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Weather forecasting based on hybrid decomposition methods and adaptive deep learning strategy

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Abstract

Many global climate-affecting factors are combined to influence the weather and the most challenging ones are the wind speed and the air temperature. The objective of our study is to build a reliable model, capable of handling both data different behaviors for an efficient 12 hours-ahead prediction of each parameter separately. A hybrid strategy called OVMD-DWT-Attention-based-Adaptive-mLSTM was proposed for this purpose, based on a double decomposition method that combines the optimized variational mode decomposition (OVMD) algorithm with the discrete wavelet transform (DWT) technique, that proved its efficiency in extracting the appropriate features, and pre-processing both datasets, in order to reach the desired data denoised effect. The denoised high and low-frequency sub-sequences of each parameter resulted are then forecasted separately using the Adaptive-Multiplicative-LSTM (Adaptive-mLSTM) model to eliminate the inter-correlations between the sub-signals. Finally, each parameter predicted results are reconstructed and fitted independently to the Attention-based-Adaptive mLSTM proposed model for the final prediction. A benchmark of models was implemented for comparison purpose and evaluated over both wind speed and air temperature datasets characteristics, collected from three different stations, where the proposed strategy showed the best and the more consistent results.

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Data availability

In this paper, the wind speed and air temperature datasets were obtained from Raspisaniye Pogodi Ltd, “Weather for 243 countries of the world” site. URL: https://rp5.ru/Weather-in- the-world.

References

  1. Thangprasert N, Suwanarat S (2017) September. The relationships between wind speed and temperature time series in Bangkok, Thailand. In: Journal of physics: conference series (Vol 901, No 1, p 012043). IOP Publishing

  2. Liu X, Xie L, Wang Y, Zou J, Xiong J, Ying Z, Vasilakos AV (2021) Privacy and security issues in deep learning: a survey. IEEE Access 9:4566–4593

    Article  Google Scholar 

  3. Oseni A, Moustafa N, Janicke H, Liu P, Tari Z, Vasilakos A (2021) Security and privacy for artificial intelligence: Opportunities and challenges. arXiv preprint arXiv:2102.04661

  4. Zouaidia K, Ghanemi S, Rais MS et al (2021) Hybrid intelligent framework for one-day ahead wind speed forecasting. Neural Comput Appl 33:16591–16608. https://doi.org/10.1007/s00521-021-06255-5

    Article  Google Scholar 

  5. Wooten RD (2011) Statistical analysis of the relationship between wind speed, pressure and temperature. J Appl Sci 11(15):2712–2722. https://doi.org/10.3923/jas.2011.2712.2722

    Article  Google Scholar 

  6. Rais MS, Zouaidia K, Boudour R (2022) Enhanced decision making in multi-scenarios for autonomous vehicles using alternative bidirectional Q network. Neural Comput Appl. https://doi.org/10.1007/s00521-022-07278-2

    Article  Google Scholar 

  7. Rais MS, Boudour R, Zouaidia K et al (2022) Decision making for autonomous vehicles in highway scenarios using Harmonic SK Deep SARSA. Appl Intell. https://doi.org/10.1007/s10489-022-03357-y

    Article  Google Scholar 

  8. Wang GG, Lu M, Dong YQ et al (2016) Self-adaptive extreme learning machine. Neural Comput Appl 27:291–303. https://doi.org/10.1007/s00521-015-1874-3

    Article  Google Scholar 

  9. Yi JH, Wang J, Wang GG (2016) Improved probabilistic neural networks with self-adaptive strategies for transformer fault diagnosis problem. Adv Mech Eng. https://doi.org/10.1177/1687814015624832

    Article  Google Scholar 

  10. Wang Y, Qiao X, Wang GG (2022) Architecture evolution of convolutional neural network using monarch butterfly optimization. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-022-03766-4

    Article  Google Scholar 

  11. Cui Z, Xue F, Cai X, Cao Y, Wang GG, Chen J (2018) Detection of malicious code variants based on deep learning. IEEE Trans Indus Inform 14(7):3187–3196

    Article  Google Scholar 

  12. Li C, Xiao Z, Xia X, Zou W, Zhang C (2018) A hybrid model based on synchronous optimisation for multi-step short-term wind speed forecasting. Appl Energy 215:131–144. https://doi.org/10.1016/j.apenergy.2018.01.094

    Article  Google Scholar 

  13. Ren Y, Suganthan PN, Srikanth N (2017) A comparative study of empirical mode decomposition-based short-term wind speed forecasting methods. IEEE Trans Sustain Energy 6(1):236–244. https://doi.org/10.1109/TSTE.2014.2365580

    Article  Google Scholar 

  14. Liu H-Q, Tian D.-F. Pan, Li Y-F (2013) Forecasting models for wind speed using wavelet, wavelet packet, time series and artificial neural networks. Appl Energy 107:191–208. https://doi.org/10.1016/j.apenergy.2013.02.002

    Article  Google Scholar 

  15. Barbosh M, Singh P, Sadhu A (2020) Empirical mode decomposition and its variants: a review with applications in structural health monitoring. Smart Mater Struct 29(9):093001. https://doi.org/10.1088/1361-665X/aba539

    Article  Google Scholar 

  16. Dragomiretskiy K, Zosso D (2013) Variational mode decomposition. IEEE Trans Signal Process 62(3):531–544. https://doi.org/10.1109/TSP.2013.2288675

    Article  MATH  MathSciNet  Google Scholar 

  17. Khouloud Z, Saber RM (2021, September) Multi-step wind speed forecasting based on hybrid deep learning model and trailing moving average denoising technique. In: 2021 international conference on recent advances in mathematics and informatics (ICRAMI) (pp 1-5). IEEE

  18. Zouada K, Ghanem S, Ras MS (2021) Hourly wind speed forecasting using FFT-encoder-decoder-LSTM in south west of Algeria (Adrar). Int J Inform Appl Math 4(1):72–83

    Google Scholar 

  19. Wang GG, Deb S, Coelho LD (2018) Earthworm optimisation algorithm: a bio-inspired metaheuristic algorithm for global optimisation problems. Int J Bio-inspired Comput 12(1):1–22. https://doi.org/10.1504/IJBIC.2018.093328

    Article  Google Scholar 

  20. Wang GG (2018) Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems. Memetic Comput 10(2):151–164. https://doi.org/10.1007/s12293-016-0212-3

    Article  Google Scholar 

  21. Wang GG, Deb S, Cui Z (2019) Monarch butterfly optimization. Neural Comput Appl 31:1995–2014. https://doi.org/10.1007/s00521-015-1923-y

    Article  Google Scholar 

  22. Liu H, Tian H, Liang X, Li Y (2015) Wind speed forecasting approach using secondary decomposition algorithm and Elman neural networks. Appl Energy 157:183–94. https://doi.org/10.1016/j.apenergy.2015.08.014

    Article  Google Scholar 

  23. Liu H, Duan Z, Han F, Li Y (2018) Big multi-step wind speed forecasting model based on secondary decomposition, ensemble method and error correction algorithm. Energy Convers Manag 156:525–41. https://doi.org/10.1016/j.enconman.2017.11.049

    Article  Google Scholar 

  24. Zhang D, Peng X, Pan K, Liu Y (2019) A novel wind speed forecasting based on hybrid decomposition and online sequential outlier robust extreme learning machine. Energy Convers Manag 180:338–57. https://doi.org/10.1016/j.enconman.2018.10.089

    Article  Google Scholar 

  25. Sun N, Zhou J, Chen L, Jia B, Tayyab M, Peng T (2018) An adaptive dynamic short-term wind speed forecasting model using secondary decomposition and an improved regularized extreme learning machine. Energy 165:939–57. https://doi.org/10.1016/j.energy.2018.09.180

    Article  Google Scholar 

  26. Moreno SR, da Silva RG, Mariani VC, dos Santos CL (2020) Multi-step wind speed forecasting based on hybrid multi-stage decomposition model and long short-term memory neural network. Energy Convers Manag 213:112869. https://doi.org/10.1016/j.enconman.2020.112869

    Article  Google Scholar 

  27. Zhang Y, Chen B, Pan G, Zhao Y (2019) A novel hybrid model based on VMD-WT and PCA-BP-RBF neural network for short-term wind speed forecasting. Energy Convers Manag 195:180–197. https://doi.org/10.1016/j.enconman.2019.05.005

    Article  Google Scholar 

  28. Huang N, Wu Y, Cai G, Zhu H, Yu C, Jiang L, Zhang Y, Zhang J, Xing E (2019) Short-term wind speed forecast with low loss of information based on feature generation of OSVD. IEEE Access 7:81027–81046. https://doi.org/10.1109/ACCESS.2019.2922662

    Article  Google Scholar 

  29. Liu H, Mi X, Li Y (2018) Smart multi-step deep learning model for wind speed forecasting based on variational mode decomposition, singular spectrum analysis, LSTM network and ELM. Energy Convers Manag 159:54–64. https://doi.org/10.1016/j.enconman.2018.01.010

    Article  Google Scholar 

  30. Liu Y, Guan L, Hou C, Han H, Liu Z, Sun Y, Zheng M (2019) Wind power short-term prediction based on LSTM and discrete wavelet transform. Appl Sci 9(6):1108

    Article  Google Scholar 

  31. Liao X, Liu Z, Deng W (2021) Short-term wind speed multistep combined forecasting model based on two-stage decomposition and LSTM. Wind Energy 24(9):991–1012

    Article  Google Scholar 

  32. Qian J, Zhu M, Zhao Y, He X (2021) Short-term wind speed prediction with a two-layer attention-based LSTM. Computer Syst Sci Eng 39(2):197–209

    Article  Google Scholar 

  33. Zouaidia K, Ghanemi S, Rais MS (2021) Wind speed forecasting based on discrete wavelet transform, moving average method and gated recurrent unit. In: Hatti M (ed) Artificial intelligence and renewables towards an energy transition. ICAIRES 2020. Lecture notes in networks and systems, vol 174. Springer, Cham. https://doi.org/10.1007/978-3-030-63846-7_8

    Chapter  Google Scholar 

  34. Krause B, Lu L, Murray I, Renals S (2016) Multiplicative LSTM for sequence modelling. arXiv preprint arXiv:1609.07959. https://doi.org/10.48550/arXiv.1609.07959

  35. Kim Y, Denton C, Hoang L, Rush AM (2017) Structured attention networks. arXiv preprint arXiv:1702.00887. https://doi.org/10.48550/arXiv.1702.00887

  36. Raspisaniye Pogodi Ltd.: Weather for 243 countries of the world. https://rp5.ru/Weather-in- the-world. Accessed 12 Dec 2021

  37. Daaou-Nedjari H, Haddouche SK, Balehouane A, Guerri O (2018) Optimal windy sites in Algeria: potential and perspectives. Energy 147:1240–1255. https://doi.org/10.1016/j.energy.2017.12.046

    Article  Google Scholar 

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Authors and Affiliations

  1. Embedded Systems Laboratory, Badji Mokhtar University, Annaba, Algeria

    Khouloud Zouaidia, Mohamed Saber Rais & Salim Ghanemi

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  1. Khouloud Zouaidia
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  2. Mohamed Saber Rais
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  3. Salim Ghanemi
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Correspondence to Khouloud Zouaidia.

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Zouaidia, K., Rais, M.S. & Ghanemi, S. Weather forecasting based on hybrid decomposition methods and adaptive deep learning strategy. Neural Comput & Applic 35, 11109–11124 (2023). https://doi.org/10.1007/s00521-023-08288-4

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