Hybrid Prediction Method for Solar Power Using Different Computational Intelligence Algorithms

Affiliation(s)

Power Engineering Research Group (PERG), Central Queensland University, Rockhampton, Australia..

Power Engineering Research Group (PERG), Central Queensland University, Rockhampton, Australia..

ABSTRACT

Computational Intelligence (CI) holds the key to the development of smart grid to overcome the challenges of planning and optimization through accurate prediction of Renewable Energy Sources (RES). This paper presents an architectural framework for the construction of hybrid intelligent predictor for solar power. This research investigates the applicability of heterogeneous regression algorithms for 6 hour ahead solar power availability forecasting using historical data from Rockhampton, Australia. Real life solar radiation data is collected across six years with hourly resolution from 2005 to 2010. We observe that the hybrid prediction method is suitable for a reliable smart grid energy management. Prediction reliability of the proposed hybrid prediction method is carried out in terms of prediction error performance based on statistical and graphical methods. The experimental results show that the proposed hybrid method achieved acceptable prediction accuracy. This potential hybrid model is applicable as a local predictor for any proposed hybrid method in real life application for 6 hours in advance prediction to ensure constant solar power supply in the smart grid operation.

Cite this paper

M. Hossain, A. Oo and A. Ali, "Hybrid Prediction Method for Solar Power Using Different Computational Intelligence Algorithms,"*Smart Grid and Renewable Energy*, Vol. 4 No. 1, 2013, pp. 76-87. doi: 10.4236/sgre.2013.41011.

M. Hossain, A. Oo and A. Ali, "Hybrid Prediction Method for Solar Power Using Different Computational Intelligence Algorithms,"

References

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[28] S. Shevade, S. Keerthi, C. Bhattacharyya and K. Murthy, “Improvements to the SMO Algorithm for SVM Regression,” IEEE Transactions on Neural Networks, Vol. 11, No. 5, 2000, pp. 1188-1183. doi:10.1109/72.870050

[29] R. J. Hyndman and A. B. Koehler, “Another Look at Measures of Forecast Accuracy,” International Journal of Forecasting, Vol. 22, No. 4, 2005, pp. 679-688.

[30] C. J. Willmott and K. Matsuura, “Advantages of the Mean Absolute Error (MAE) over the Root Mean Square Error (RMSE) in Assessing Average Model Performance,” Climate Research, Vol. 30, No. 1, 2005, pp. 79-82. doi:10.3354/cr030079

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[33] D. Monn, D. Christenson and R. Chevallaz-Perrier, “Support Vector Machines Technology Coupled with Physics-Based Modeling for Wind Facility Power Production Forecasting,” CD-Proceedings of the Global Wind Power Conference, Chicago, 28-31 March 2004, pp. 97-102.

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[38] N. Sharma, P. Sharma, D. Irwin and P. Shenoy, “Predicting Solar Generation from Weather Forecasts Using Machine Learning,” Proceedings of the 2nd IEEE International Conference on Smart Grid Communications, Brussels, 17-20 October 2011, pp. 32-37.

[39] K. R. Müller, A. Smola, G. R?tsch, B. Sch?lkopf, J. Kohlmorgen and V. Vapnik, “Using Support Vector Machines for Time Series Prediction,” In: B. Sch?lkopf, C. Burges and A. Smola, Eds., Advances in Kernel Methods—Support Vector Learning, MIT Press, Cambridge, 1998.

[40] O. Kramer, B. Satzger and J. L?ssig, “Power Prediction in Smart Grids with Evolutionary Local Kernel Regression,” International Computer Science Institute, Berkeley. http://www.icsi.berkeley.edu/pubs/algorithms/powerprediction10.pdf

[41] P. J. Rousseeuw, “Least Median of Squares Regression,” Journal of American Statistics, Vol. 79, No. 388, 1984, pp. 871-880. doi:10.1080/01621459.1984.10477105

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[43] K. M. Hornik, M. Stinchcombe and H. White, “Multilayer Feed Forward Networks Are Universal Approximator,” Neural Networks, Vol. 2, No. 2, 1989, pp. 359-366. doi:10.1016/0893-6080(89)90020-8

[44] R. Fletcher, “Practical Methods of Optimization,” 2nd Edition, Wiley, Chichester, 1990.

[1] B. Parsons, M. Milligan, B. Zavadil, D. Brooks, B. Kirby, K. Dragoon and J. Caldwell, “Grid Impacts of Wind Power: A Summary of Recent Studies in the United States,” National Renewable Energy Laboratory, Madrid, 2003.

[2] R. Doherty and M. O’Malley, “Quantifying Reserve Demands Due to Increasing Wind Power Penetration,” 2003 IEEE Bologna Power Tech Conference Proceedings, Bologna, Vol. 2, 23-26 June 2003, pp. 23-26.

[3] R. Doherty and M. O’Malley, “A New Approach to Quantify Reserve Demand in Systems with Significant Installed Wind Capacity,” IEEE Transactions on Power Systems, Vol. 20, No. 2, 2005, pp. 587-595. doi:10.1109/TPWRS.2005.846206

[4] N. Hatziargyriou, A. Tsikalakis, A. Dimeas, D. Georgiadis, A. Gigantidou, J. Stefanakis and E. Thalassinakis, “Security and Economic Impacts of High Wind Power Penetration in Island Systems,” Proceedings of Cigre Session, Paris, 30 August-3 September 2004, pp. 1-9.

[5] N. Hatziargyriou, G. Contaxis, M. Matos, J. A. P. Lopes, G. Kariniotakis, D. Mayer, J. Halliday, G. Dutton, P. Dokopoulos, A. Bakirtzis, J. Stefanakis, A. Gigantidou, P. O’Donnell, D. McCoy, M. J. Fernandes, J. M. S. Cotrim and A. P. Figueira, “Energy Management and Control of Island Power Systems with Increased Penetration from Renewable Sources,” Power Engineering Society Winter Meeting, Vol. 1, No. 27-31, 2002, pp. 335-339.

[6] E. D. Castronuovo and J. A. P. Lopes, “On the Optimization of the Daily Operation of a Wind-Hydro Power Plant,” IEEE Transactions on Power Systems, Vol. 19, No. 3, 2004, pp. 1599-1606. doi:10.1109/TPWRS.2004.831707

[7] M. C. Alexiadis, P. S. Dokopoulos, and H. S. Sahsamanoglou, “Wind Speed and Power Forecasting Based on Spatial Correlation Models,” IEEE Transactions on Energy Conversion, Vol. 14, No. 3, 1999, pp. 836-842. doi:10.1109/60.790962

[8] T. G. Barbounis, J. B. Theocharis, M. C. Alexiadis and P. S. Dokopoulos, “Long-Term Wind Speed and Power Forecasting Using Local Recurrent Neural Network Models,” IEEE Transactions on Energy Conversion, Vol. 21, No. 1, 2006, pp. 273-284. doi:10.1109/TEC.2005.847954

[9] L. Landberg, G. Giebel, H. A. Nielsen, T. Nielsen and H. Madsen, “Short-Term Prediction—An Overview,” Wind Energy, Vol. 6, No. 3, 2003, pp. 273-280. doi:10.1002/we.96

[10] G. Giebel, L. Landberg, G. Kariniotakis and R. Brownsword, “State-of-the-Art on Methods and Software Tools for Short-Term Prediction of Wind Energy Production,” EWEC, Madrid, 16-19 June 2003, pp. 27-36.

[11] G. Giebel, R. Brownsword and G. Kariniotakis, “The State of the Art in Short-Term Prediction of Wind Power: A Literature Overview,” 2003. http://130.226.56.153/rispubl/vea/veapdf/ANEMOS_giebel.pdf

[12] J. M. Gordon and T. A. Reddy, “Time Series Analysis of Hourly Global Horizontal Solar Radiation,” Solar Energy, Vol. 41, No. 5, 1988, pp. 423-429.

[13] J. M. Gordon and T. A. Reddy, “Time Series Analysis of Daily Horizontal Solar Radiation,” Solar Energy, Vol. 41, No. 3, 1988, pp. 215-226. doi:10.1016/0038-092X(88)90139-9

[14] K. M. Knight, S. A. Klein and J. A. Duffie, “A Methodology for the Synthesis of Hourly Weather Data,” Solar Energy, Vol. 46, No. 2, 1991, pp. 109-120. doi:10.1016/0038-092X(91)90023-P

[15] T. R. Morgan, “The Performance and Optimization of Autonomous Renewable Energy Systems,” Ph.D. Thesis, University of Wales, Cardiff, 1996.

[16] N. Jankowski, K. Gr?bczewski and W. Duch, “Hybrid Systems, Ensembles and Meta-Learning Algorithms,” 2008. http://www.is.umk.pl/WCCI-HSEMLA/

[17] S. B. Kotsiantis and P. E. Pintelas, “Predicting Students’ Marks in Hellenic Open University,” Proceedings of 5th IEEE International Conference on Advanced Learning Technologies, Kaohsiung, 5-8 July 2005, pp. 664-668.

[18] S. Kotsiantis, G. Tsekouras, C. Raptis and P. Pintelas, “Modelling the Organoleptic Properties of Matured Wine Distillates,” MLDM’05 Proceedings of the 4th International Conference on Machine Learning and Data Mining in Pattern Recognition, Vol. 3587, 2005, pp. 667-673.

[19] A. Sharkey, N. Sharkey, U. Gerecke and G. Chandroth, “The Test and Select Approach to Ensemble Combination,” Springer-Verlag, Cagliari, 2000.

[20] N. L. Hjort and G. Claeskens, “Frequentist Model Average Estimators,” Journal of the American Statistical Association, Vol. 98, No. 464, 2003, pp. 879-899. doi:10.1198/016214503000000828

[21] L. Breiman, “Bagging Predictors,” Machine Learning, Vol. 24, No. 2, 1996, pp. 123-140. doi:10.1007/BF00058655

[22] R. Zemel and T. Pitassi, “A Gradient-Based Boosting Algorithm for Regression Problems,” MIT Press, Cambridge, 2001.

[23] G. Brown, J. Wyatt and P. Tino, “Managing Diversity in Regression Ensembles,” Journal of Machine Learning Research, Vol. 6, No. 1, 2005, pp. 1621-1650.

[24] U. Naftaly, N. Intrator and D. Horn, “Optimal Ensemble Averaging of Neural Networks,” Network, Vol. 8, No. 3, 1997, pp. 283-296. doi:10.1088/0954-898X/8/3/004

[25] P. Langley, “Selection of Relevant Features in Machine Learning,” AAAI Press, New Orleans, 1994.

[26] J. Harburn, “RECs/STCs-What Are They and How Are They Calculated?” http://www.solarchoice.net.au/blog/recs-what-are-they-and-how-are-they-calculated/

[27] R. Remco, Bouckaert, E. Frank, M. Hall, R. Kirkby, P. Reutemann, A. Seewald and D. Scuse, “WEKA Manual for Version 3-7-3,” The University of Waikato, New Zealand, 2010.

[28] S. Shevade, S. Keerthi, C. Bhattacharyya and K. Murthy, “Improvements to the SMO Algorithm for SVM Regression,” IEEE Transactions on Neural Networks, Vol. 11, No. 5, 2000, pp. 1188-1183. doi:10.1109/72.870050

[29] R. J. Hyndman and A. B. Koehler, “Another Look at Measures of Forecast Accuracy,” International Journal of Forecasting, Vol. 22, No. 4, 2005, pp. 679-688.

[30] C. J. Willmott and K. Matsuura, “Advantages of the Mean Absolute Error (MAE) over the Root Mean Square Error (RMSE) in Assessing Average Model Performance,” Climate Research, Vol. 30, No. 1, 2005, pp. 79-82. doi:10.3354/cr030079

[31] V. Vapnik and A. Lerner, “Pattern Recognition Using Generalized Portrait Method,” Automation and Remote Control, Vol. 24, No. 6, 1963, pp. 774-780.

[32] K. Larson and T. Gneiting, “Advanced Short-Range Wind Energy Forecasting Technologies-Challenges, Solutions and Validation,” CD-Proceedings of the Global Wind Power Conference, Chicago, 28-31 March 2004, pp. 67-73.

[33] D. Monn, D. Christenson and R. Chevallaz-Perrier, “Support Vector Machines Technology Coupled with Physics-Based Modeling for Wind Facility Power Production Forecasting,” CD-Proceedings of the Global Wind Power Conference, Chicago, 28-31 March 2004, pp. 97-102.

[34] G. Kariniotakis, “Estimation of the Uncertainty in Wind Power Forecasting,” Ph.D. Thesis, Department of Energy, Ecole des Mines de Paris, Paris, 2006.

[35] M. Mohandes, T. Halawani and R. A. Hussain, “Support Vector Machines for Wind Speed Prediction,” Renewable Energy, Vol. 29, No. 6, 2004, pp. 939-947. doi:10.1016/j.renene.2003.11.009

[36] J. Zeng and W. Qiao, “Support Vector Machine-Based Short-Term Wind Power Forecasting,” Proceedings of the IEEE PES Power System Conference and Exposition, Arizona, 20-23 March 2011, pp. 1-8.

[37] J. Shi, W. Lee, Y. Liu, Y. Yang and P. Wang, “Forecasting Power Output of Photovoltaic System Based on Weather Classification and Support Vector Machine,” IEEE Industry Applications Society Annual Meeting (IAS), Orlando, 9-13 October 2011, pp. 1-6.

[38] N. Sharma, P. Sharma, D. Irwin and P. Shenoy, “Predicting Solar Generation from Weather Forecasts Using Machine Learning,” Proceedings of the 2nd IEEE International Conference on Smart Grid Communications, Brussels, 17-20 October 2011, pp. 32-37.

[39] K. R. Müller, A. Smola, G. R?tsch, B. Sch?lkopf, J. Kohlmorgen and V. Vapnik, “Using Support Vector Machines for Time Series Prediction,” In: B. Sch?lkopf, C. Burges and A. Smola, Eds., Advances in Kernel Methods—Support Vector Learning, MIT Press, Cambridge, 1998.

[40] O. Kramer, B. Satzger and J. L?ssig, “Power Prediction in Smart Grids with Evolutionary Local Kernel Regression,” International Computer Science Institute, Berkeley. http://www.icsi.berkeley.edu/pubs/algorithms/powerprediction10.pdf

[41] P. J. Rousseeuw, “Least Median of Squares Regression,” Journal of American Statistics, Vol. 79, No. 388, 1984, pp. 871-880. doi:10.1080/01621459.1984.10477105

[42] S. A. Kalogirou, “Artificial Neural Networks in Renewable Energy Systems Applications: A Review,” Renewable and Sustainable Energy Reviews, Vol. 5, No. 4, 2001, pp. 373-401.

[43] K. M. Hornik, M. Stinchcombe and H. White, “Multilayer Feed Forward Networks Are Universal Approximator,” Neural Networks, Vol. 2, No. 2, 1989, pp. 359-366. doi:10.1016/0893-6080(89)90020-8

[44] R. Fletcher, “Practical Methods of Optimization,” 2nd Edition, Wiley, Chichester, 1990.