Multi-step wind speed prediction by combining a WRF simulation and an error correction strategy

Abstract The accurate prediction of wind speed is important in satisfying the demands of power grids. However, the prediction of wind speed is challenging because of its randomness and volatility, especially in multi-step cases. This study proposes a novel multi-step wind speed prediction model combining a Weather Research and Forecasting (WRF) simulation and an error correction strategy. First, the WRF model is adopted to predict the wind speed. Variational Mode Decomposition (VMD) is then employed to mine features of the predicted wind speed using the WRF model. The Principal Component Analysis (PCA) method is next used to extract the main components and remove illusive components. Using these principal components and prediction error as the training dataset, Long Short-Term Memory (LSTM) is applied for error correction. The WRF-VMD-PCA-LSTM model is thus developed for the multi-step prediction of wind speed. In a case study of a wind farm located in Sichuan Province, China, the proposed WRF-VMD-PCA-LSTM model outperforms models to which it is compared. The results reveal that the VMD-PCA method effectively extracts features hidden in the numerical WRF output. The proposed model effectively improves the accuracy of multi-step wind speed prediction.

[1]  Xiaofeng Meng,et al.  Wind Power Forecasts Using Gaussian Processes and Numerical Weather Prediction , 2014, IEEE Transactions on Power Systems.

[2]  Ángel M. Pérez-Bellido,et al.  Hybridizing the fifth generation mesoscale model with artificial neural networks for short-term wind speed prediction , 2009 .

[3]  Bo Ming,et al.  Optimizing utility-scale photovoltaic power generation for integration into a hydropower reservoir by incorporating long- and short-term operational decisions , 2017 .

[4]  Jing Zhao,et al.  An improved multi-step forecasting model based on WRF ensembles and creative fuzzy systems for wind speed , 2016 .

[5]  Haiyan Lu,et al.  Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model , 2012 .

[6]  Jie Yu,et al.  Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach , 2014 .

[7]  Zhang Yan,et al.  A review on the forecasting of wind speed and generated power , 2009 .

[8]  F. Cassola,et al.  Wind speed and wind energy forecast through Kalman filtering of Numerical Weather Prediction model output , 2012 .

[9]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[10]  Sancho Salcedo-Sanz,et al.  Short term wind speed prediction based on evolutionary support vector regression algorithms , 2011, Expert Syst. Appl..

[11]  Zheng Yao,et al.  Wind power forecasting based on principle component phase space reconstruction , 2015 .

[12]  J. Torres,et al.  Forecast of hourly average wind speed with ARMA models in Navarre (Spain) , 2005 .

[13]  A. Immanuel Selvakumar,et al.  Linear and non-linear autoregressive models for short-term wind speed forecasting , 2016 .

[14]  Xiangang Peng,et al.  A novel wind speed forecasting based on hybrid decomposition and online sequential outlier robust extreme learning machine , 2019, Energy Conversion and Management.

[15]  Zijun Zhang,et al.  Short-Horizon Prediction of Wind Power: A Data-Driven Approach , 2010, IEEE Transactions on Energy Conversion.

[16]  Jing Yan,et al.  Advanced wind power prediction based on data-driven error correction , 2019, Energy Conversion and Management.

[17]  Jianzhou Wang,et al.  Analysis and application of forecasting models in wind power integration: A review of multi-step-ahead wind speed forecasting models , 2016 .

[18]  J. Dudhia,et al.  Improving the representation of resolved and unresolved topographic effects on surface wind in the WRF model , 2012 .

[19]  Lei Wu,et al.  Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method , 2016 .

[20]  Jun Liang,et al.  Analysis of multi-scale chaotic characteristics of wind power based on Hilbert–Huang transform and Hurst analysis , 2015 .

[21]  Dominique Zosso,et al.  Variational Mode Decomposition , 2014, IEEE Transactions on Signal Processing.

[22]  Jingwen Zhang,et al.  Hydropower reservoir reoperation to adapt to large-scale photovoltaic power generation , 2019, Energy.

[23]  Jing Zhao,et al.  Multi-step wind speed and power forecasts based on a WRF simulation and an optimized association method , 2017 .

[24]  Paras Mandal,et al.  A Hybrid Intelligent Model for Deterministic and Quantile Regression Approach for Probabilistic Wind Power Forecasting , 2014, IEEE Transactions on Power Systems.

[25]  Xiaobing Kong,et al.  Wind speed prediction using reduced support vector machines with feature selection , 2015, Neurocomputing.

[26]  Chongqing Kang,et al.  A Short-Term Wind Power Forecasting Approach With Adjustment of Numerical Weather Prediction Input by Data Mining , 2015, IEEE Transactions on Sustainable Energy.

[27]  Jujie Wang,et al.  Multi-step ahead wind speed prediction based on optimal feature extraction, long short term memory neural network and error correction strategy , 2018, Applied Energy.

[28]  William P. Mahoney,et al.  The impact of model physics on numerical wind forecasts , 2013 .

[29]  Ricardo Nicolau Nassar Koury,et al.  Prediction of wind speed and wind direction using artificial neural network, support vector regression and adaptive neuro-fuzzy inference system , 2018 .

[30]  Qinghua Hu,et al.  Transfer learning for short-term wind speed prediction with deep neural networks , 2016 .

[31]  İnci Okumuş,et al.  Current status of wind energy forecasting and a hybrid method for hourly predictions , 2016 .

[32]  Joachim Reuder,et al.  Validation of boundary layer parameterization schemes in the weather research and forecasting model under the aspect of offshore wind energy applications— Part I: Average wind speed and wind shear , 2015 .

[33]  R. Kavasseri,et al.  Day-ahead wind speed forecasting using f-ARIMA models , 2009 .

[34]  Erasmo Cadenas,et al.  Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA–ANN model , 2010 .

[35]  Yang Fu,et al.  Short-term wind power forecasts by a synthetical similar time series data mining method , 2018 .

[36]  Ladislav Zjavka,et al.  Wind speed forecast correction models using polynomial neural networks , 2015 .