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To enhance the performance of the prediction intervals (PIs), a novel very short-term probabilistic prediction method for wind speed via nonlinear quantile regression (NQR) based on adaptive least absolute shrinkage and selection operator (ALASSO) and integrated criterion (IC) is proposed. The ALASSO method is studied for shrinkage of output weights and selection of variables. Furthermore, for the better performance of PIs, composite weighted linear programming (CWLP) is proposed to modify the conventional linear programming cost function of quantile regression (QR), by combining it with Bayesian information criterion (BIC) as an IC to optimize the coefficients of PIs. Then, the multiple fold cross model (MFCM) is utilized to improve the PIs performance. Multistep probabilistic prediction of 15-minute wind speed is performed based on the real wind farm data from the northeast of China. The effectiveness of the proposed approach is validated through the performances’ comparisons with conventional methods.
R. Bo and F. X. Li, “Efficient estimation of critical load levels using variable substitution method,” IEEE Transactions on Power Systems, vol. 26, no. 4, pp. 2472–2482, Nov. 2011.
C. F. Zhao, C. Wan, and Y. H. Song, “An adaptive bilevel programming model for nonparametric prediction intervals of wind power generation,” IEEE Transactions on Power Systems, vol. 35, no. 1, pp. 424–439, Jun. 2019.
O. Abedinia, M. Zareinejad, M. H. Doranehgard, G. Fathi, and N. Ghadimi, “Optimal offering and bidding strategies of renewable energy based large consumer using a novel hybrid robust-stochastic approach,” Journal of Cleaner Production, vol. 215, pp. 878–889, Apr. 2019.
M. Saeedi, M. Moradi, M. Hosseini, A. Emamifar, and N. Ghadimi, “Robust optimization based optimal chiller loading under cooling demand uncertainty,” Applied Thermal Engineering, vol. 148, pp. 1081–1091, Jan. 2019.
W. Gao, A. Darvishan, M. Toghani, M. Mohammadi, O. Abedinia, and N. Ghadimi “Different states of multi-block based forecast engine for price and load prediction,” Electrical Power and Energy Systems, vol. 104, pp. 423–435, Jan. 2019.
N. Ghadimi, A. Akbarimajd, H. Shayeghi, and O. Abedinia, “Two stage forecast engine with feature selection technique and improved meta-heuristic algorithm for electricity load forecasting,” Energy, vol. 161, pp. 130–142, Oct. 2018.
H. Khodaei, M. Hajiali, A. Darvishan, M. Sepehr, and N. Ghadimi, “Fuzzy-based heat and power hub models for cost-emission operation of an industrial consumer using compromise programming,” Applied Thermal Engineering, vol. 137, pp. 395–405, Jun. 2018.
H. A. Bagal, Y. N. Soltanabad, M. Dadjuo, K. Wakil, and N. Ghadimi, “Risk-assessment of photovoltaic-wind-battery-grid based large industrial consumer using information gap decision theory,” Solar Energy, vol. 169, pp. 343–352, Jul. 2018.
R. Bo and F. X. Li, “Probabilistic LMP forecasting considering load uncertainty,” IEEE Transactions on Power Systems, vol. 24, no. 3, pp. 1279–1289, Aug. 2009.
T. Y. Hu, W. C. Wu, Q. L. Guo, H. B. Sun, L. B. Shi, and X. W. Shen, “Very short-term spatial and temporal wind power forecasting: A deep learning approach,” CSEE Journal of Power and Energy Systems, vol. 6, no. 2, pp. 434–443, Jun. 2020.
Y. S. Xue, C. Chen, K. Li, F. S. Wen, Y. Ding, Q. W. Wu, and G. Y. Yang, “Adaptive ultra-short-term wind power prediction based on risk assessment,” CSEE Journal of Power and Energy Systems, vol. 2, no. 3, pp. 59–64, Sep. 2016.
S. X. Wang, X. Zhao, H. Wang, and M. Li, “Small-world neural network and its performance for wind power forecasting,” CSEE Journal of Power and Energy Systems, vol. 6, no. 2, pp. 362–373, Jun. 2020.
S. Saroha and S. K. Aggarwal, “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, Jun. 2018.
C. W. Potter and M. Negnevitsky, “Very short-term wind forecasting for Tasmanian power generation,” IEEE Transactions on Power Systems, vol. 21, no. 2, pp. 965–972, May 2006.
M. Javadi, A. M. Malyscheff, D. Wu, C. Q. Kang, and J. N. Jiang, “An algorithm for practical power curve estimation of wind turbines,” CSEE Journal of Power and Energy Systems, vol. 4, no. 1, pp. 93–102, Mar. 2018.
A. M. Foley, P. G. Leahy, A Marvuglia, and E. J. Mckeogh, “Current methods and advances in forecasting of wind power generation,” Renewable Energy, vol. 37, no. 1, pp. 1–8, Jan. 2012.
J. T. G. Hwang and A. A. Ding, “Prediction intervals for artificial neural networks,” Journal of the American Statistical Association, vol. 92, no. 438, pp. 748–757, Jun. 1997.
H. Bludszuweit, J. A. Dominguez-navarro, and A. Llombart, “Statistical analysis of wind power forecast error,” IEEE Transactions on Power Systems, vol. 23, no. 3, pp. 983–991, Sep. 2008.
S. Tewari, C. J. Geyer, and N. Mohan, “A statistical model for wind power forecast error and its application to the estimation of penalties in liberalized markets,” IEEE Transactions on Power Systems, vol. 26, no. 4, pp. 2031–2039, Nov. 2011.
P. Pinson, “Very-short-term probabilistic forecasting of wind power with generalized logit—normal distributions,” Journal of the Royal Statistical Society Series C-Applied Statistics, vol. 61, no. 4, pp. 555–576, Aug. 2012.
C. Wan, Z. Xu, P. Pinson, Z. Y. Dong, and K. P. Wong, “Probabilistic forecasting of wind power generation using extreme learning machine,” IEEE Transactions on Power Systems, vol. 29, no. 3, pp. 1033–1044, May 2014.
A. Khosravi, E. Mazloumi, S. Nahavandi, D. Creighton, and J. W. C. Lint, “Prediction intervals to account for uncertainties in travel time prediction,” IEEE Transactions on Intelligent Transportation Systems, vol. 12, no. 2, pp. 537–547, Jul. 2014.
D. A. Nix and A. S. Weigend, “Estimating the mean and variance of the target probability distribution,” Proceedings of 1994 IEEE International Conference on Neural Networks, vol. 1, pp. 55–60, Jan. 1994.
J. Jung and R. P. Broadwater, “Current status and future advances for wind speed and power forecasting,” Renewable and Sustainable Energy Reviews, vol. 31, no. 2, pp. 762–777, Mar. 2014.
C. Wan, Z. Xu, P. Pinson, Z. Y. Dong, and K. P. Wong, “Direct interval forecasting of wind power,” IEEE Transactions on Power Systems, vol. 28, no. 4, pp. 4877–4878, Nov. 2013.
C. Wan, Z. Xu, P Pinson, Z. Y. Dong, and K. P. Wong, “Optimal prediction intervals of wind power generation,” IEEE Transactions on Power Systems, vol. 29, no. 3, pp. 1166–1174, May 2014.
C. Wan, J. H. Wang, J. Lin, Y. H. Song, and Z. Y. Dong, “Nonparametric prediction intervals of wind power via linear programming,” IEEE Transactions on Power Systems, vol. 33, no. 1, pp. 1074–1076, Jan. 2018.
J. B. Bremnes, “Probabilistic wind power forecasts using local quantile regression,” Wind Energy, vol. 7, no. 1, pp. 47–54, Jan. 2010.
R. J. Bessa, V. Miranda, A. Botterud, Z. Zhou, and J. Wang, “Time-adaptive quantile-copula for wind power probabilistic forecasting,” Renewable Energy, vol. 40, no. 1, pp. 29–39, Apr. 2012.
C. Wan, J. Lin, J. H. Wang, Y. H. Song, and Z. Y. Dong, “Direct quantile regression for nonparametric probabilistic forecasting of wind power generation,” IEEE Transactions on Power Systems, vol. 32, no. 4, pp. 2767–2778, Jul. 2016.
G. B. Huang, H. M. Zhou, X. J. Ding, and R. Zhang, “Extreme learning machine for regression and multiclass classification,” IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics, vol. 42, no. 2, pp. 513–529, Oct. 2011.
N. Y. Liang, G. B. Huang, P. Saratchandran, and N. Sundararajan, “A fast and accurate online sequential learning algorithm for feedforward networks,” IEEE Transactions on Neural Networks, vol. 17, no. 6, pp. 1411–1423, Nov. 2006.
Y. H. Sun, P. Wang, S. W. Zhai, D. C. Hou, S. Wang, and Y. Zhou, “Ultra short-term probability prediction of wind power based on LSTM network and condition normal distribution,” Wind Energy, vol. 23, no. 1, pp. 63–76, Jan. 2020.
L. X. Lian, A. Liu, and V. K. N. Lau, “Weighted LASSO for sparse recovery with statistical prior support information,” IEEE Transactions on Signal Processing, vol. 66, no. 6, pp. 1607–1618, Mar. 2018.
R. Tibshirani, “Regression shrinkage and selection via the LASSO: A restropective,” Journal of the Royal Statistical Society Series B-Statistical Methodology, vol. 73, no. 3, pp. 273–282, Apr. 2011.
W. H. Zhao, R. Q. Zhang, and J. C. Liu, “Sparse group variable selection based on quantile hierarchical Lasso,” Journal of Applied Statistics, vol. 41, no. 7, pp. 1658–1677, Feb. 2014.
N. K. Tang, S. W. Mao, Y. Wang, and R. M. Nelms, “Solar power generation forecasting with a LASSO-based approach,” IEEE Internet of Things Journal, vol. 5, no. 2, pp. 1090–1099, Apr. 2018.
H. S. Wang and C. L. Leng, “A note on adaptive group lasso,” Computational Statistics and Data Analysis, vol. 52, no. 12, pp. 5277–5286, Aug. 2008.
G. B. Huang, Q. Y. Zhu, and C. K. Siew, “Extreme learning machine: Theory and applications,” Neurocomputing, vol. 70, no. 1–3, pp. 489–501, May 2006.
R. Koenker and G. Bassett, “Regression quantiles,” Econometrica, vol. 46, no. 1, pp. 33–50, Jan. 1978.
X. G. Agoua, R. Girard, and G. Kariniotakis, “Probabilistic model for spatio-temporal photovoltaic power forecasting,” IEEE Transactions on Sustainable Energy, vol. 10, no. 2, pp. 780–789, Apr. 2019.
A. Khosravi, S. Nahavandi, and D. Creighton, “Prediction intervals for short-term wind farm power generation forecasts,” IEEE Transactions on Sustainable Energy, vol. 4, no. 3, pp. 602–610, Apr. 2013.
E. R. Lee, H. Noh, and B. U. Park, “Model selection via Bayesian information criterion for quantile regression models,” Journal of the American Statistical Association, vol. 109, no. 505, pp. 216–229, Jan. 2014.
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