Ultra-short-term Interval Prediction of Wind Farm Cluster Power Based on LASSO

Efficient and accurate power prediction of wind farm cluster is an effective method to improve the safety and reliability of power system for large-scale wind power. In this paper, the probabilistic prediction model of regional wind power is studied. The nonparametric method based on least absolute shrinkage and selection operator (LASSO) is used for the ultra-short-term probabilistic prediction. In this paper, the prediction model of nonlinear quantile regression (NQR) model based on quantile regression (QR) and extreme learning machine (ELM) is studied. Then, LASSO is utilized to shrink the output weights for the sparsity. The penalty of LASSO can prevent the overfitting and improve the performance of prediction intervals (PIs), without the reduction of computational efficiency. With the actual dataset of the wind farms in northeast China, the PIs performance is verified, compared with other well-established benchmarks.

[1]  Kit Po Wong,et al.  Probabilistic Forecasting of Wind Power Generation Using Extreme Learning Machine , 2014, IEEE Transactions on Power Systems.

[2]  A. Llombart,et al.  Statistical Analysis of Wind Power Forecast Error , 2008, IEEE Transactions on Power Systems.

[3]  Andrew Kusiak,et al.  Very short-term wind speed forecasting with Bayesian structural break model , 2013 .

[4]  R. Tibshirani,et al.  Regression shrinkage and selection via the lasso: a retrospective , 2011 .

[5]  A. Conejo,et al.  Economic Valuation of Reserves in Power Systems With High Penetration of Wind Power , 2009 .

[6]  Ding Yuyu.  A Regional Wind Power Forecasting Method Based on Statistical Upscaling Approach , 2013 .

[7]  Shouxiang Wang,et al.  An Improved Interval AHP Method for Assessment of Cloud Platform-based Electrical Safety Monitoring System , 2017 .

[8]  Henrik Madsen,et al.  A review on the young history of the wind power short-term prediction , 2008 .

[9]  John Bjørnar Bremnes,et al.  Probabilistic wind power forecasts using local quantile regression , 2004 .

[10]  M. G. Lobo,et al.  Regional Wind Power Forecasting Based on Smoothing Techniques, With Application to the Spanish Peninsular System , 2012, IEEE Transactions on Power Systems.

[11]  Mao Yang,et al.  Investigating the Wind Power Smoothing Effect Using Set Pair Analysis , 2020, IEEE Transactions on Sustainable Energy.

[12]  R. Koenker,et al.  Regression Quantiles , 2007 .

[13]  Vladimiro Miranda,et al.  Time-adaptive quantile-copula for wind power probabilistic forecasting , 2012 .

[14]  Antonio J. Conejo,et al.  Economic Valuation of Reserves in Power Systems With High Penetration of Wind Power , 2009, IEEE Transactions on Power Systems.

[15]  Yonghui Sun,et al.  Power Compensation of Network Losses in a Microgrid With BESS by Distributed Consensus Algorithm , 2021, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[16]  J. T. Hwang,et al.  Prediction Intervals for Artificial Neural Networks , 1997 .

[17]  Narasimhan Sundararajan,et al.  A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks , 2006, IEEE Transactions on Neural Networks.

[18]  P. Pinson,et al.  Very‐short‐term probabilistic forecasting of wind power with generalized logit–normal distributions , 2012 .

[19]  Aoife Foley,et al.  Current methods and advances in forecasting of wind power generation , 2012 .

[20]  Shouxiang WANG,et al.  Hybrid interval AHP-entropy method for electricity user evaluation in smart electricity utilization , 2018 .

[21]  Jin Lin,et al.  Direct Quantile Regression for Nonparametric Probabilistic Forecasting of Wind Power Generation , 2017, IEEE Transactions on Power Systems.

[22]  Saeid Nahavandi,et al.  Prediction Intervals to Account for Uncertainties in Travel Time Prediction , 2011, IEEE Transactions on Intelligent Transportation Systems.

[23]  S. E. Haupt,et al.  A Wind Power Forecasting System to Optimize Grid Integration , 2012, IEEE Transactions on Sustainable Energy.

[24]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[25]  Wang Peng,et al.  Ultra short‐term probability prediction of wind power based on LSTM network and condition normal distribution , 2019, Wind Energy.

[26]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[27]  Yi Wang,et al.  Robust Forecasting-Aided State Estimation for Power System Against Uncertainties , 2020, IEEE Transactions on Power Systems.

[28]  Mao Yang,et al.  Ultra-Short-Term Multistep Wind Power Prediction Based on Improved EMD and Reconstruction Method Using Run-Length Analysis , 2018, IEEE Access.

[29]  S. Tewari,et al.  A Statistical Model for Wind Power Forecast Error and its Application to the Estimation of Penalties in Liberalized Markets , 2011, IEEE Transactions on Power Systems.