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2016 | OriginalPaper | Chapter

Comparison Between Neuronal Networks and ANFIS for Wind Speed-Energy Forecasting

Authors : Helbert Espitia, Guzmán Díaz

Published in: Applied Computer Sciences in Engineering

Publisher: Springer International Publishing

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Abstract

The generation distributed systems are a good alternative for the reasonable use of energy. Moreover, neuronal networks are an appropriate option for modeling and control of nonlinear complex systems. The eolian energy has shown to be an alternative for electric power generation, even though it also presents limitations for proper management due to associated variations to weather conditions which affect wind speed. In this paper, considering the characteristics present in wind power, neuronal and neuro-fuzzy systems are suggested for the prediction of wind velocity associated whit wind power. The results show an adequate performance of neuro-fuzzy systems for forecasting of wind speed.

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Literature
1.
Dugan, R., McDermott, T., Ball, G.: Planning for distributed generation. IEEE Industry Application Magazine (2001)
2.
Tong, J., Yu, T.: Nonlinear PID control design for improving stability of micro-turbine systems. In: Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies DRPT, pp. 2515–2518 (2008)
3.
Mathew, S.: Wind Energy, Fundamentals, Resource Analysis and Economics. Springer, Heidelberg (2006)
4.
Heier, S., Waddington, R.: Grid Integration of Wind Energy Conversion Systems, 2nd edn. Wiley, Hoboken (2006)
5.
Khatamianfar, A., Khalid, M., Savkin, A., Agelidis, V.: Improving wind farm dispatch in the Australian electricity market with battery energy storage using model predictive control. IEEE Trans. Sustain. Energ. 4(3), 745–755 (2013)CrossRef
6.
Teleke, S., Baran, M., Huang, A., Bhattacharya, S., Anderson, L.: Control strategies for battery energy storage for wind farm dispatching. IEEE Trans. Energ. Convers. 24(3), 725–732 (2009)CrossRef
7.
Wang, X., Vilathgamuwa, D., Choi, S.: Determination of battery storage capacity in energy buffer for wind farm. IEEE Trans. Energ. Convers. 23(3), 868–878 (2008)CrossRef
8.
Holttinen, H., Hirvonen, R.: Power system requirements for wind power. In: Ackermann, T. (ed.) Wind Power in Power Systems, pp. 143–167. Wiley, New York (2005)
9.
Han, C., Huang, A., Baran, M., Bhattacharya, S., Litzenberger, W., Anderson, L., Johnson, A., Edris, A.: STATCOM impact study on the integration of a large wind farm into a weak loop power system. IEEE Trans. Energ. Convers. 23(1), 226–233 (2008)CrossRef
10.
Lundsager, P., Barring-Gould, E.: Isolated systems with wind power. In: Ackermann, T. (ed.) Wind Power in Power Systems, pp. 299–329. Wiley, New York (2005)
11.
Abdullah, M., Muttaqi, K., Sutanto, D., Agalgaonkar, A.: An effective power dispatch control strategy to improve generation schedulability and supply reliability of a wind farm using a battery energy storage system. IEEE Trans. Sustain. Energ. 6(3), 1093–1102 (2015)CrossRef
12.
Borowy, B., Salameh, Z.: Dynamic response to a stand-alone wind energy conversion system with battery energy storage to a wind gust. IEEE Trans. Energ. Convers. 12, 73–78 (1997)CrossRef
13.
Huang, C.: Modified neural network for dynamic control and operation of a hybrid generation systems. J. Appl. Res. Technol. 12(6) (2014)
14.
Dowell, J., Pinson, P.: Very-short-term probabilistic wind power forecasts by sparse vector autoregression. IEEE Trans. Smart Grid 7(2), 763–770 (2015)
15.
Hyndman, R., Athanasopoulos, G.: Forecasting: principles and practice. OTexts, Melbourne (2014)
16.
Huarng, K., Yu, T.: The application of neural networks to forecast fuzzy time series. Physica A. Stat. Mech. Appl. 363(2), 481–491 (2006)CrossRef
17.
Peng, H., Wu, S., Wei, C., Lee, S.: Time series forecasting with a neuro-fuzzy modeling scheme. Appl. Soft Comput. 32, 481–493 (2015)CrossRef
18.
Singh, M., Singh, I., Verma, A.: Identification on non linear series-parallel model using neural network. MIT Int. J. Electr. Instrumen. Eng. 3(1), 21–23 (2013)
19.
Torres, N., Hernandez, C., Pedraza, L.: Redes neuronales y predicción de tráfico. Revista Tecnura 15(29), 90–97 (2011)
20.
Martinez, F., Gómez, D., Castiblanco, M.: Optimization of a neural architecture for the direct control of a Boost converter. Revista Tecnura 16(32), 41–49 (2012)CrossRef
21.
Arrieta, J., Torres, J., Velásquez, H.: Predicciones de modelos econométricos y redes neuronales: el caso de la acción de SURAMINV. Semestre Económico 12(25), 95–109 (2009)
22.
Pérez, F., Fernández, H.: Las redes neuronales y la evaluación del riesgo de crédito. Revista Ingenierpias Universidad de Medellín 6(10), 77–91 (2007)
23.
Heaton, J.: Introduction to Neural Networks with Java. Heaton Research Inc. (2008)
24.
Wang, L.: A Course on Fuzzy Systems and Control. Prentice Hall PTR, New Jersey (1997)
25.
Del Brio, B., Sanz, A.: Redes Neuronales y Sistemas Difusos. Alfaomega, Segunda Edición (2006)
26.
Babuska, R.: Fuzzy Modeling for Control. Kluwer Academic Publishers, Wiley, London, England (1998)
27.
Jang, J., Sun, C., Mizutani, E.: Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Matlab Curriculum Series (1990)
Metadata
Title
Comparison Between Neuronal Networks and ANFIS for Wind Speed-Energy Forecasting
Authors
Helbert Espitia
Guzmán Díaz
Copyright Year
2016
Book
Applied Computer Sciences in Engineering

Print ISBN: 978-3-319-50879-5

Electronic ISBN: 978-3-319-50880-1

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-50880-1_9

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