Abstract
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.
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Jiang P, Li R, Zhang K (2018) Two combined forecasting models based on singular spectrum analysis and intelligent optimized algorithm for short-term wind speed. Neural Comput Appl 30:1–19
(2019) GWEC, Global wind power report; 2018
Qian Z, Pei Y, Zareipour H, Chen N (2019) A review and discussion of decomposition-based hybrid models for wind energy forecasting applications. Appl Energy 235:939–953. https://doi.org/10.1016/j.apenergy.2018.10.080
Baptista D, Carvalho JP, Morgado-Dias F (2018) Comparing different solutions for forecasting the energy production of a wind farm. Neural Comput Appl, pp 1–9
Wang H, Lei Z, Zhang X et al (2019) A review of deep learning for renewable energy forecasting. Energy Convers Manag. https://doi.org/10.1016/j.enconman.2019.111799
Aasim SSN, Mohapatra A (2019) Repeated wavelet transform based ARIMA model for very short-term wind speed forecasting. Renew Energy 136:758–768. https://doi.org/10.1016/j.renene.2019.01.031
Mishra SP, Dash PK (2019) Short-term prediction of wind power using a hybrid pseudo-inverse Legendre neural network and adaptive firefly algorithm. Neural Comput Appl 31:2243–2268
Salazar L, Nicolis O, Ruggeri F et al (2019) Predicting hourly ozone concentrations using wavelets and ARIMA models. Neural Comput Appl 31:4331–4340
Liu H, Duan Z, Li Y, Lu H (2018) A novel ensemble model of different mother wavelets for wind speed multi-step forecasting. Appl Energy 228:1783–1800. https://doi.org/10.1016/j.apenergy.2018.07.050
Chang GW, Lu HJ, Chang YR, Lee YD (2017) An improved neural network-based approach for short-term wind speed and power forecast. Renew Energy 105:301–311. https://doi.org/10.1016/j.renene.2016.12.071
Esener İI, Yüksel T, Kurban M (2015) Short-term load forecasting without meteorological data using AI-based structures. Turkish J Electr Eng Comput Sci 23:370–380
Kölmek M, Navruz İ (2015) Forecasting the day-ahead price in electricity balancing and settlement market of Turkey by using artificial neural networks. Turkish J Electr Eng Comput Sci 23:841–852
Liu H, Chen C, Lv X et al (2019) Deterministic wind energy forecasting: a review of intelligent predictors and auxiliary methods. Energy Convers Manag 195:328–345. https://doi.org/10.1016/j.enconman.2019.05.020
Liu T, Wei H, Zhang C, Zhang K (2017) Time series forecasting based on wavelet decomposition and feature extraction. Neural Comput Appl 28:183–195
Anjoy P, Paul RK (2019) Comparative performance of wavelet-based neural network approaches. Neural Comput Appl 31:3443–3453
Catalão JPS, Pousinho HMI, Mendes VMF (2011) Short-term wind power forecasting in Portugal by neural networks and wavelet transform. Renew Energy 36:1245–1251. https://doi.org/10.1016/j.renene.2010.09.016
Liu D, Niu D, Wang H, Fan L (2014) Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm. Renew Energy 62:592–597. https://doi.org/10.1016/j.renene.2013.08.011
Mandal P, Zareipour H, Rosehart WD (2014) Forecasting aggregated wind power production of multiple wind farms using hybrid wavelet-PSO-NNs. Int J Energy Res. https://doi.org/10.1002/er.3171
Berrezzek F, Khelil K, Bouadjila T (2019) Efficient wind speed forecasting using discrete wavelet transform and artificial neural networks. Rev d’Intelligence Artif 33:447–452. https://doi.org/10.18280/ria.330607
Taher MA, Khooban NM (2018) Probabilistic wind power forecasting using a novel hybrid intelligent method. Neural Comput Appl 30:473–485. https://doi.org/10.1007/s00521-016-2703-z
Sherlock BG, Monro DM (1996) Optimized wavelets for fingerprint compression. In: 1996 IEEE international conference on acoustics, speech, and signal processing conference proceedings. Atlanta, GA, USA, pp 1447–1450
Daamouche A, Melgani F, Hamami L (2009) Optimizing wavelets for hyperspectral image classification. Int Geosci Remote Sens Symp 2:302–305. https://doi.org/10.1109/IGARSS.2009.5418070
Daamouche A, Hamami L, Alajlan N, Melgani F (2012) A wavelet optimization approach for ECG signal classification. Biomed Signal Process Control 7:342–349. https://doi.org/10.1016/j.bspc.2011.07.001
Sherlock BG, Monro DM (1998) On the space of orthonormal wavelets. IEEE Trans Signal Process 46:1716–1720. https://doi.org/10.1109/78.678504
Vaidyanathan PP (1993) Multirate systems and filter banks. Prentice-Hall, Englewood Cliffs, NJ
Yan R, Gao RX, Chen X (2014) Wavelets for fault diagnosis of rotary machines: a review with applications. Signal Process 96:1–15. https://doi.org/10.1016/j.sigpro.2013.04.015
Stephane GM (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell II:674–693. https://doi.org/10.1109/34.192463
Khokhar S, Asuhaimi A, Zin M et al (2017) A new optimal feature selection algorithm for classification of power quality disturbances using discrete wavelet transform and probabilistic neural network. Measurement 95:246–259. https://doi.org/10.1016/j.measurement.2016.10.013
Strang G, Nguyen T (1996) Wavelets and Filter Banks. Wellesley-Cambridge Press, Wellesley, MA
Marugán AP, Pedro F, Márquez G et al (2018) A survey of artificial neural network in wind energy systems. Appl Energy 228:1822–1836. https://doi.org/10.1016/j.apenergy.2018.07.084
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This work is supported by the Directorate General of Scientific Research and Technological Development (DGRSDT), Algeria.
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Khelil, K., Berrezzek, F. & Bouadjila, T. GA-based design of optimal discrete wavelet filters for efficient wind speed forecasting. Neural Comput & Applic 33, 4373–4386 (2021). https://doi.org/10.1007/s00521-020-05251-5
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DOI: https://doi.org/10.1007/s00521-020-05251-5