Research and application of a hybrid model based on multi-objective optimization for electrical load forecasting

Applied Energy

Volumn 180, Issue , 2016, Pages 213-233

  Xiao, Liye ^a   Shao, Wei ^a   Wang, Chen ^b   Zhang, Kequan ^b   Lu, Haiyan ^c  

^a UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA   (China)

^b LANZHOU UNIVERSITY   (China)

^c UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

Author keywords

Hybrid forecasting model; Multi objective firefly algorithm (MOFA); Multi objective optimization; Short term load forecasting (STLF)

Indexed keywords

BIOLUMINESCENCE; DATA HANDLING; ELECTRIC POWER PLANT LOADS; FORECASTING; MULTIOBJECTIVE OPTIMIZATION; NEURAL NETWORKS; OPTIMIZATION; STABILITY;

EFFECTIVENESS AND EFFICIENCIES; EFFICIENT MANAGEMENTS; ELECTRICAL LOAD FORECASTING; FIREFLY ALGORITHMS; GENERALIZED REGRESSION NEURAL NETWORK(GRNN); HYBRID FORECASTING; RESEARCH AND APPLICATION; SHORT TERM LOAD FORECASTING;

ELECTRIC LOAD FORECASTING;

ACCURACY ASSESSMENT; ALGORITHM; ELECTRICAL POWER; EXPERIMENTAL STUDY; FORECASTING METHOD; MODEL TEST; NUMERICAL MODEL; OPTIMIZATION; RESEARCH METHOD;

AUSTRALIA;

EID: 84980410056     PISSN: 03062619     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apenergy.2016.07.113     Document Type: Article

References (70)

1. [1] Masa-Bote, D., Castillo-Cagigal, M., Matallanas, E., et al. Improving photovoltaics grid integration through short time forecasting and self-consumption. Appl Energy 125:15 (2014), 103–113.
2. [2] Javed, F., Arshad, N., Wallin, F., Vassileva, I., Vassileva, E., Forecasting for demand response in smart grids: an analysis on use of anthropologic and structural data and short term multiple loads forecasting. Appl Energy 96 (2012), 150–160.
3. [3] Xiao, L., Wang, J., Yang, X., Xiao, L., A hybrid model based on data preprocessing for electrical power forecasting. Electr Power Energy Syst 64 (2015), 311–327.
4. [4] http://roll.sohu.com/20120801/n349565472.shtml.
5. [5] Xiao, L., Wang, J., Hou, R., Wu, J., A combined model based on data pre-analysis and weight coefficients optimization for electrical load forecasting. Energy 82 (2015), 524–549.
6. [6] Wang, Y., Wang, J., Zhao, G., Dong, Y., Application of residual modification approach in seasonal ARIMA for electricity demand forecasting: a case study of China. Energy Policy 48 (2012), 284–294.
7. [7] Zhang, M., Bao, H., Yan, L., Cao, J., Du, J., Research on processing of short-term historical data of daily load based on Kalman filter. Power System Technol, 10, 2003, 9.
8. [8] Amral, N., Ozveren, C.S., King, D., Short term load forecasting using multiple linear regression. Universities power engineering conference, 2007. UPEC 2007. 42nd international, 2007, IEEE, 1192–1198.
9. [9] Wei, L., Zhen-gang, Z., Based on time sequence of ARIMA model in the application of short-term electricity load forecasting. International conference on research challenges in computer science, 2009. ICRCCS’09, 2009, IEEE, 11–14.
10. [10] Christiaanse, W., Short-term load forecasting using general exponential smoothing. IEEE Trans Power Apparat Syst 8 (1971), 900–911.
11. [11] Irisarri, G., Widergren, S.E., Yehsakul, P.D., On-line load forecasting for energy control center application. IEEE Trans Power Apparat Syst, 1982, 71–78.
12. [12] Nie, H., Liu, G., Liu, X., Wang, Y., Hybrid of ARIMA and SVMs for short-term loadforecasting. Energy Proc 16 (2012), 1455–1460.
13. [13] Lin, W., Gow, H., Tsai, M., An enhanced radial basis function network for short-term electricity price forecasting. Appl Energy 87:10 (2010), 3226–3234.
14. [14] Wang, J., Li, L., Niu, D., Tan, Z., An annual load forecasting model based on support vector regression with differential evolution algorithm. Appl Energy 94 (2012), 65–70.
15. [15] Jain, R., Smith, K., Culligan, P., Taylor, J., Forecasting energy consumption of multi-family residential buildings using support vector regression: investigating the impact of temporal and spatial monitoring granularity on performance accuracy. Appl Energy 123:15 (2014), 168–178.
16. [16] Liu, H., Tian, H., Pan, D., Li, Y., Forecasting models for wind speed using wavelet, wavelet packet, time series and Artificial Neural Networks. Appl Energy 107 (2013), 191–208.
17. 2 and electricity prices and their volatilities. Appl Energy 101 (2013), 363–375.
18. [18] Uri, N., Forecasting peak system load using a combined time series and econometric model. Appl Energy 4:3 (1978), 219–227.
19. [19] Metaxiotis, K., Kagiannas, A., Askounis, D., Psarras, J., Artificial intelligence in short term electric load forecasting: a state-of-the-art survey for the researcher. Energy Convers Manage 44 (2003), 1525–1534.
20. [20] Xiao, L., Shao, W., Liang, T., Wang, C., A combined model based on multiple seasonal patterns and modified firefly algorithm for electrical load forecasting. Appl Energy 167 (2016), 135–153.
21. [21] Specht, D., A general regression neural network. IEEE Trans Neural, 1991.
22. [22] Bodyanskiy, Y., Popov, S., Rybalchenko, T., Multilayer neuro-fuzzy network for short-term electric load forecasting. Lecture Notes Comput Sci 5010 (2008), 339–348.
23. [23] Nagi, J., Yap, K., Nagi, F., Tiong, S., Ahmed, S., A computational intelligence for prediction of daily peak load. Appl Soft Comput 11 (2011), 4773–4788.
24. [24] Rahman, S., Bhatangar, R., An expert system based algorithm for short term load forecasting. IEEE Trans Power Syst 3 (1988), 392–399.
25. [25] Cai, Y., Wang, J., Tang, Y., Yang, Y., An efficient approach for electric load forecasting using distributed ART (adaptive resonance theory) & HS-ARTMAP (Hyper-spherica l ARTMAP network) neural network. Energy 36 (2011), 1340–1350.
26. [26] Kim, Y., Jeong, D., Ko, I., Combining rainfall-runoff model outputs for improving ensemble streamflow prediction. J Hydrol Eng 11:6 (2006), 578–588.
27. [27] Wang, J., Song, Y., Liu, F., Hou, R., Analysis and application of forecasting models in wind power integration: a review of multi-step-ahead wind speed forecasting models. Renew Sustain Energy Rev 60 (2016), 960–981.
28. [28] Liu, N., Tang, Q., Zhang, J., Fan, W., Liu, J., A hybrid forecasting model with parameter optimization for short-term load forecasting of micro-grids. Appl Energy 129 (2014), 336–345.
29. [29] Yu, F., Xu, X., A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network. Appl Energy 134 (2014), 102–113.
30. [30] Tascikaraoglu, A., Uzunoglu, M., A review of combined approaches for prediction of short-term wind speed and power. Renew Sustain Energy Rev 34 (2014), 243–254.
31. [31] Raza, Qamar, Muhammad, Khosravi, Abbas, A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings. Renew Sustain Energy Rev 50 (2015), 1352–1372.
32. [32] Selakov, A., Cvijetinovic, D., Milovic, 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 16 (2014), 80–88.
33. [33] Liao, G., Hybrid improved differential evolution and wavelet neural network with load forecasting problem of air conditioning. Electr Power Energy Syst 61 (2014), 673–682.
34. [34] Abdollah, K., Haidar, S., Fatemeh, M., A new hybrid modified firefly algorithm and support vector regression model for accurate short term load forecasting. Expert Syst Appl 41 (2014), 6047–6056.
35. [35] Savic, D., Walters, G., Genetic algorithms for the least-cost design of water distribution networks. J Water Res Plan Manage 123:2 (1997), 67–77.
36. [36] Bureerat, S., Sriworamas, K., Simultaneous topology and sizing optimization of a water distribution network using a hybrid multi-objective evolutionary algorithm. Appl Soft Comput 13:8 (2013), 3693–3702.
37. [37] Lu, J., Ireland, D., Lewis, A., Multi-objective optimization in high frequency electro magnetic-an effective technique for Smart Mobile Terminal Antenna (SMTA) design. IEEE Trans Evol Comput 45:3 (2009), 1072–1075.
38. [38] Kong, W., Chai, T., Yang, S., Ding, J., A hybrid evolutionary multi-objective optimization strategy for the dynamic power supply problem in magnesia grain manufacturing. Appl Soft Comput 13:5 (2013), 2960–2969.
39. [39] Deb, K., Pratap, A., Agarwal, S., Meyarivan, T., A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6:2 (2002), 182–197.
40. [40] Zitzler, E., Laumanns, M., Thiele, L., SPEA2: improving the strength pareto evolutionary algorithm. Technical report 103, 2001, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH), Zurich (Switzerland).
41. [41] Zitzler, E., Evolutionary algorithms for multiobjective optimization: methods and applications (Ph.D. thesis)., 1999, ETH Zurich, Switzerland.
42. [42] Steuer, R., Multiple criteria optimization: theory computation, and application. 1986, Wiley, New York.
43. [43] Cohon, J.L., Multiobjective programming planning. 1978, Academic Press, New York.
44. [44] Schaffer, J.D., Multiple objective optimization with vector evaluated genetic algorithms. Proceedings of the first international conference on genetic algorithms, 1985, 93–100.
45. [45] Srinivas, N., Deb, K., Multi-objective optimization using non-dominated sorting in genetic algorithms. Evol Comput 2:3 (1994), 221–248.
46. [46] Zhan, Z., Li, J.J., Cao, J., Zhang, J., Chung, H., Shi, Y., Multiple populations for multiple objectives: a co-evolutionary technique for solving multi-objective optimization problems. IEEE Trans Cybern 42:2 (2013), 445–463.
47. [47] Nebro, A., Durillo, J., Garcia-Nieto, J., Coello Coello, C., Luna, F., Alba, E., SMPSO: a new pso-based metaheuristic for multi-objective optimization. Proceedings of IEEE symposium on computational intelligence in multi-criteria decision-making, 2009, 66–73.
48. [48] Dorigo, M., Blum, C., Ant colony optimization theory: a survey. Theor Comput Sci 344:2–3 (2005), 243–278.
49. [49] Yang, J., Zhuang, Y., An improved ant colony optimization algorithm for solving a complex combinatorial optimization problem. Appl Soft Comput 10:2 (2010), 653–660.
50. [50] Coello, C., Cruz, C., An approach to solve multi-objective optimization problems based on an artificial immune system. Proceedings of first international conference on artificial immune systems, 2002, 212–221.
51. [51] Jiao, L., Gong, M., Shang, R., Du, H., Lu, B., Clonal selection with immune dominance and energy based multi-objective optimization evolutionary multicriterion optimization. Lect Notes Comput Sci 3410 (2005), 474–489.
52. [52] Yang, X., Nature-inspired metaheuristic algorithms. 2008, Luniver Press, UK.
53. [53] Yang, X., Multi-objective firefly algorithm for continuous optimization. Eng Comput 29 (2013), 175–184.
54. [54] Shu, T., Gao, X., Chen, S., Wang, S., Lai, K., Gan, L., Weighing efficiency-robustness in supply chain disruption by multi-objective firefly algorithm. Sustainability 8 (2016), 250–277.
55. [55] Marichelvam, M., Prabaharan, T., Yang, X., A discrete firefly algorithm for the multi-objective hybrid flowshop scheduling problems. IEEE Trans Evolut Comput 18 (2014), 301–305.
56. [56] Liang, R., Wang, J., Chen, Y., Tseng, W., An enhanced firefly algorithm to multi-objective optimal active/reactive power dispatch with uncertainties consideration. Electr Power Energy Syst 64 (2015), 1088–1097.
57. [57] Karthikeyan, S., Asokan, P., Nickolas, S., Tom, P., A hybrid discrete firefly algorithm for solving multi-objective flexible job shop scheduling problems. Int J Bio-Inspired Comput, 7, 2015, 6.
58. [58] Specht, D.F., A general regression neural network. IEEE Trans Neural Networks 2:6 (1991), 568–576.
59. [59] Crone, S., Kourentzes, N., Input-variable specification for neural networks-an-analysis of forecasting low and high time series frequency. Proceedings of the international joint conference on neural networks (IJCNN), 2009.
60. [60] Darbellay, G., Slama, M., Forecasting the short-term demand for electricity-do neural networks stand a better chance?. Int J Forecast 16 (2000), 71–83.
61. [61] Yang, X., Firefly algorithms for multimodal optimization, stochastic algorithms: foundations and applications. SAGA 2009. Lect Notes Comput Sci 5792 (2009), 169–178.
62. [62] Srinivas, N., Deb, K., Multiobjective function optimization using nondominated sorting genetic algorithms. Evol Comput 2:3 (1995), 221–248 [Fall].
63. [63] Deb, K., Pratap, A., Agarwal, S., Meyarivan, T., A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6:2 (2002), 182–197.
64. [64] Polat, O., Yildirim, T., Hand geometry identification without feature extraction by general regression neural network. Expert Syst Appl 34 (2008), 845–849.
65. [65] Diebold, F.X., Mariano, R., Comparing predictive accuracy. J Bus Econ Stat 13 (1995), 253–263.
66. [66] Chen, H.Y., Hou, D.P., Research on superior combination forecasting model based on forecasting effective measure. J Univ Sci Technol China, 2, 2002, 006.
67. [67] Zhang, Jie, Morri, A.J., A sequential learning approach for single hidden layer neural networks. Neural Networks 11:1 (1998), 65–80.
68. [68] Gemperline, P.J., Long, J.R., Gregoriou, V.G., Nonlinear multivariate calibration using principal components regression and artificial neural networks. Anal Chem 63 (1991), 2313–2323.
69. [69] http://www.sgcc.com.cn/dwxx/qydwyxqk/zgfhyjfhl/290684.shtml.
70. [70] Hobbs, B.F., Jitprapaikulsarn, S., Konda, S., Chankong, V., Loparo, K.A., Maratukulam, D.J., Analysis of the value for unit commitment of improved load forecasts. IEEE Trans Power Syst 14:4 (1999), 1342–1348.