Short-term wind speed prediction using an extreme learning machine model with error correction

Abstract Wind speed forecasting is an important technology in the wind power field; however, because of their chaotic nature, predicting wind speeds accurately is difficult. Aims at this challenge, a new hybrid model is proposed for short-term wind speed forecasting, where the short-term forecasting period is ten minutes. The model combines extreme learning machine with improved complementary ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and autoregressive integrated moving average (ARIMA). The extreme learning machine model is employed to obtain short-term wind speed predictions, while the autoregressive model is used to determine the best input variables. An ensemble method is used to improve the robustness of the extreme learning machine. To improve the prediction accuracy, the ICEEMDAN-ARIMA method is developed to postprocess the errors; this method can also be used to preprocess original wind speed. Additionally, this paper reports the results of a comparative study on preprocessing and postprocessing time series data. Three experimental results show that: (1) the error correction is effective in decreasing the prediction error, and the proposed models with error correction are suitable for short-term wind speed forecasting; (2) the ICEEMDAN method is more powerful than other variants of empirical mode decomposition in performing non-stationary decomposition, and the ICEEMDAN-ARIMA method achieves satisfactory performance both for preprocessing and postprocessing; and (3) for prediction, the preprocessing of time series is more effective than its postprocessing.

[1]  Robin E. Bell,et al.  Identification and control of subglacial water networks under Dome A, Antarctica , 2013 .

[2]  Spyros Makridakis,et al.  Accuracy measures: theoretical and practical concerns☆ , 1993 .

[3]  Heng-Li Yang,et al.  Applying the Hybrid Model of EMD, PSR, and ELM to Exchange Rates Forecasting , 2017 .

[4]  Lars Kai Hansen,et al.  Neural Network Ensembles , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[6]  Nasrudin Abd Rahim,et al.  Long-term electric energy consumption forecasting via artificial cooperative search algorithm , 2016 .

[7]  Michael Negnevitsky,et al.  Wind speed forecast model for wind farm based on a hybrid machine learning algorithm , 2015 .

[8]  Yun Wang,et al.  A hybrid wind speed forecasting model based on phase space reconstruction theory and Markov model: A case study of wind farms in northwest China , 2015 .

[9]  Wei Sun,et al.  Wind speed forecasting using FEEMD echo state networks with RELM in Hebei, China , 2016 .

[10]  Shervin Motamedi,et al.  Extreme learning machine approach for sensorless wind speed estimation , 2016 .

[11]  Jianzhou Wang,et al.  Forecasting wind speed using empirical mode decomposition and Elman neural network , 2014, Appl. Soft Comput..

[12]  Hui Liu,et al.  An EMD-recursive ARIMA method to predict wind speed for railway strong wind warning system , 2015 .

[13]  Tolga Taner,et al.  Energy and Economic Analysis of the Wind Turbine Plant’s Draft for the Aksaray City , 2014 .

[14]  B. K. Panigrahi,et al.  A hybrid wavelet-ELM based short term price forecasting for electricity markets , 2014 .

[15]  Qing Xiao,et al.  Heihe Watershed Allied Telemetry Experimental Research (HiWATER): Scientific Objectives and Experimental Design , 2013 .

[16]  Jeyraj Selvaraj,et al.  Long-term electrical energy consumption formulating and forecasting via optimized gene expression programming , 2017 .

[17]  Sancho Salcedo-Sanz,et al.  Feature selection in wind speed prediction systems based on a hybrid coral reefs optimization – Extreme learning machine approach , 2014 .

[18]  Xiaoming Zha,et al.  Wind power prediction method based on regime of switching kernel functions , 2016 .

[19]  Norden E. Huang,et al.  Complementary Ensemble Empirical Mode Decomposition: a Novel Noise Enhanced Data Analysis Method , 2010, Adv. Data Sci. Adapt. Anal..

[20]  Hui Liu,et al.  Wind speed forecasting approach using secondary decomposition algorithm and Elman neural networks , 2015 .

[21]  Chao Huang,et al.  An EPC Forecasting Method for Stock Index Based on Integrating Empirical Mode Decomposition, SVM and Cuckoo Search Algorithm , 2014 .

[22]  Chengwei Li,et al.  Improved CEEMDAN and PSO-SVR Modeling for Near-Infrared Noninvasive Glucose Detection , 2016, Comput. Math. Methods Medicine.

[23]  Norden E. Huang,et al.  Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..

[24]  Jiemin Wang,et al.  Intercomparison of surface energy flux measurement systems used during the HiWATER‐MUSOEXE , 2013 .

[25]  C. W. Tong,et al.  RETRACTED ARTICLE: Application of extreme learning machine for estimation of wind speed distribution , 2016, Climate Dynamics.

[26]  Jianzhou Wang,et al.  A hybrid forecasting approach applied to wind speed time series , 2013 .

[27]  María Eugenia Torres,et al.  Improved complete ensemble EMD: A suitable tool for biomedical signal processing , 2014, Biomed. Signal Process. Control..

[28]  Xin Li,et al.  Integrated research methods in watershed science , 2015, Science China Earth Sciences.

[29]  Shaomin Liu,et al.  A comparison of eddy-covariance and large aperture scintillometer measurements with respect to the energy balance closure problem , 2011 .

[30]  Lucy Pao,et al.  Optimal Control of Wind Energy Systems: Towards a Global Approach (Munteanu, I. et al.; 2008) [Bookshelf] , 2009, IEEE Control Systems.

[31]  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 .

[32]  Hui Liu,et al.  Four wind speed multi-step forecasting models using extreme learning machines and signal decomposing algorithms , 2015 .

[33]  Zaccheus O. Olaofe,et al.  A 5-day wind speed & power forecasts using a layer recurrent neural network (LRNN) , 2014 .

[34]  Y. Ge,et al.  Upscaling evapotranspiration measurements from multi-site to the satellite pixel scale over heterogeneous land surfaces , 2016 .

[35]  Liangxu Wang,et al.  A multiscale dataset for understanding complex eco-hydrological processes in a heterogeneous oasis system , 2017 .

[36]  Wenyu Zhang,et al.  A novel hybrid approach for wind speed prediction , 2014, Inf. Sci..

[37]  Mohamed Machmoum,et al.  Control of a wind energy conversion system equipped by a DFIG for active power generation and power quality improvement , 2013 .

[38]  Qisheng Yan,et al.  Forecasting Uranium Resource Price Prediction by Extreme Learning Machine with Empirical Mode Decomposition and Phase Space Reconstruction , 2014 .

[39]  Yong Yu,et al.  Sales forecasting using extreme learning machine with applications in fashion retailing , 2008, Decis. Support Syst..

[40]  Hui Liu,et al.  Comparison of new hybrid FEEMD-MLP, FEEMD-ANFIS, Wavelet Packet-MLP and Wavelet Packet-ANFIS for wind speed predictions , 2015 .