting of electric power demand Electr Power Syst Res 40 1997 17 22 (Pubitemid 127412680)
34. Bhattacharya M, Abraham A, Nath B. A linear genetic programming approach for modeling electricity demand prediction in Victoria. In: Proceedings of the hybrid information systems. Australia: Adelaide; 2001. p. 379-93.
35. N. Metropolis, A.W. Rosenbluth, M.N. Rosenbluth, A.H. Teller, and E. Teller Equation of state calculations by fast computing mechanics J Chem Phys 21 6 1953 1087 1092
36. Folino G, Pizzuti C, Spezzano G. Genetic programming and simulated annealing: a hybrid method to evolve decision trees. In: Proceedings of EuroGP'2000; 1802. p. 294-03.
37. Deschaine LM, Zafran FA, Patel JJ, Amick D, Pettit R, Francone FD, et al. Solving the unsolved using machine learning, data mining and knowledge discovery to model a complex production process. In: Proceedings of advanced technology simulation conference. Wasington, DC; 2000.
38. A.H. Alavi, A.H. Gandomi, M. Mousavi, and A. Mollahasani High-precision modeling of uplift capacity of suction caissons using a hybrid computational method GeomechEng 2 4 2010 253 280
39. P. Aminian, H. Niroomand, A.H. Gandomi, A.H. Alavi, and M. Arab Esmaeili New design equations for assessment of load carrying capacity of CSB: a machine learning approach Neural Compu Appl 2013 10.1007/s00521-012-1138-4
40. Tesink S. Improving intrusion detection systems through machine learning. In: ILK research group, Technical report series no. 07-02; 2007.
41. Hall M, Smith L. Practical feature subset selection for machine learning. In: Proceedings of the Australian computer science conference (university of Western Australia). University of Waikato; 1996.
42. A.H. Gandomi, A.H. Alavi, and M.G. Sahab New formulation for compressive strength of CFRP confined concrete cylinders using linear genetic programming Mater Struct 43 7 2010 963 983
43. F.D. Francone Discipulus lite TM software owner's manual 2004 Machine Learning Technologies Inc. Littleton, CO, USA
44. M. Conrads, O. Dolezal, F.D. Francone, and P. Nordin Discipulus Lite TM-fast genetic programming based on AIM learning technology 2004 Register Machine Learning Technologies Inc. Littleton, CO, USA
45. Deschaine LM. Using genetic programming to develop a C/C++ simulation model of a waste incinerator. Science applications international corp., Draft Technical Report; 2000.
46. G.N. Smith Probability and statistics in civil engineering 1986 Collins London
47. 2 J Mole Graph Model 20 2002 269 276
48. P.P. Roy, and K. Roy On some aspects of variable selection for partial least squares regression models QSAR Comb Sci 27 2008 302 313
49. M.R. Mirzahosseini, A. Aghaeifar, A.H. Alavi, A.H. Gandomi, and M. Seyednour Permanent deformation analysis of asphalt mixtures using soft computing techniques Exp Syst Appl 38 2011 6081 6100
50. A.H. Alavi, and A.H. Gandomi Prediction of Principal Ground-Motion Parameters Using a Hybrid Method Coupling Artificial Neural Networks and Simulated Annealing Comput Struct 89 23-24 2011 2176 2194
51. A. Peyvandi, P. Soroushian, A.M. Balachandra, and K. Sobolev Enhancement of the durability characteristics of concrete nanocomposite pipes with modified graphite nanoplatelets Constr Build Mater 47 2013 111 117
52. A. Peyvandi, P. Soroushian, and S. Jahangirnejad Enhancement of the structural efficiency and performance of concrete pipes through fiber reinforcement Constr Build Mater 45 2013 36 44
53. A.H. Gandomi, and A.H. Alavi Multi-Stage Genetic Programming: A New Strategy to Nonlinear System Modeling Inf Sci 181 23 2011 5227 5239
54. X.S. Yang, A.H. Gandomi, S. Talatahari, and A.H. Alavi Metaheuristics in Water Resources, Geotechnical and Transportation Engineering 2012 Elsevier Waltham
55. A.H. Alavi, and A.H. Gandomi Energy-Based Models for Assessment of Soil Liquefaction Geosci Frontiers 3 4 2012 541 555