A Hybrid Framework for Short Term Multi-Step Wind Speed Forecasting Based on Variational Model Decomposition and Convolutional Neural Network

Wind speed is an important factor in wind power generation. Wind speed forecasting is complicated due to its highly nonstationary character. Therefore, this paper presents a hybrid framework for the development of multi-step wind speed forecasting based on variational model decomposition and convolutional neural networks. In the first step of signal pre-processing, the variational model decomposition approach decomposes the wind speed data into several independent modes under different center pulsation. The vibrations of decomposed modes are useful for accurate wind speed forecasting. Then, the influence of different numbers of modes and the input length of the convolutional neural network are discussed to select the optimal value through calculating the errors. During the regression step, each mode is treated as a channel that constitutes the input of the forecasting model. The convolution operations in convolutional neural networks extract helpful local features in each mode and the relationships between modes for forecasting. We take advantage of the convolutional neural network and directly output multi-step forecasting results. In order to show the forecasting and generalization performance of the proposed method, wind seed data from two wind farms in Inner Mongolia, China and Sotavento Galicia, Spain with different statistical information were employed. Some classic statistical approaches were adopted for comparison. The experimental results show the satisfactory performance for all of the methods in single-step forecasting and the advantages of using decomposed modes. The root mean squared errors range from 0.79 m/s to 1.64 m/s for all of the methods. In the case of multi-step forecasting, our proposed method achieves an outstanding improvement compared with the other methods. The root mean squared error of our proposed method was 1.30 m/s while the worst performance of the other methods was 9.68 m/s. The proposed method is able to directly predict the variation trend of wind speed based on historical data with minor errors. Hence, the proposed forecasting schemes can be utilized for wind speed multi-step forecasting to cost-effectively manage wind power generation.

[1]  Gaoliang Peng,et al.  A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load , 2018, Mechanical Systems and Signal Processing.

[2]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[3]  J.B. Theocharis,et al.  Long-term wind speed and power forecasting using local recurrent neural network models , 2006, IEEE Transactions on Energy Conversion.

[4]  Ali Akbar Abdoos,et al.  A new intelligent method based on combination of VMD and ELM for short term wind power forecasting , 2016, Neurocomputing.

[5]  Wenlong Fu,et al.  A state tendency measurement for a hydro-turbine generating unit based on aggregated EEMD and SVR , 2015 .

[6]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[7]  T. Funabashi,et al.  Application of Recurrent Neural Network to Long-Term-Ahead Generating Power Forecasting for Wind Power Generator , 2006, 2006 IEEE PES Power Systems Conference and Exposition.

[8]  Qiang Miao,et al.  A parameter-adaptive VMD method based on grasshopper optimization algorithm to analyze vibration signals from rotating machinery , 2018, Mechanical Systems and Signal Processing.

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

[10]  Lars Landberg,et al.  Short-term prediction of the power production from wind farms , 1999 .

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

[12]  Soumith Chintala,et al.  Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.

[13]  Yachao Zhang,et al.  Deterministic and probabilistic interval prediction for short-term wind power generation based on variational mode decomposition and machine learning methods , 2016 .

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

[15]  Salim Lahmiri,et al.  Comparing Variational and Empirical Mode Decomposition in Forecasting Day-Ahead Energy Prices , 2017, IEEE Systems Journal.

[16]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[17]  Marco Raberto,et al.  Modeling and forecasting of electricity spot-prices: Computational intelligence vs classical econometrics , 2014, AI Commun..

[18]  Yanfei Li,et al.  An experimental investigation of three new hybrid wind speed forecasting models using multi-decomposing strategy and ELM algorithm , 2018 .

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

[20]  Phil Blunsom,et al.  A Convolutional Neural Network for Modelling Sentences , 2014, ACL.

[21]  Ranjeeta Bisoi,et al.  Prediction interval forecasting of wind speed and wind power using modes decomposition based low rank multi-kernel ridge regression , 2018, Renewable Energy.

[22]  Lazar Lazić,et al.  Wind forecasts for wind power generation using the Eta model , 2010 .

[23]  Henrik Madsen,et al.  Wind Power Forecasting Based on Echo State Networks and Long Short-Term Memory , 2018 .

[24]  Marco Raberto,et al.  An agent-based stock-flow consistent model of the sustainable transition in the energy sector , 2018 .

[25]  Paras Mandal,et al.  A review of wind power and wind speed forecasting methods with different time horizons , 2010, North American Power Symposium 2010.

[26]  W. Rivera,et al.  Wind speed forecasting in the South Coast of Oaxaca, México , 2007 .

[27]  Lukás Burget,et al.  Recurrent neural network based language model , 2010, INTERSPEECH.

[28]  Chu Zhang,et al.  A compound structure of ELM based on feature selection and parameter optimization using hybrid backtracking search algorithm for wind speed forecasting , 2017 .

[29]  M. Negnevitsky,et al.  Very short-term wind forecasting for Tasmanian power generation , 2006, 2006 IEEE Power Engineering Society General Meeting.

[30]  R. Weron.  Electricity price forecasting: A review of the state-of-the-art with a look into the future , 2014 .

[31]  Yanfeng Wang,et al.  A Novel Hybrid Strategy Using Three-Phase Feature Extraction and a Weighted Regularized Extreme Learning Machine for Multi-Step Ahead Wind Speed Prediction , 2018 .