GA-based design of optimal discrete wavelet filters for efficient wind speed forecasting

Wind energy is getting more and more integrated into power grids, giving rise to some challenges because of its inherent intermittent and irregular nature. Wind speed forecasting plays a fundamental role in overcoming such challenging issues and, thus, assisting the power utility manager in optimizing the supply–demand balancing through wind energy generation. This paper suggests a new hybrid scheme WNN, based on discrete wavelet transform (DWT) combined with artificial neural network (ANN), for wind speed forecasting. More specifically, this work aims at designing the most appropriate discrete wavelet filters, best adapted to a one day ahead wind speed forecasting. The optimized DWT filters are intended to effectively preprocess the wind speed time series data in order to enhance the prediction accuracy. Using wind speed data collected from three different locations in the Magherbian region, the obtained simulation results indicate that the proposed approach outperforms other conventional wavelet-based forecasting structures regarding the wind speed prediction precision. Moreover, compared to the standard wavelet ‘db4’ based approach, the optimized wavelet filter-based structure leads to a forecasting accuracy improvement, in terms of RMSE and MAPE index errors, that amounts to nearly 13% and 19%, respectively.

[1]  Khaled Khelil,et al.  Efficient Wind Speed Forecasting Using Discrete Wavelet Transform and Artificial Neural Networks , 2019, Rev. d'Intelligence Artif..

[2]  Abheejeet Mohapatra,et al.  Repeated wavelet transform based ARIMA model for very short-term wind speed forecasting , 2019, Renewable Energy.

[3]  Ranjit Kumar Paul,et al.  Comparative performance of wavelet-based neural network approaches , 2019, Neural Computing and Applications.

[4]  Darío Baptista,et al.  Comparing different solutions for forecasting the energy production of a wind farm , 2018, Neural Computing and Applications.

[5]  Hui Liu,et al.  A novel ensemble model of different mother wavelets for wind speed multi-step forecasting , 2018, Applied Energy.

[6]  Robert Bregovic,et al.  Multirate Systems and Filter Banks , 2002 .

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

[8]  Milan Stehlík,et al.  Predicting hourly ozone concentrations using wavelets and ARIMA models , 2019, Neural Computing and Applications.

[9]  Taher Niknam,et al.  Probabilistic wind power forecasting using a novel hybrid intelligent method , 2016, Neural Computing and Applications.

[10]  Y. Meyer,et al.  Wavelets and Filter Banks , 1991 .

[11]  Naif Alajlan,et al.  A wavelet optimization approach for ECG signal classification , 2012, Biomed. Signal Process. Control..

[12]  Barry G. Sherlock,et al.  On the space of orthonormal wavelets , 1998, IEEE Trans. Signal Process..

[13]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Mehmet Kurban,et al.  Short-term load forecasting without meteorological data using AI-based structures , 2015 .

[15]  Barry G. Sherlock,et al.  Optimized wavelets for fingerprint compression , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[16]  Fausto Pedro García Márquez,et al.  A survey of artificial neural network in wind energy systems , 2018, Applied Energy.

[17]  Aslam P. Memon,et al.  A new optimal feature selection algorithm for classification of power quality disturbances using discrete wavelet transform and probabilistic neural network , 2017 .

[18]  Ping Jiang,et al.  Two combined forecasting models based on singular spectrum analysis and intelligent optimized algorithm for short-term wind speed , 2016, Neural Computing and Applications.

[19]  Mehmet Ali Kölmek,et al.  Forecasting the day-ahead price in electricity balancing and settlement market of Turkey by using artificial neural networks , 2015 .

[20]  P. K. Dash,et al.  Short-term prediction of wind power using a hybrid pseudo-inverse Legendre neural network and adaptive firefly algorithm , 2017, Neural Computing and Applications.

[21]  Joao P. S. Catalao,et al.  Short-term wind power forecasting in Portugal by neural networks and wavelet transform , 2011 .

[22]  Jianchun Peng,et al.  A review of deep learning for renewable energy forecasting , 2019, Energy Conversion and Management.

[23]  Chi Zhang,et al.  Time series forecasting based on wavelet decomposition and feature extraction , 2016, Neural Computing and Applications.

[24]  Hui Liu,et al.  Deterministic wind energy forecasting: A review of intelligent predictors and auxiliary methods , 2019, Energy Conversion and Management.

[25]  Farid Melgani,et al.  Optimizing wavelets for hyperspectral image classification , 2009, 2009 IEEE International Geoscience and Remote Sensing Symposium.

[26]  Hamidreza Zareipour,et al.  Forecasting aggregated wind power production of multiple wind farms using hybrid wavelet‐PSO‐NNs , 2014 .

[27]  Robert X. Gao,et al.  Wavelets for fault diagnosis of rotary machines: A review with applications , 2014, Signal Process..

[28]  Hamidreza Zareipour,et al.  A review and discussion of decomposition-based hybrid models for wind energy forecasting applications , 2019, Applied Energy.

[29]  Dongxiao Niu,et al.  Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm , 2014 .