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Ultra-short-term wind power prediction based on copula function and bivariate EMD decomposition algorithm

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Against the background of increasing installed capacity of wind power in the power generation system, high-precision ultra-short-term wind power prediction is significant for safe and reliable operation of the power generation system. We present a method for ultra-short-term wind power prediction based on a copula function, bivariate empirical mode decomposition (BEMD) algorithm and gated recurrent unit (GRU) neural network. First we use the copula function to analyze the nonlinear correlation between wind power and external factors to extract the key factors influencing wind power generation. Then the joint data composed of the key factors and wind power are decomposed into a series of stationary subsequence data by a BEMD algorithm which can decompose the bivariate data jointly. Finally, the prediction model based on a GRU network uses the decomposed data as the input to predict the power output in the next four hours. The experimental results show that the proposed method can effectively improve the accuracy of ultra-short-term wind power prediction.
Rocznik
Strony
271--286
Opis fizyczny
Bibliogr. 31 poz., rys., tab., wz.
Twórcy
autor
  • North China Electric Power University China
autor
  • North China Electric Power University China
autor
  • North China Electric Power University China
Bibliografia
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  • [2] Aliyu A.K., Modu B., Tan C.W., A review of renewable energy development in Africa: A focus in South Africa, Egypt and Nigeria, Renewable and Sustainable Energy Reviews, vol. 81, no. 2, pp. 2502–2518 (2018).
  • [3] https://irena.org/publications/2019/Mar/Renewable-Capacity-Statistics-2019, accessed March 2019.
  • [4] Waśkowicz B., Statistical analysis and dimensioning of a wind farm energy storage system, Archives of Electrical Engineering, vol. 66, no. 2, pp. 265–277 (2017).
  • [5] Han S., Qiao Y.H., Yan J., Mid-to-long term wind and photovoltaic power generation prediction based on copula function and long short term memory network, Applied Energy, vol. 239, pp. 181–191 (2019).
  • [6] Chen N., Qian Z., Nabney I.T., Meng X., Wind power forecasts using Gaussian processes and numerical weather prediction, IEEE Transactions on Power Systems, vol. 29, no. 2, pp. 656–665 (2014).
  • [7] Carvalho D., Rocha A., Gómez-Gesteira M., Santos C., WRF wind simulation and wind energy production estimates forced by different reanalyses: comparison with observed data for Portugal, Applied Energy, vol. 117, no. 3, pp. 116–126 (2014).
  • [8] Shi J., Ding Z., Lee W., Yang Y., Liu Y., Zhang M., Hybrid Forecasting Model for Very-Short Term Wind Power Forecasting Based on Grey Relational Analysis and Wind Speed Distribution Features, IEEE Transactions on Smart Grid, vol. 5, no. 1, pp. 521–526 (2014).
  • [9] He D., Ruiye Liu R., Ultra-short-term wind power prediction using ANN ensemble based on PCA, International Power Electronics and Motion Control Conference, Harbin, China, pp. 2108–2112 (2012).
  • [10] Gyu G.K., Jin H.C., So Y.P., Byeong G.B., Woo J.N., Hae L.C., Prediction Model for PV Performance With Correlation Analysis of Environmental Variables, IEEE Journal of Photovoltaics, vol. 9, no. 3, pp. 832–841 (2019).
  • [11] Jing P., Su Y., Jin X., Zhang C., High-Order Temporal Correlation Model Learning for Time-Series Prediction, IEEE Transactions on Cybernetics, vol. 49, no. 6, pp. 2385–2397 (2019).
  • [12] Sumit S., Aggarwal S.K., Wind power forecasting using wavelet transforms and neural networks with tapped delay, CSEE Journal of Power and Energy Systems, vol. 4, no. 2, pp. 197–209 (2018).
  • [13] Yang M., Chen X., Jian Du J., Cui Y., Ultra-Short-Term Multistep Wind Power Prediction Based on Improved EMD and Reconstruction Method Using Run-Length Analysis, IEEE Access, vol. 6, pp. 31908–31917 (2018).
  • [14] Su Y., Wang S., Xiao Z., Tan M., Wang M., An Ultra-Short-Term Wind Power Forecasting Approach Based on Wind Speed Decomposition, Wind Direction and Elman Neural Networks, 2018 2nd IEEE Conference on Energy Internet and Energy System Integration, Beijing, China, pp. 1–9 (2018).
  • [15] Yeh J., Shieh J., Huang N.E., Complementary ensemble empirical mode decomposition: A novel noise enhanced data analysis method, Advances in Adaptive Data Analysis, vol. 2, no. 2, pp. 135–156 (2010).
  • [16] Wu J.L., Ji T.Y., Li M.S., Wu Q.H., Multi-step wind power forecast based on similar segments extracted by mathematical morphology, Power and Energy Engineering Conference, Hong Kong, China, pp. 1–6 (2015).
  • [17] Cui M., Peng X., Xia J., Sun Y., Wu Z., Short term power forecasting of a wind farm based on atomic sparse decomposition theory, IEEE International Conference on Power System Technology, Auckland, New Zealand, pp. 1–5 (2013).
  • [18] Chen N., Qian Z., Nabney I., Meng X., Wind power forecasts using Gaussian processes and numerical weather prediction, IEEE Trans. Power Syst., vol. 29, no. 2, pp. 656–665 (2014).
  • [19] Xie W., Zhang P., Chen R., Zhou Z., A Nonparametric Bayesian framework for short-term wind power probabilistic forecast, IEEE Trans. Power Syst., vol. 34, no. 1, pp. 371–379 (2019).
  • [20] Wan C., Wang J., Lin J., Song Y., Dong Z., Nonparametric prediction intervals of wind power via linear programming, IEEE Trans. Power Syst., vol. 33, no. 1, pp. 1074–1076 (2018).
  • [21] Zeng J.W., Qiao W., Short-term wind power prediction using a wavelet support vector machine, IEEE Transactions on Sustainable Energy, vol. 3, no. 2, pp. 255–264 (2012).
  • [22] Sideratos G., Hatziargyriou N., Probabilistic wind power forecasting using radial basis function neural networks, IEEE Trans. Power Syst., vol. 27, no. 4, pp. 1788–1796 (2012).
  • [23] Luo X., Sun J., Wang L., Wang W., Zhao W., Wu J., Short-term wind speed forecasting via stacked extreme learning machine with generalized correntropy, IEEE Trans. Ind. Inf., vol. 14, no. 11, pp. 4963–4971 (2018).
  • [24] Zaccheus O., A 5-day wind speed and power forecasts using a layer recurrent neural network (LRNN), Sustainable Energy Technologies and Assessments, vol. 6, pp. 1–24 (2014).
  • [25] Xie Z.Q., Ji T.Y., Li M.S., Wu Q.H., Quasi-Monte Carlo Based Probabilistic Optimal Power Flow Considering the Correlation of Wind Speeds Using Copula Function, IEEE Transactions on Power Systems, vol. 33, no. 2, pp. 2239–2247 (2018).
  • [26] Xie Z., Statistical Analysis and Application of MATLAB: An Analysis of 40 Cases, Beihang University Press (2010).
  • [27] Huang N.E., Shen Z., The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-Stationary Time Series Analysis, Proceedings: Mathematical, Physical and Engineering Sciences, vol. 454, pp. 903–995 (1998).
  • [28] An X., Jiang D., Zhao M., Liu C., Short-term prediction of wind power using EMD and chaotic theory, Communications in Nonlinear Science and Numerical Simulation, vol. 17, no. 2, pp. 1036–1042 (2012).
  • [29] Rilling G., Flandrin P., Goncalves P., Bivariate Empirical Mode Decomposition, IEEE Signal Processing Letters, vol. 14, no. 12, pp. 936–939 (2007).
  • [30] Jiang T., Subband-based EMD Signal Decomposition Algorithm, PhD Thesis, School of Electronic Engineering, Xidian University, Xi’an (2013).
  • [31] Li W.X., Logenthiran T., Woo W.L., Multi-GRU prediction system for electricity generation’s planning and operation, IET Generation, Transmission and Distribution, vol. 13, no. 9, pp. 1630–1637 (2019).
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-b62f5a43-bfb3-4d0c-80bc-870222733d4b
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