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Soft Computing in Green and Renewable Energy Systems pp 97–116Cite as

A Comprehensive Overview of Short Term Wind Forecasting Models Based on Time Series Analysis

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Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 269))

Abstract

This chapter presents a comprehensive overview of short term wind forecasting models based on time series analysis. Several different approaches, presently considered as mature, are re-examined with an eye towards setting automated procedures to clarify grey areas in their application. Additionally, some approaches recently proposed in the literature are examined that include the application of localized linear models, and clustering algorithms coupled with linear and nonlinear models. Additionally, the impact of changing synoptic weather characteristics is captured, through the utilization of global meteorological variables and the subsequent development of a customized regime model. The application of the developed approach on an annual hourly wind speed data set is presented.

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References

  • Abdel-Aal, R.E., Elhadidy, M.A., Shaahid, S.M.: Modeling and forecasting the mean hourly wind speed time series using GMDH-based abductive networks. Renewable Energy 34(7), 1686–1699 (2009)

    Article  Google Scholar 

  • Alexiadis, M.C., Dokopoulos, P.S., Sahsamanoglou, H.S., Manousaridis, I.M.: Short term forecasting of wind speed and related electric power. Solar Energy 63(1), 61–68 (1998)

    Article  Google Scholar 

  • Argonne National Laboratory (ANL 2009), Wind Power Forecasting: State-of-the-Art, ANL/DIS-10-1

    Google Scholar 

  • Barbounis, T.G., Theocharis, J.B.: Long-term wind speed and power forecasting using local recurrent neural network models. IEEE Transactions on Energy Conversion 21(1), 273–284 (2006)

    Article  Google Scholar 

  • Bossanyi, E.: Short-Term Wind Prediction Using Kalman Filters. Wind Engineering 9(1), 1–8 (1985)

    Google Scholar 

  • Contaxis, G.A., Kabouris, J.: Short term scheduling in a wind/diesel autonomous energy syste. IEEE Tr. on Power Systems 6(3), 1161–1167 (1991)

    Article  Google Scholar 

  • Costa, A., Crespo, A., Navarro, J., Lizcano, G., Madsen, H., Feitosa, E.: A review on the young history of the wind power short-term prediction. Renewable and Sustainable Energy 12, 1725–1744 (2008)

    Article  Google Scholar 

  • Costa, A., Crespo, A., Migoya, E.: First results from a prediction project. In: Proceedings of the European Wind Energy Conference, EWEC 2003, Madrid, Spain (2003)

    Google Scholar 

  • Damousis, I.G., Dokopoulos, P.: A fuzzy model expert system for the fore-casting of wind speed and power generation in wind farms. In: Proceedings of the IEEE Int. Conf. on Power Industry Computer Applications, PICA 2001, pp. 63–69 (2001)

    Google Scholar 

  • Damousis, I.G., Alexiadis, M.C., Theocharis, J.B., Dokopoulos, P.: A fuzzy model for wind speed prediction and power generation in wind farms using spatial correlation. IEEE Transactions on Energy Conversion 19(2), 352–361 (2004)

    Article  Google Scholar 

  • Dudhia, J., Grell, G., Stauffer, D.R.: A description of the fifth-generation Penn System/NCAR Mesoscale Model (MM5), NCAR Tech. Note NCAR/TN-39811A, p. 107 (1994)

    Google Scholar 

  • El-Fouly, T.H.M., El-Saadany, E.F., Salama, M.M.A.: Grey Predictor for Wind Energy Conversion Systems Output Power Prediction. IEEE Transactions on Power System 21(3), 1450–1452 (2006)

    Article  Google Scholar 

  • EWEA, Making 180 GW a reality by 2020, The European Wind Energy Association Position Paper (October 2007)

    Google Scholar 

  • EWEA. Pure Power: Wind Energy Scenarios up to 2030, The European Wind Energy Association (March 2008)

    Google Scholar 

  • Farmer, J.D., Sidorowich, J.J.: Predicting chaotic dynamics. In: Kelso, J.A.S., Mandell, A.J., Shlesinger, M.F. (eds.) Dynamic Patterns in Complex Systems, pp. 265–292. World Scientific, Singapore (1988)

    Google Scholar 

  • Frías, L., Gastón, M., Martí, I.: A new model for wind energy forecasting focused in the intra-daily markets. In: Proceedings of European Wind Energy Conference, EWEC 2007, Milan, Italy (2007)

    Google Scholar 

  • Fugon, F., Juban, J., Kariniotakis, G.: Data mining for Wind Power Forecasting. In: Proceedings of the European Wind Energy Conference, EWEC 2008, Brussels, Belgium (2008)

    Google Scholar 

  • Giebel, G., Kariniotakis, G., Brownsword, R.: State of the Art on Short-term Wind Power Prediction, ANEMOS Deliverable Report D1.1 (2003)

    Google Scholar 

  • Glahn, H.R., Lowry, D.A.: The use of Model Output Statistics (MOS) in objective weather forecasting. Journal of Applied Meteorology 11, 1202–1211 (1972)

    Google Scholar 

  • Hagan, M., Menhaj, M.: Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks 5, 989–993 (1996)

    Article  Google Scholar 

  • Jursa, R.: Wind power prediction with different artificial intelligence models. In: Proceedings of the European Wind Energy Conference, EWEC 2007, Milan, Italy (2007)

    Google Scholar 

  • Kariniotakis, G., et al.: ANEMOS: Development of a Next Generation Wind Power Forecasting System for the Large-Scale Integration of Onshore & Offshore Wind Farms. In: Proceedings of the European Wind Energy Conference & Exhibition, EWEC 2003, Madrid, Spain, June 16-19 (2003)

    Google Scholar 

  • Kusiak, A., Zheng, H.-Y., Song, Z.: Short-Term Prediction of Wind Farm Power: A Data-Mining Approach. IEEE Transactions on Energy Conversion 24(1), 125–136 (2009)

    Article  Google Scholar 

  • Kavasseri, R.G., Seetharaman, K.: Day-ahead wind speed forecasting using f-ARIMA models. Renewable Energy 34(5), 1388–1393 (2009)

    Article  Google Scholar 

  • Kim, D.J., Park, Y.W., Park, D.J.: A Novel Validity Index for Determination of the Optimal Number of Clusters. IEICE Tr. on Information and Systems E84(2), 281–285 (2001)

    Google Scholar 

  • Landberg, L., Giebel, G., Nielsen, H., Nielsen, T., Madsen, H.: Short-term Pre-diction – An Overview. Wind Energy 6(3), 273–280 (2003)

    Article  Google Scholar 

  • Leia, M., Shiyana, L., Chuanwen, J., Honglinga, L., Yana, Z.: A review on the forecasting of wind speed and generated power. Renewable and Sustainable Energy Reviews 13, 915–920 (2009)

    Article  Google Scholar 

  • Lin, C.T., Lee, C.S.: Neural fuzzy systems. A neuro-fuzzy synergism to intelligent systems. Prentice Hall, Englewood Cliffs (1996)

    Google Scholar 

  • Maqsood, I., Khan, M., Huang, G., Abdalla, R.: Application of soft comput-ing models to hourly weather analysis in southern Saskatchewan, Canada. Engineering Applications of Artificial Intelligence 18(1), 115–125 (2005)

    Article  Google Scholar 

  • McQueen, J.B.: Some Methods for Classification and Analysis of Multivari-ate Observations. In: Proc. of 5th Berkley Symposium on Mathematical Statistics and Probability, pp. 281–297 (1967)

    Google Scholar 

  • NERC (North American Electric Reliability Corporation) Accommodating High Levels of Variable Generation, Special Report (April 2009), http://www.nerc.com/news_pr.php?npr=283

  • Nielsen, T.S., Madsen, H., Nielsen, H., Landberg, L., Giebel, G.: Zephyr – The Prediction Models. In: Proceedings of the European Wind Energy Con-ference, EWEC 2001, Copenhagen, Denmark, July 2-6, pp. 868–871 (2001)

    Google Scholar 

  • Pinson, P., Christensen, L.E.A., Madsen, H., Sørensen, P., Donovan, M.H., Jensen, L.E.: Regime-switching modelling of the fluctuations of offshore wind generation. Journal of Wind Engineering & Industrial Aerodynamics 96(12), 2327–2347 (2008)

    Article  Google Scholar 

  • Poggi, P., Muselli, M., Notton, G., Cristofi, C., Louche, A.: Forecasting and simulating wind speed in Corsica by using an autoregressive model. Energy Conversion and Management 14(20), 3177–3196 (2003)

    Article  Google Scholar 

  • Potter, C.W., Negnevistky, M.: Very short-term wind forecasting for Tasmanian power generation. IEEE Transactions on Power Systems 21(2), 965–972 (2006)

    Article  Google Scholar 

  • Ramírez-Rosado, I.J., Fernández-Jiménez, L.A.: Next-day wind farm electric energy generation forecasting using fuzzy time-series. In: Proceedings Int. Conf. on Modeling, Identification and Control, Innsbruck, Austria, pp. 237–240 (2003)

    Google Scholar 

  • Ramírez-Rosado, I.J., Fernández-Jiménez, L.A.: An advanced model for short-term forecasting of mean wind speed and wind electric power. Control and Intelligent Systems 31(1), 21–26 (2004)

    Google Scholar 

  • Sfetsos, A.: A comparison of various forecasting techniques applied to mean hourly wind speed time series. Renewable Energy 21(1), 23–35 (2000)

    Article  Google Scholar 

  • Sfetsos, A.: A novel approach for the forecasting of mean hourly wind speed time series. Renewable Energy 27(2), 163–174 (2002)

    Article  Google Scholar 

  • Skamarock, W.C., Klemp, J.B., Dudhia, J., Gill, D.O., Barker, D.M., Wang, W., Pow-ers, J.G.: A Description of the Advanced Research WRF Version 2, NCAR/TN–468 (2005)

    Google Scholar 

  • Torres, J.L., García, A., de Blas, M., de, F.: A Forecast of hourly av-erages wind speed with ARMA models in Navarre. Solar Energy 79(1), 65–77 (2005)

    Article  Google Scholar 

  • Vlachogiannis, D., Sfetsos, A., Andronopoulos, S., Gounaris, N., Yiotis, A., Stubos, A.K.: The Demokritos air quality web based system for the Greater Athens Area. In: iEMSs 2008, International Congress on Environmental Modelling and Software, Barcelona, Catalonia, July 7-10 (2008)

    Google Scholar 

  • Wu, Y.K., Hong, J.S.: A literature review of wind forecasting technology in the world. In: Proceedings of IEEE Power Tech Conference, Lausanne, Switzerland, July 1-5, pp. 504–509 (2007)

    Google Scholar 

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Authors and Affiliations

  1. Environmental Research Laboratory, Institute of Nuclear Technology and Radiation Protection, National Centre for Scientific Research Demokritos, 153 10, Ag. Paraskevi, Greece

    Athanasios Sfetsos

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  1. Athanasios Sfetsos
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Editors and Affiliations

  1. Civil Engineering Associate, Ames Lab, US Department of Energy Research Affiliate, Iowa Bioeconomy Institute, Iowa State University, 354 Town Engineering Building, 50011, Ames, IA, USA

    Kasthurirangan Gopalakrishnan

  2. Department of Electrical and Computer Engineering, Iowa State University, 1113 Coover Hall, 50011, Ames, IA, USA

    Siddhartha Kumar Khaitan

  3. Department of Mechanical Engineering and Materials Sciences and Engineering, Cyprus University of Technology, P.O. Box 50329, 3603, Limassol, Cyprus

    Soteris Kalogirou

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Sfetsos, A. (2011). A Comprehensive Overview of Short Term Wind Forecasting Models Based on Time Series Analysis. In: Gopalakrishnan, K., Khaitan, S.K., Kalogirou, S. (eds) Soft Computing in Green and Renewable Energy Systems. Studies in Fuzziness and Soft Computing, vol 269. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22176-7_4

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  • DOI: https://doi.org/10.1007/978-3-642-22176-7_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-22175-0

  • Online ISBN: 978-3-642-22176-7

  • eBook Packages: EngineeringEngineering (R0)

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