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Adaptation of models from determined chaos theory to short-term power forecasts for wind farms

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper proposes an adaptation of mathematical models derived from the theory of deterministic chaos to short-term power forecasts of wind turbines. The operation of wind power plants and the generated power depend mainly on the wind speed at a given location. It is a stochastic process dependent on many factors and very difficult to predict. Classical forecasting models are often unable to find the existing relationships between the factors influencing wind power output. Therefore, we decided to refer to fractal geometry. Two models based on self-similar processes (M-CO) and (M-COP) and the (M-HUR) model were built. The accuracy of these models was compared with other short-term forecasting models. The modified model of power curve adjusted to local conditions (M-PC) and Canonical Distribution of the Vector of Random Variables Model (CDVRM). Examples of applications confirm the valuable properties of the proposed approaches.
Rocznik
Strony
1491--1501
Opis fizyczny
Bibliogr. 39 poz., rys., tab.
Twórcy
  • Department of Electrical Engineering, Czestochowa University of Technology, Al. Armii Krajowej 17, 42-200 Czestochowa, Poland
autor
  • Department of Electrical Engineering, Czestochowa University of Technology, Al. Armii Krajowej 17, 42-200 Czestochowa, Poland
autor
  • Department of Power Management, Opole University of Technology, ul. Ozimska 75, 45-370 Opole, Poland
Bibliografia
  • [1] A. Augustyn and J. Kamiński, “A review of methods applied for wind power generation forecasting”, Polityka Energetyczna – Energy Policy Journal 21(2), 139–150 (2018).
  • [2] H. Liu, Ch. Chen, X. Lv, X. Wu, and M. Liu, “Deterministic wind energy forecasting: A review of intelligent predictors and auxiliary methods”, Energ. Convers. Manage. 195, 328–345 (2019).
  • [3] M. Lei, L. Shiyan, J. Chuanwen, L. Hongling, and Z. Yan, “A review on the forecasting of wind speed and generated power”, Renew. Sust. Energ. Rev. 13, 915–920 (2009).
  • [4] E. Erdem and J. Shi, “ARMA based approaches for forecasting the tuple of wind speed and direction”, Appl. Energy 88, 1405–1414 (2011).
  • [5] E. Cadenas and W. Rivera, “Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA-ANN model”, Renew. Energy 35, 2732–2738 (2010).
  • [6] R.G. Kavasseri and K. Seetharaman, “Day-ahead wind speed forecasting using f-ARIMA models”, Renew. Energy 34, 1388–1393 (2009).
  • [7] J. Torres, A. Garcia, M.D. Blas, and A.D. Francisco, “Forecast of hourly average wind speed with ARMA models in Navarre (Spain)”, Sol. Energy 79, 65–77 (2005).
  • [8] A. Sfetsos, “A novel approach for the forecasting of mean hourly wind speed time series”, Renew. Energy 27, 163–174 (2002).
  • [9] R. Blonbou, S. Monjoly, and J.F. Dorville, “An adaptive short-term prediction scheme for wind energy storage management”, Energ. Convers. Manage. 52, 2412–2416 (2011).
  • [10] J. Catalao, H. Pousinho, and V. Mendes, “Short-term wind power forecasting in Portugal by neural networks and wavelet transform”, Renew. Energy 36, 1245–1251 (2011).
  • [11] N.K. Paliwal, A.K. Singh, ang N.K. Singh, “Short-term optimal energy management in stand-alone microgrid with battery energy storage”, Arch. Elect. Eng. 67(3), 499–513 (2018). doi: 10.24425/123659.
  • [12] G. Dudek, “Multilayer perceptron for short-term load forecasting: from global to local approach”, Neural Comput. Appl. 32, 3695–3707 (2020). doi: 10.1007/s00521-019-04130-y.
  • [13] A.M. Foley, P.G. Leahy, A. Marvvuglia, and E.J. McKeog, “Current methods and advances in forecasting of wind power generation”, Renew. Energy 37, 1–8 (2012).
  • [14] Z.S. Yang and J. Wang, “A combination forecasting approach applied in multistep wind speed forecasting based on a data processing strategy and an optimized artificial intelligence algorithm”, Appl. Energy 230, 1108–1125 (2018).
  • [15] J, Wang, Y. Wang, and Y. Li, “A novel hybrid strategy using three-phase feature extraction and a weighted regularized extreme learning machine for multi-step ahead wind speed prediction”, Energies 11(2), 321 (2018).
  • [16] I. Okumus and A. Dinler, “Current status of wind energy forecasting and a hybrid method for hourly predictions”, Energ. Conver.s Manage. 123, 362–71 (2016).
  • [17] A. Zendehboudi, M. Baseer, and R. Saidur, “Application of support vector machine models for forecasting solar and wind energy resources: a review”, J. Clean Prod. 199, 272–285 (2018).
  • [18] N. Amjady, F. Keynia, and H. Zareipour, “Short-term wind power forecasting using ridgelet neural network”, Elect. Pow. Syst. Res. 81, 2099–2107 (2011).
  • [19] T. Barbounis and J. Theocharis, “A locally recurrent fuzzy neural network with application to the wind speed prediction using spatial correlation”, Neurocomputing 70, 1525–1542 (2007).
  • [20] L. Thiaw, G. Sow, S. Fall, M. Kasse, E. Sylla, and S. Thioye, “A neural network based approach for wind resource and wind generators production assessment”, Appl. Energy 87, 1744–1748 (2010).
  • [21] L. Landberg, “Short-term prediction of local wind conditions”, J. Wind. Eng. Ind. Aerod. 89, 235–245 (2001).
  • [22] 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”, in Proceedings of European wind energy conference, Madryt, 2003.
  • [23] E. López, C. Valle, H. Allende, E. Gil, and H. Madsen, “Wind power forecasting based on echo state networks and long short-term memory”, Energies 11(3), 526 (2018).
  • [24] A. Kusiak, H.-Y. Zheng, and Z. Song, “Wind Farm Power Prediction: A Data-Mining Approach”, Wind Energy 12(3), 275–293 (2009).
  • [25] T. Poplawski, Theory and Practice of Planning Development and Exploitation of Power Engineering Systems, Technological University of Cz ̨estochowa, Cz ̨estochowa, 2013, [in Polish].
  • [26] T. Poplawski and D. Calus, “Adaptation of selected aspects of deterministic chaos for long-term forecasts of peak power demand for Poland”, Prz. Elektrotechniczny R94 (12/2018), 79–85 (2018).
  • [27] I. Prigogine and I. Stengers, Order out of Chaos, University of Michigan, Bantam Books, 1984.
  • [28] T. Poplawski and P. Szelag, “Use the similarity of processes to predict the power output of wind turbines”, Energy Market 92, 103–107 (2011) [in Polish].
  • [29] D. Grzech and G. Pamuła, “The local Hurst exponent of the financial time series in the vicinity of crashes on the Polish stock exchange market”, Physica A 387, 4299–4308 (2008).
  • [30] B. Mandelbrot, The Fractal geometry of nature, W.H. Freeman & Co, 1982.
  • [31] H.O. Peitgen, H. Jurgens, and D. Saupe, The borders of chaos, Fractals, PWN Publishing House, 1997 [in Polish].
  • [32] E.E. Peters, Chaos And Order In The Capital Markets – A New View Of Cycles, Prices, And Market Volatility, Second Edition, John Wiley & Sons, New York, 1996.
  • [33] K. Domiono, “The use of the Hurst exponent to predict changes in trends on the Warsaw Stock Exchange”, Physica A 390, 98–109 (2011).
  • [34] S.E. Kruger, O. Matos, J. Marcos, J. Mauricio, E.P.D. Moura, and A. Rebello, “Rescaled range analysis and fluctuation analysis study of cast irons ultrasonic backscattered signals”, Chaos Solitons Fractals 19(1), 55–60 (2004).
  • [35] M. Gilmore, T.L. Rhodes, W.A. Peebles, and C.X. Yu, “Investigation of rescaled range analysis, the Hurst exponent, and long-time correlations in plasma turbulence”, Phys. Plasmas. 9(4), 1312–1317 (2002).
  • [36] D.W. Qian, Y.F. Xi, and S.W. Tong, “Chaos synchronization of uncertain coronary artery systems through sliding mode”, Bull. Pol. Ac.: Tech. 67(3), 456–462 (2019).
  • [37] Y. Li and X. Wang, “Improved dolphin swarm optimization algorithm based on information entropy”, Bull. Pol. Ac.: Tech 67(4), 679–685 (2019).
  • [38] W. Cieslewicz and A. Dudtkowska, “The accuracy of some meteorological parameters modeling for the southern baltic sea area – a comparative study”, Infrastructure and ecology of rural areas, 6/2011, PAN, 59–68, (2011) [in Polish].
  • [39] D.A. Swanson, J. Tayman, and T.M. Bryan, “MAPE-R: a rescaled measure of accuracy for cross-sectional subnational population forecasts”, J. Popul. Res. 28, 225–243 (2011).
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-ad279fc4-28f5-4529-88ea-5e543982aaf5
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