An alternative electric power source, such as wind power, has to be both reliable and autonomous. An accurate wind speed forecasting method plays the key role in achieving the aforementioned properties and also is a valuable tool in overcoming a variety of economic and technical problems connected to wind power production. The method proposed is based on the reformulation of the problem in the standard state space form and on implementing a bank of Kalman filters (KF), each fitting an ARMA model of different order. The proposed method is to be applied to a greenhouse unit which incorporates an automatized use of renewable energy sources including wind speed power. 1. Introduction Energy is considered amongst the most significant factors that are closely related to both economic and social developments. It is also a fact that nowadays the majority of the electrical energy production is based on the fossil fuels, which on one hand are, without any doubt, highly efficient but on the other are responsible for the emission of greenhouse gases and their reserves are limited. Consequently renewable sources of energy, such as wind, biomass, solar power, and wave power, have been already adopted for electric power production. It is well known that the wind power generation raises issues of reliability due to the fact that the wind speed is significantly and directly affected by various factors such as the type of the terrain, the height, season of the year, atmospheric conditions, obstacles present, and many more. This leads to the conclusion that unless the reliability of the wind power generation is at an acceptable level, wind power is not eligible for constant electrical energy supply to the power system [1, 2]. Recent studies have shown that combined forecasting methods can offer robust solutions and can be efficiently implemented to various real-life problems in diverging fields such as chemical processes, economics, load forecasting, tourism demand, environmental issues, medicine, and many more [3–7]. In this study a hybrid model is presented that reveals the advantages of an ARMA and SVM model in wind speed modelling and prediction problem. Initially successful model identification and parameter estimation have to be performed in order to choose the most appropriate ARMA models. For tackling this task the well-established MMPA was used. This approach was introduced by Lainiotis [8, 9] and summarizes the parametric model uncertainty into an unknown, finite dimensional parameter vector whose values are assumed to lie within a known set of finite
References
[1]
M. Lei, L. Shiyan, J. Chuanwen, L. Hongling, and Z. Yan, “A review on the forecasting of wind speed and generated power,” Renewable and Sustainable Energy Reviews, vol. 13, no. 4, pp. 915–920, 2009.
[2]
M. Marciukaitis, V. Katinas, and A. Kavaliauskas, “Wind power usage and prediction prospects in Lithuania,” Renewable and Sustainable Energy Reviews, vol. 12, no. 1, pp. 265–277, 2008.
[3]
C.-H. Wen and C. A. Vassiliadis, “Applying hybrid artificial intelligence techniques in wastewater treatment,” Engineering Applications of Artificial Intelligence, vol. 11, no. 6, pp. 685–705, 1998.
[4]
M. Shafie-Khah, M. P. Moghaddam, and M. K. Sheikh-El-Eslami, “Price forecasting of day-ahead electricity markets using a hybrid forecast method,” Energy Conversion and Management, vol. 52, no. 5, pp. 2165–2169, 2011.
[5]
K.-Y. Chen, “Combining linear and nonlinear model in forecasting tourism demand,” Expert Systems with Applications, vol. 38, no. 8, pp. 10368–10376, 2011.
[6]
B. Baruque, E. Corchado, A. Mata, and J. M. Corchado, “A forecasting solution to the oil spill problem based on a hybrid intelligent system,” Information Sciences, vol. 180, no. 10, pp. 2029–2043, 2010.
[7]
W.-C. Yeh, W.-W. Chang, and Y. Y. Chung, “A new hybrid approach for mining breast cancer pattern using discrete particle swarm optimization and statistical method,” Expert Systems with Applications, vol. 36, no. 4, pp. 8204–8211, 2009.
[8]
D. G. Lainiotis, “Partitioning: a unifying framework for adaptive systems—I: estimation,” Proceedings of the IEEE, vol. 64, no. 8, pp. 1126–1143, 1976.
[9]
D. G. Lainiotis, “A unifying framework for adaptive systems—II: control,” Proceedings of the IEEE, vol. 64, no. 8, pp. 1182–1198, 1976.
[10]
S. S. Pappas, A. K. Leros, and S. K. Katsikas, “Joint order and parameter estimation of multivariate autoregressive models using multi-model partitioning theory,” Digital Signal Processing, vol. 16, no. 6, pp. 782–795, 2006.
[11]
S. K. Katsikas, S. D. Likothanassis, G. N. Beligiannis, K. G. Berketis, and D. A. Fotakis, “Genetically determined variable structure multiple model estimation,” IEEE Transactions on Signal Processing, vol. 49, no. 10, pp. 2253–2261, 2001.
[12]
P. Papaparaskeva, “A partitioned adaptive approach to nonlinear channel equalization demetrios G. lainiotis and,” IEEE Transactions on Communications, vol. 46, no. 10, pp. 1325–1336, 1998.
[13]
V. C. Moussas, S. D. Likothanassis, S. K. Katsikas, and A. K. Leros, “Adaptive on-line multiple source detection,” in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '05), pp. 1029–1032, March 2005.
[14]
D. G. Lainiotis, K. S. Katsikas, and S. D. Likothanasis, “Adaptive deconvolution of seismic signals—performance, computational analysis, parallelism,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 36, no. 11, pp. 1715–1734, 1988.
[15]
N. V. Nikitakos, A. K. Leros, and S. K. Katsikas, “Towed array shape estimation using multimodel partitioning filters,” IEEE Journal of Oceanic Engineering, vol. 23, no. 4, pp. 380–384, 1998.
[16]
V. C. Moussas and S. S. Pappas, “Adaptive network anomaly detection using bandwidth utilization data,” in Proceedings of the 1st International Conference on Experiments/Processes/System Modelling/Simulation/Optimization, Athens, Greece, 2005.
[17]
S. S. Pappas, L. Ekonomou, P. Karampelas, S. K. Katsikas, and P. Liatsis, “Modeling of the grounding resistance variation using ARMA models,” Simulation Modelling Practice and Theory, vol. 16, no. 5, pp. 560–570, 2008.
[18]
S. S. Pappas, L. Ekonomou, D. C. Karamousantas, G. E. Chatzarakis, S. K. Katsikas, and P. Liatsis, “Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models,” Energy, vol. 33, no. 9, pp. 1353–1360, 2008.
[19]
S. S. Pappas, L. Ekonomou, P. Karampelas et al., “Electricity demand load forecasting of the Hellenic power system using an ARMA model,” Electric Power Systems Research, vol. 80, no. 3, pp. 256–264, 2010.
[20]
E. Peter Carden and J. M. W. Brownjohn, “ARMA modelled time-series classification for structural health monitoring of civil infrastructure,” Mechanical Systems and Signal Processing, vol. 22, no. 2, pp. 295–314, 2008.
[21]
Y. Chen, H. Zhao, and L. Yu, “Demand forecasting in automotive aftermarket based on ARMA model,” in Proceedings of the International Conference on Management and Service Science (MASS '10), pp. 1–4, 2010.
[22]
Z. Jie, X. Limei, and L. Lin, “Equipment fault forecasting based on ARMA model,” in Proceedings of the IEEE International Conference on Mechatronics and Automation (ICMA '07), pp. 3514–3518, August 2007.
[23]
R. Majhi, S. Mishra, B. Majhi, G. Panda, and M. Rout, “Efficient sales forecasting using PSO based adaptive ARMA model,” in Proceedings of the World Congress on Nature and Biologically Inspired Computing (NABIC '09), pp. 1333–1337, December 2009.
[24]
F. Li and P. Luan, “ARMA model for predicting the number of new outbreaks of newcastle disease during the month,” in Proceedings of the IEEE International Conference on Computer Science and Automation Engineering (CSAE '11), vol. 4, pp. 660–663, June 2011.
[25]
S. S. Pappas, L. Ekonomou, V. C. Moussas, P. Karampelas, and S. K. Katsikas, “Adaptive load forecasting of the Hellenic electric grid,” Journal of Zhejiang University: Science A, vol. 9, no. 12, pp. 1724–1730, 2008.
[26]
G. Beligiannis, L. Skarlas, and S. Likothanassis, “A generic applied evolutionary hybrid technique,” IEEE Signal Processing Magazine, vol. 21, no. 3, pp. 28–38, 2004.
[27]
S. S. Pappas, N. Harkiolakis, P. Karampelas, L. Ekonomou, and S. K. Katsikas, “A new algorithm for on-line Multivariate ARMA identification using Multimodel Partitioning Theory,” in Proceedings of the 12th Pan-Hellenic Conference on Informatics (PCI '08), pp. 222–226, August 2008.
[28]
V. C. Moussas, S. K. Katsikas, and D. G. Lainiotis, “Adaptive estimation of FCG using nonlinear state-space models,” Stochastic Analysis and Applications, vol. 23, no. 4, pp. 705–722, 2005.
[29]
V. Vapnik, The Nature of Statistic Learning Theory, Springer, New York, NY, 1995.
