Very Short-Term Generating Power Forecasting for Wind Power Generators Based on Time Series Analysis

Affiliation(s)

University of the Ryukyus, Okinawa, Japan.

Meidensha Corporation, Tokyo, Japan.

Sungkyunkwan University, Suwon City, South Korea..

University of the Ryukyus, Okinawa, Japan.

Meidensha Corporation, Tokyo, Japan.

Sungkyunkwan University, Suwon City, South Korea..

ABSTRACT

In recent years, there has been introduction of alternative energy sources such as wind energy. However, wind speed is not constant and wind power output is proportional to the cube of the wind speed. In order to control the power output for wind power generators as accurately as possible, a method of wind speed estimation is required. In this paper, a technique considers that wind speed in the order of 1 - 30 seconds is investigated in confirming the validity of the Auto Regressive model (AR), Kalman Filter (KF) and Neural Network (NN) to forecast wind speed. This paper compares the simulation results of the forecast wind speed for the power output forecast of wind power generator by using AR, KF and NN.

In recent years, there has been introduction of alternative energy sources such as wind energy. However, wind speed is not constant and wind power output is proportional to the cube of the wind speed. In order to control the power output for wind power generators as accurately as possible, a method of wind speed estimation is required. In this paper, a technique considers that wind speed in the order of 1 - 30 seconds is investigated in confirming the validity of the Auto Regressive model (AR), Kalman Filter (KF) and Neural Network (NN) to forecast wind speed. This paper compares the simulation results of the forecast wind speed for the power output forecast of wind power generator by using AR, KF and NN.

Cite this paper

A. Yona, T. Senjyu, F. Toshihisa and C. Kim, "Very Short-Term Generating Power Forecasting for Wind Power Generators Based on Time Series Analysis,"*Smart Grid and Renewable Energy*, Vol. 4 No. 2, 2013, pp. 181-186. doi: 10.4236/sgre.2013.42022.

A. Yona, T. Senjyu, F. Toshihisa and C. Kim, "Very Short-Term Generating Power Forecasting for Wind Power Generators Based on Time Series Analysis,"

References

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[19] M. A. H. El-Sayed, “Substitution Potential of Wind Energy in Egypt,” Energy Policy, Vol. 30, No. 8, 2002, pp. 681-687. doi:10.1016/S0301-4215(02)00030-7

[20] M. A. Elhadidy and S. M. Shaahid, “Decentralized/StandAlone Hybrid Wind-Diesel Power Systems to Meet Residential Loads of Hot Coastal Regions,” Energy Conversion and Management, Vol. 46, No. 15-16, 2005, pp. 2501-2503. doi:10.1016/j.enconman.2004.11.010

[21] S. M. Shaahid, L. M. Al-Hadhrami and M. K. Rahman, “Economic Feasibility of Development of Wind Power Plants in Coastal Locations of Saudi Arabia—A Review,” Renewable and Sustainable Energy Reviews, Vol. 19, No. C, 2013, pp. 589-587. doi:10.1016/j.rser.2012.11.058

[1] A. C. N. Salles, A. C. G. Melo and L. F. L. Legey, “Risk Analysis Methodologies for Financial Evaluation of Wind Energy Power Generation Projects in the Brazilian System,” 2004 International Conference on Probabilistic Methods Applied to Power Systems, 16 September 2004, pp. 457-462.

[2] D. E. Gustafson and W. H. Ledsham, “Applications of Estimation Theory to Inverse Problems in Meteorology,” 1978 IEEE Conference on Decision and Control Including the 17th Symposium on Adaptive Processes, Vol. 17, No. 1, 1978, pp. 405-412.

[3] L. Hui, K. L. Shi and P. G. McLaren, “Neural-NetworkBased Sensorless Maximum Wind Energy Capture with Compensated Power Coefficient,” IEEE Transactions on Industry Applications, Vol. 41, No. 6, 2005, pp. 15481556. doi:10.1109/TIA.2005.858282

[4] G. Galanis, P. Louka, P. Katsafados, I. Pytharoulis and G. Kallos, “Applications of Kalman Filters Based on NonLinear Functions to Numerical Weather Predictions,” Annales Geophysicae, No. 24, 2006, pp. 1-10.

[5] M. A. Mohandes, S. Rehman and T. O. Halawani, “A Neural Networks Approach for Wind Speed Prediction,” Renewable Energy, Vol. 13, No. 3, 1998, pp. 345-354. doi:10.1016/S0960-1481(98)00001-9

[6] T. Toumiya, E. Sakaguchi, T. Tanaka and T. Suzuki, “Modeling of Wind Power Generating System with a Propeller Type Windmill by Neural Networks,” The Institute of Electrical Engineers of Japan, Vol. 118-B, No. 7-8, 1998, pp. 794-801. (in Japanese)

[7] Y. Kemmoku, H. Ishii, H. Takikawa, T. Kawamoto and T. Sakakibara, “Method of Forecasting Wind Velocity of Next Day Using Weather Data over Wide Area,” Journal of Japan Solar Energy Society, Vol. 27, No. 1, 2001, pp. 85-91. (in Japanese)

[8] P. Flores, A. Tapia and G. Tapia, “Application of a Control Algorithm for Wind Speed Prediction and Power Generation,” Renewable Energy, Vol. 30, No. 5, 2004, pp. 523-536.

[9] S. Kelouwani and K. Agbossou, “Nonlinear Model Identification of Wind Turbine with a Neural Network,” IEEE Transactions on Energy Conversion, Vol. 19, No. 3, 2004, pp. 607-612. doi:10.1109/TEC.2004.827715

[10] A. Oztopal, “Artificial Neural Network Approach to Spatial Estimation of Wind Velocity Data,” Energy Conversion and Management, Vol. 47, No. 4, 2006, pp. 395-406. doi:10.1016/j.enconman.2005.05.009

[11] J. L. Elman, “Finding Structure in Time,” Cognitive Science, Vol. 14, No. 2, 1990, pp. 179-211. doi:10.1207/s15516709cog1402_1

[12] J.-H. Li, A. N. Michel and W. Porod, “Analysis and Synthesis of a Class of Neural Networks: Linear Systems Operating on a Closed Hypercube,” IEEE Transactions on Circuits and Systems, Vol. 36, No. 11, 1989, pp. 14051422. doi:10.1109/31.41297

[13] G. N. Kariniotakis, G. S. Stavrakakis and E. F. Nogaret, “Wind Power Forecasting Using Advanced Neural Networks Models,” IEEE Transactions on Energy Conversion, Vol. 11, No. 4, 1996, pp. 762-767. doi:10.1109/60.556376

[14] T. G. J. Barbounis, B. Thecharis, 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

[15] Y. Kitamura and A. Matsuda, “Study on Raising Efficiency of Heat Accumulating Air Conditioning System Using Knowledge Processing Techniques,” Journal of Mitsubishi Research Institute, No. 36, 2000, pp. 31-51. (in Japanese)

[16] B. Kermanshahi, “Recurrent Neural Network for Forecasting Next 10 Years Loads of Nine Japanese Utilities,” Neurocomputing, Vol. 23, No. 1-3, 1998, pp. 125-133. doi:10.1016/S0925-2312(98)00073-3

[17] M. T. Hagan and M. B. Menhaj, “Training Feed-Forward Networks with the Marquardt Algorithm,” IEEE Transactions on Neural Networks, Vol. 5, No. 6, 1994, pp. 989993. doi:10.1109/72.329697

[18] V. D. Hoven, “Power Spectrum of Horizontal Wind Speed in the Frequency Range from 0.007 to 900 Cycles,” Journal of Meteorology, Vol. 14, No. 2, 1957, p. 160. doi:10.1175/1520-0469(1957)014<0160:PSOHWS>2.0.CO;2

[19] M. A. H. El-Sayed, “Substitution Potential of Wind Energy in Egypt,” Energy Policy, Vol. 30, No. 8, 2002, pp. 681-687. doi:10.1016/S0301-4215(02)00030-7

[20] M. A. Elhadidy and S. M. Shaahid, “Decentralized/StandAlone Hybrid Wind-Diesel Power Systems to Meet Residential Loads of Hot Coastal Regions,” Energy Conversion and Management, Vol. 46, No. 15-16, 2005, pp. 2501-2503. doi:10.1016/j.enconman.2004.11.010

[21] S. M. Shaahid, L. M. Al-Hadhrami and M. K. Rahman, “Economic Feasibility of Development of Wind Power Plants in Coastal Locations of Saudi Arabia—A Review,” Renewable and Sustainable Energy Reviews, Vol. 19, No. C, 2013, pp. 589-587. doi:10.1016/j.rser.2012.11.058