Multi-layer perceptron hybrid model integrated with the firefly optimizer algorithm for windspeed prediction of target site using a limited set of neighboring reference station data

Renewable Energy

Volumn 116, Issue , 2018, Pages 309-323

  Deo, Ravinesh C ^a   Ghorbani, Mohammad Ali ^b,c   Samadianfard, Saeed ^b   Maraseni, Tek ^a   Bilgili, Mehmet ^d   Biazar, Mustafa ^b  

^a UNIVERSITY OF SOUTHERN QUEENSLAND   (Australia)

^b UNIVERSITY OF TABRIZ   (Iran)

^c NEAR EAST UNIVERSITY   (Turkey)

^d CUKUROVA UNIVERSITY   (Turkey)

Author keywords

Firefly algorithm (FFA); Hybrid predictive model; Multilayer perceptron; Prediction; Windspeed

Indexed keywords

BIOLUMINESCENCE; CLIMATE MODELS; ENERGY CONVERSION; ERRORS; FLOOD CONTROL; INVESTMENTS; MULTILAYER NEURAL NETWORKS; MULTILAYERS; OPTIMIZATION; SITE SELECTION; WEATHER FORECASTING; WIND POWER;

ALGORITHM MODEL; FIREFLY ALGORITHM; FIREFLY ALGORITHMS; HYBRID MODEL; HYBRID PREDICTIVE MODEL; MULTILAYERS PERCEPTRONS; OPTIMIZERS; PREDICTIVE MODELS; TARGET SITES; WINDSPEED;

MEAN SQUARE ERROR;

ACCURACY ASSESSMENT; ARTIFICIAL INTELLIGENCE; MODEL VALIDATION; PERFORMANCE ASSESSMENT; WIND FARM; WIND VELOCITY;

EAST AZERBAIJAN; IRAN; KALEYBAR; MIANEH; SAHAND; SARAB; TABRIZ;

EID: 85030656566     PISSN: 09601481     EISSN: 18790682     Source Type: Journal    
DOI: 10.1016/j.renene.2017.09.078     Document Type: Article

References (93)

1. Edenhofer, O., et al. Summary for Policy Makers. IPCC special report on renewable energy sources and climate change mitigation, 2011, 1–29.
2. IPCC, Summary for policymakers: a special report of working groups I and II of the intergovernmental panel on climate change. Field, C.B., et al. (eds.) Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation, 2012, Cambridge University Press, Cambridge, UK.
3. Waewsak, J., Landry, M., Gagnon, Y., Offshore wind power potential of the Gulf of Thailand. Renew. Energy 81 (2015), 609–626.
4. Maraseni, T.N., Cockfield, G., Maroulis, J., An assessment of greenhouse gas emissions: implications for the Australian cotton industry. J. Agric. Sci. 148:05 (2010), 501–510.
5. Maraseni, T.N., Mushtaq, S., Maroulis, J., Greenhouse gas emissions from rice farming inputs: a cross-country assessment. J. Agric. Sci. 147:02 (2009), 117–126 [6] Marchal, V., Dellink, R., Vuuren, D.v., Clapp, C., Château, J., Lanzi, E., Magné, B. and Vliet, J.v., 2011. OECD Environmental Outlook to 2050: Chapter 3 Climate Change, Pre-release Version, November 2011. OECD, https://www.oecd.org/env/cc/49082173.pdf.
6. Marchal, V., Dellink, R., Vuuren, D.v., Clapp, C., Château, J., Lanzi, E., Magné, B., Vliet, J.v., OECD Environmental Outlook to 2050: Chapter 3 Climate Change, Pre-release Version, November 2011. 2011, OECD https://www.oecd.org/env/cc/49082173.pdf.
7. Burton, T., Sharpe, D., Jenkins, N., Bossanyi, E., Wind Energy Handbook. 2001, John Wiley & Sons.
8. Li, G., Shi, J., On comparing three artificial neural networks for wind speed forecasting. Appl. Energy 87:7 (2010), 2313–2320.
9. GWEC, Global Wind Report Annual Market Update., 2016, Global Wind Energy Council Available Online: http://files.gwec.net/register?file=/files/GWR2016.pdf.
10. Mohandes, M.A., Rehman, S., Halawani, T.O., A neural networks approach for wind speed prediction. Renew. Energy 13:3 (1998), 345–354.
11. Fazelpour, F., Soltani, N., Soltani, S., Rosen, M.A., Assessment of wind energy potential and economics in the north-western Iranian cities of Tabriz and Ardabil. Renew. Sustain. energy Rev. 45 (2015), 87–99.
12. Nasehi, S., Karimi, S., Jafari, H., Application of Fuzzy GIS and ANP for Wind Power Plant Site Selection in East Azerbaijan Province of Iran. 2016.
13. Qolipour, M., et al. Evaluation of wind power generation potential using a three hybrid approach for households in Ardebil Province, Iran. Energy Convers. Manag. 118 (2016), 295–305.
14. Florens, M., Cairns, I.H., Knock, S., Robinson, P., Data-driven solar wind model and prediction of type II bursts. Geophys. Res. Lett., 34(4), 2007.
15. Salcedo-Sanz, S., Pastor-Sánchez, A., Prieto, L., Blanco-Aguilera, A., García-Herrera, R., Feature selection in wind speed prediction systems based on a hybrid coral reefs optimization–Extreme learning machine approach. Energy Convers. Manag. 87 (2014), 10–18.
16. Salcedo-Sanz, S., et al. Accurate short-term wind speed prediction by exploiting diversity in input data using banks of artificial neural networks. Neurocomputing 72:4 (2009), 1336–1341.
17. Damousis, I.G., Alexiadis, M.C., Theocharis, J.B., Dokopoulos, P.S., A fuzzy model for wind speed prediction and power generation in wind parks using spatial correlation. IEEE Trans. Energy Convers. 19:2 (2004), 352–361.
18. Deo, R.C., Sahin, M., Forecasting long-term global solar radiation with an ANN algorithm coupled with satellite-derived (MODIS) land surface temperature (LST) for regional locations in Queensland. Renew. Sustain. energy Rev. 72 (2017), 828–848.
19. Deo, R.C., Wen, X., Feng, Q., A wavelet-coupled support vector machine model for forecasting global incident solar radiation using limited meteorological dataset. Appl. Energy 168 (2016), 568–593.
20. Kariniotakis, G., Stavrakakis, G., Nogaret, E., Wind power forecasting using advanced neural networks models. IEEE Trans. Energy Convers. 11:4 (1996), 762–767.
21. Hofleitner, A., A Hybrid Approach of Physical Laws and Data-driven Modeling for Estimation: the Example of Queuing Networks. 2013, EECS Department, University of California, Berkeley http://www2.eecs.berkeley.edu/Pubs/TechRpts/2013/EECS-2013-87.pdf.
22. Barthelmie, R.J., et al. Modelling and measuring flow and wind turbine wakes in large wind farms offshore. Wind Energy 12:5 (2009), 431–444.
23. Diamantoulakis, P.D., Kapinas, V.M., Karagiannidis, G.K., Big data analytics for dynamic energy management in smart grids. Big Data Res. 2:3 (2015), 94–101.
24. Nabati, E.G., Thoben, K.D., Big Data Analytics in the Maintenance of Off-shore Wind Turbines: a Study on Data Characteristics. Dynamics in Logistics. 2017, Springer, 131–140.
25. Bilgili, M., Sahin, B., Wind speed prediction of target station from reference stations data. Energy Sources, Part A Recovery, Util. Environ. Eff. 35:5 (2013), 455–466.
26. Dokur, E., Kurban, M., Ceyhan, S., Hybrid model for short term wind speed forecasting using empirical mode decomposition and artificial neural network. Electrical and Electronics Engineering (ELECO). 2015 9th International Conference on, IEEE, 2015, 420–423.
27. Islam, M.S., Mohandes, M., Rehman, S., Vertical extrapolation of wind speed using artificial neural network hybrid system. Neural Comput. Appl., 2016, 1–11.
28. Mabel, M.C., Fernandez, E., Analysis of wind power generation and prediction using ANN: a case study. Renew. Energy 33:5 (2008), 986–992.
29. Barbounis, T., Theocharis, J., A locally recurrent fuzzy neural network with application to the wind speed prediction using spatial correlation. Neurocomputing 70:7 (2007), 1525–1542.
30. Ghorbani, M., Khatibi, R., Hosseini, B., Bilgili, M., Relative importance of parameters affecting wind speed prediction using artificial neural networks. Theor. Appl. Climatol. 114:1–2 (2013), 107–114.
31. More, A., Deo, M., Forecasting wind with neural networks. Mar. Struct. 16:1 (2003), 35–49.
32. Guo, Z., Zhao, J., Zhang, W., Wang, J., A corrected hybrid approach for wind speed prediction in Hexi Corridor of China. Energy 36:3 (2011), 1668–1679.
33. Hu, J., Wang, J., Zeng, G., A hybrid forecasting approach applied to wind speed time series. Renew. Energy 60 (2013), 185–194.
34. McCulloch, W.S., Pitts, W., A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5:4 (1943), 115–133.
35. Gardner, M.W., Dorling, S., Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmos. Environ. 32:14 (1998), 2627–2636.
36. Tu, J.V., Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. J. Clin. Epidemiol. 49:11 (1996), 1225–1231.
37. Gocić, M., et al. Soft computing approaches for forecasting reference evapotranspiration. Comput. Electron. Agric. 113 (2015), 164–173.
38. Collobert, R., Bengio, S., Links between perceptrons, MLPs and SVMs. Proceedings of the Twenty-first International Conference on Machine Learning, 2004, ACM, 23.
39. Vieira, J., Dias, F.M., Mota, A., Neuro-fuzzy systems: a survey. 5th WSEAS NNA International Conference, 2004.
40. Pater, L., Application of Artificial Neural Networks and Genetic Algorithms for Crude Fractional Distillation Process Modeling. 2016 arXiv preprint arXiv:1605.00097.
41. Currie, J.J., Goulet, P.J., Ratsimandresy, A.W., Wind conditions in a fjordlike bay and predictions of wind speed using neighboring stations employing neural network models. J. Appl. Meteorol. Climatol. 53:6 (2014), 1525–1537.
42. Salcedo-Sanz, S., Deo, R.C., Carro-Calvo, L., Saavedra-Moreno, B., Monthly prediction of air temperature in Australia and New Zealand with machine learning algorithms. Theor. Appl. Climatol., 2015, 10.1007/s00704-00015-01480-00704.
43. Yang, X.-S., Firefly algorithm, stochastic test functions and design optimisation. Int. J. Bio-Inspired Comput. 2:2 (2010), 78–84.
44. Kaushik, A., Tayal, D.K., Yadav, K., Kaur, A., Integrating firefly algorithm in artificial neural network models for accurate software cost predictions. J. Softw. Evol. Process 28:8 (2016), 665–688.
45. Hassanzadeh, T., Faez, K., Seyfi, G., A speech recognition system based on structure equivalent fuzzy neural network trained by firefly algorithm. Biomedical Engineering (ICoBE), 2012 International Conference on, IEEE, 2012, 63–67.
46. Soleymani, S.A., et al. A novel method to water level prediction using RBF and FFA. Water Resour. Manag. 30:9 (2016), 3265–3283.
47. Nandy, S., et al. Agent based adaptive firefly back-propagation neural network training method for dynamic systems. Hybrid Intelligent Systems (HIS), 2012 12th International Conference on, IEEE, 2012, 449–454.
48. Alweshah, M., Firefly algorithm with artificial neural network for time series problems. Res. J. Appl. Sci. Eng. Technol. 7:19 (2014), 3978–3982.
49. 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. 55 (2014), 87–100.
50. Ghorbani, M.A., Deo, R.C., Yaseen, Z.M.K., Mahasa, H., Mohammad, B., Pan evaporation prediction using a hybrid multilayer perceptron-firefly algorithm (MLP-FFA) model: case study in North Iran. Theor. Appl. Climatol., 2017, 10.1007/s00704-017-2244-0 In Press.
51. Raheli, B., Aalami, M.T., El-Shafie, A., Ghorbani, M.A., Deo, R.C., Uncertainty assessment of the multilayer perceptron (MLP) neural network model with implementation of the novel hybrid MLP-FFA method for prediction of biochemical oxygen demand and dissolved oxygen: a case study of Langat River. Environ. Earth Sci., 76(14), 2017, 503.
52. Yaseen, Z.M., et al. Rainfall pattern forecasting using novel hybrid intelligent model based ANFIS-FFA. Water Resour. Manag., 2017 In Press.
53. Keyhani, A., Ghasemi-Varnamkhasti, M., Khanali, M., Abbaszadeh, R., An assessment of wind energy potential as a power generation source in the capital of Iran, Tehran. Energy 35:1 (2010), 188–201.
54. Koçak, K., Practical ways of evaluating wind speed persistence. Energy 33:1 (2008), 65–70.
55. Bilgili, M., Sahin, B., Yasar, A., Application of artificial neural networks for the wind speed prediction of target station using reference stations data. Renew. Energy 32:14 (2007), 2350–2360.
56. Carta, J.A., Ramirez, P., Bueno, C., A joint probability density function of wind speed and direction for wind energy analysis. Energy Convers. Manag. 49:6 (2008), 1309–1320.
57. García-Rojo, R., Algorithm for the estimation of the long-term wind climate at a meteorological mast using a joint probabilistic approach. Wind Eng. 28:2 (2004), 213–223.
58. Velázquez, S., Carta, J.A., Matías, J., Comparison between ANNs and linear MCP algorithms in the long-term estimation of the cost per kWh produced by a wind turbine at a candidate site: a case study in the Canary Islands. Appl. Energy 88:11 (2011), 3869–3881.
59. Casella, L., Improving long-term wind speed assessment using joint probability functions applied to three wind data sets. Wind Eng. 36:4 (2012), 473–483.
60. Fu, Q., Jiang, R., Wang, Z., Li, T., Optimization of soil water characteristic curves parameters by modified firefly algorithm. Trans. Chin. Soc. Agric. Eng. 31:11 (2015), 117–122.
61. Garousi-Nejad, I., Bozorg-Haddad, O., Loáiciga, H.A., Modified firefly algorithm for solving multireservoir operation in continuous and discrete domains. J. Water Resour. Plan. Manag., 2016 p. 04016029.
62. Olatomiwa, L., et al. A support vector machine–firefly algorithm-based model for global solar radiation prediction. Sol. Energy 115 (2015), 632–644.
63. McClelland, J.L., Rumelhart, D.E., Explorations in Parallel Distributed Processing: a Handbook of Models, Programs, and Exercises. 1989, MIT press.
64. Yang, X.-S., Nature-inspired Metaheuristic Algorithms. 2010, Luniver press.
65. Łukasik, S., Żak, S., Firefly algorithm for continuous constrained optimization tasks. International Conference on Computational Collective Intelligence, 2009, Springer, 97–106.
66. Kayarvizhy, N., Kanmani, S., Uthariaraj, R., ANN models optimized using swarm intelligence algorithms. WSEAS Trans. Comput. 13:45 (2014), 501–519.
67. Chai, T., Draxler, R.R., Root mean square error (RMSE) or mean absolute error (MAE)?–Arguments against avoiding RMSE in the literature. Geosci. Model Dev. 7:3 (2014), 1247–1250.
68. Willmott, C.J., On the validation of models. Phys. Geogr. 2:2 (1981), 184–194.
69. Willmott, C.J., Some comments on the evaluation of model performance. Bull. Am. Meteorol. Soc. 63:11 (1982), 1309–1313.
70. Nash, J., Sutcliffe, J., River flow forecasting through conceptual models part I—a discussion of principles. J. Hydrol. 10:3 (1970), 282–290.
71. Legates, D.R., McCabe, G.J., Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model validation. Water Resour. Res. 35:1 (1999), 233–241.
72. Mohammadi, K., et al. A new hybrid support vector machine–wavelet transform approach for estimation of horizontal global solar radiation. Energy Convers. Manag. 92 (2015), 162–171.
73. Willmott, C.J., On the Evaluation of Model Performance in Physical Geography. Spatial Statistics and Models. 1984, Springer, 443–460.
74. Legates, D.R., Davis, R.E., The continuing search for an anthropogenic climate change signal: limitations of correlation-based approaches. Geophys. Res. Lett. 24:18 (1997), 2319–2322.
75. Legates, D.R., McCabe, G.J., A refined index of model performance: a rejoinder. Int. J. Climatol. 33:4 (2013), 1053–1056.
76. Kurup, P.U., Dudani, N.K., Neural networks for profiling stress history of clays from PCPT data. J. Geotech. Geoenviron. Eng. 128:7 (2002), 569–579.
77. Boadu, F.K., Rock properties and seismic attenuation: neural network analysis. pure Appl. Geophys. 149:3 (1997), 507–524.
78. Pal, M., Support vector machines-based modelling of seismic liquefaction potential. Int. J. Numer. Anal. Methods GeoMech. 30:10 (2006), 983–996.
79. Deo, R.C., Samui, P., Kim, D., Estimation of monthly evaporative loss using relevance vector machine, extreme learning machine and multivariate adaptive regression spline models. Stoch. Environ. Res. Risk Assess., 2015, 1–16.
80. Deo, R.C., Samui, P., Forecasting evaporative loss by least-square support-vector regression and evaluation with genetic programming, Gaussian process, and minimax probability machine regression: case study of Brisbane City. J. Hydrolog. Eng., 2017 p. 05017003.
81. Li, M.-F., Tang, X.-P., Wu, W., Liu, H.-B., General models for estimating daily global solar radiation for different solar radiation zones in mainland China. Energy Convers. Manag. 70 (2013), 139–148.
82. Ghorbani, M.A., et al. Application of firefly algorithm-based support vector machines for prediction of field capacity and permanent wilting point. Soil Tillage Res. 172 (2017), 32–38.
83. Yang, X.-S., Cui, Z., Xiao, R., Gandomi, A.H., Karamanoglu, M., Swarm Intelligence and Bio-inspired Computation: Theory and Applications. 2013, Newnes.
84. Pal, S.K., Rai, C., Singh, A.P., Comparative study of firefly algorithm and particle swarm optimization for noisy non-linear optimization problems. Int. J. Intell. Syst. Appl., 4(10), 2012, 50.
85. Thomas, J.B., et al. Assessment of surface winds over the Atlantic, Indian, and Pacific Ocean sectors of the Southern Ocean in CMIP5 models: historical bias, forcing response, and state dependence. J. Geophys. Res. Atmos. 118:2 (2013), 547–562.
86. Flandrin, P., Goncalves, P., Empirical mode decompositions as data-driven wavelet-like expansions. Int. J. Wavelets, Multiresolut. Inf. Process. 2:04 (2004), 477–496.
87. Huang, N.E., et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London a: Mathematical, Physical and Engineering Sciences, 1998, The Royal Society, 903–995.
88. Bechrakis, D.A., Sparis, P.D., Correlation of wind speed between neighboring measuring stations. IEEE Trans. Energy Convers. 19:2 (2004), 400–406.
89. Rauthe, M., Hense, A., Paeth, H., A model intercomparison study of climate change-signals in extratropical circulation. Int. J. Climatol. 24:5 (2004), 643–662.
90. Fant, C., Schlosser, C.A., Strzepek, K., The impact of climate change on wind and solar resources in southern Africa. Appl. Energy 161 (2016), 556–564.
91. Meehl, G.A., et al. Global Climate Projections. 2007.
92. Dee, D., et al. The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc. 137:656 (2011), 553–597.
93. Kalnay, E., et al. The NCEP/NCAR 40-year reanalysis project. Bull. Am. Meteorol. Soc. 77:3 (1996), 437–471.