Ensemble empirical mode decomposition based adaptive wavelet neural network method for wind speed prediction

Abstract Wind energy is one of the emerging sustainable sources of electricity. Wind is intermittent in nature. The typical grid operation of wind energy is complex. The significance of wind energy generation and integration with the grid is increasing day by day. An accurate wind speed forecasting method will help the utility planners and operators to meet the balance of supply and demand by generating wind energy. In this paper, a statistical-based wind speed prediction is implemented without utilizing the numerical weather prediction inputs. This analytical study proposes a hybrid short-term prediction approach that can successfully preprocess the original wind speed data to enhance the forecasting accuracy. The most efficient signal decomposition algorithm, Ensemble Empirical Mode Decomposition is used for preprocessing. This ensemble empirical mode decomposition technique decomposes the original wind speed data. Each decomposed signal is regressed to forecast the future wind speed value by utilizing the Adaptive Wavelet Neural Network model. The proposed hybrid approach is subsequently investigated with respect to the wind farm of South India. The results from a real-world case study in India are reported along with comprehensive comparison. The prediction performance delivered high accuracy, less uncertainty and low computational burden in the forecasts attained. The developed hybrid model outperforms the six other benchmark models such as persistence method, back propagation neural network, radial basis function neural network, Elman neural network, Gaussian regression neural network, and wavelet neural network.

[1]  Akin Tascikaraoglu,et al.  Evaluation of spatio-temporal forecasting methods in various smart city applications , 2018 .

[2]  Tingting Zhu,et al.  Short-term wind speed forecasting using empirical mode decomposition and feature selection , 2016 .

[3]  K. Satheesh Kumar,et al.  Improved week-ahead predictions of wind speed using simple linear models with wavelet decomposition , 2016 .

[4]  Ulagammai Meyyappan,et al.  Wavelet neural network–based wind speed forecasting and application of shuffled frog leap algorithm for economic dispatch with prohibited zones incorporating wind power , 2018 .

[5]  Neven Duić,et al.  A state-of-the-art review and feasibility analysis of high altitude wind power in Northern Ireland , 2017 .

[6]  A. Walden,et al.  Wavelet Methods for Time Series Analysis , 2000 .

[7]  Jun Liang,et al.  Day-ahead unit commitment method considering time sequence feature of wind power forecast error , 2018 .

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

[9]  Jari Backman,et al.  Wind Resource Assessment and Forecast Planning with Neural Networks , 2014 .

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

[11]  Qi-Hu Li,et al.  Optimum block-adaptive learning algorithm for error back-propagation networks , 1992, IEEE Trans. Signal Process..

[12]  Hui Liu,et al.  New wind speed forecasting approaches using fast ensemble empirical model decomposition, genetic algorithm, Mind Evolutionary Algorithm and Artificial Neural Networks , 2015 .

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

[14]  Li De,et al.  Artificial Intelligence with Uncertainty , 2004 .

[15]  Rasool Azimi,et al.  A hybrid wind power forecasting model based on data mining and wavelets analysis , 2016 .

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

[17]  N. Pindoriya,et al.  An Adaptive Wavelet Neural Network-Based Energy Price Forecasting in Electricity Markets , 2008, IEEE Transactions on Power Systems.

[18]  Hao Yin,et al.  Wind speed forecasting based on wavelet packet decomposition and artificial neural networks trained by crisscross optimization algorithm , 2016 .

[19]  Jianzhou Wang,et al.  Medium-term wind speeds forecasting utilizing hybrid models for three different sites in Xinjiang, China , 2015 .

[20]  Yanfei Li,et al.  Comparison of two new intelligent wind speed forecasting approaches based on Wavelet Packet Decomposition, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Artificial Neural Networks , 2018 .

[21]  Marc Keyser,et al.  Knowledge Is Power: Efficiently Integrating Wind Energy and Wind Forecasts , 2013, IEEE Power and Energy Magazine.

[22]  Edris Pouresmaeil,et al.  Distributed energy resources and benefits to the environment , 2010 .

[23]  Joao P. S. Catalao,et al.  Short-term wind power forecasting using adaptive neuro-fuzzy inference system combined with evolutionary particle swarm optimization, wavelet transform and mutual information , 2015 .

[24]  Kameshwar Poolla,et al.  Exploiting sparsity of interconnections in spatio-temporal wind speed forecasting using Wavelet Transform , 2016 .

[25]  Chuanjin Yu,et al.  Comparative study on three new hybrid models using Elman Neural Network and Empirical Mode Decomposition based technologies improved by Singular Spectrum Analysis for hour-ahead wind speed forecasting , 2017 .

[26]  Hui Liu,et al.  Forecasting models for wind speed using wavelet, wavelet packet, time series and Artificial Neural Networks , 2013 .

[27]  Ponnuthurai N. Suganthan,et al.  A Novel Empirical Mode Decomposition With Support Vector Regression for Wind Speed Forecasting , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[28]  Ying Wang,et al.  Optimal Wind Power Uncertainty Intervals for Electricity Market Operation , 2018, IEEE Transactions on Sustainable Energy.

[29]  Yanfei Li,et al.  Wind speed forecasting method based on deep learning strategy using empirical wavelet transform, long short term memory neural network and Elman neural network , 2018 .

[30]  Hamidreza Zareipour,et al.  Wind power forecast using wavelet neural network trained by improved Clonal selection algorithm , 2015 .

[31]  Y.-Y. Hsu,et al.  Short term load forecasting using a multilayer neural network with an adaptive learning algorithm , 1992 .

[32]  Yan Jiang,et al.  Short-term wind speed prediction: Hybrid of ensemble empirical mode decomposition, feature selection and error correction , 2017 .

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

[34]  Feng Qian,et al.  Multi-step wind speed forecasting based on a hybrid forecasting architecture and an improved bat algorithm , 2017 .

[35]  Xiaoru Wang,et al.  A novel hybrid methodology for short-term wind power forecasting based on adaptive neuro-fuzzy inference system , 2017 .

[36]  Li Li,et al.  A new method based on Type-2 fuzzy neural network for accurate wind power forecasting under uncertain data , 2018, Renewable Energy.

[37]  Osamah Basheer Shukur,et al.  Daily wind speed forecasting through hybrid KF-ANN model based on ARIMA , 2015 .

[38]  Kodjo Agbossou,et al.  Time series prediction using artificial wavelet neural network and multi-resolution analysis: Application to wind speed data , 2016 .

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

[40]  Taher Niknam,et al.  Bundled Generation and Transmission Planning Under Demand and Wind Generation Uncertainty Based on a Combination of Robust and Stochastic Optimization , 2018, IEEE Transactions on Sustainable Energy.

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

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

[43]  H. J. Lu,et al.  An improved neural network-based approach for short-term wind speed and power forecast , 2017 .

[44]  Afshin Ebrahimi,et al.  A novel hybrid approach for predicting wind farm power production based on wavelet transform, hybrid neural networks and imperialist competitive algorithm , 2016 .