Wind Power Prediction Based on Recurrent Neural Network with Long Short-Term Memory Units

Wind power is one of the most promising renewable energy sources, it is clean, safe and inexhaustible. However, predicting wind signal has always been challenging because the time series data is nonlinear, non-stationary and chaotic. In this paper, we provide a novel predicting framework including a recurrent neural network (RNN) structure model with long-short term memory (LS TM) units and an effective forecasting map adapted to different prediction horizons. We compare our new approach with concurrent methods and show that our new method is more effective in predicting wind power.

[1]  Silvano Bordignon,et al.  Mean square prediction error for long-memory processes , 2002 .

[2]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .

[3]  L. Pietrafesa,et al.  Tropical cyclone induced asymmetry of sea level surge and fall and its presentation in a storm surge model with parametric wind fields , 2006 .

[4]  Pierre Pinson,et al.  Global Energy Forecasting Competition 2012 , 2014 .

[5]  Wan Azlan Wan Zainal Abidin,et al.  Different Models of Wind Speed Prediction; A Comprehensive Review , 2014 .

[6]  Yitao Liu,et al.  Deep learning based ensemble approach for probabilistic wind power forecasting , 2017 .

[7]  Jeong-Sik Park,et al.  Long short-term memory recurrent neural network-based acoustic model using connectionist temporal classification on a large-scale training corpus , 2017, China Communications.

[8]  刘峰,et al.  An improved multi-step forecasting model based on WRF ensembles and creative fuzzy systems for wind speed , 2015 .

[9]  Michael H. Kutner.  Applied Linear Statistical Models , 1974 .

[10]  Qing Cao,et al.  Forecasting wind speed with recurrent neural networks , 2012, Eur. J. Oper. Res..

[11]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[12]  Kaile Su,et al.  Long Short-Term Memory Projection Recurrent Neural Network Architectures for Piano's Continuous Note Recognition , 2017, J. Robotics.

[13]  Jun Wang,et al.  A prediction scheme of tropical cyclone frequency based on lasso and random forest , 2018, Theoretical and Applied Climatology.

[14]  V. Barnett,et al.  Applied Linear Statistical Models , 1975 .

[15]  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.