Abstract
Wind energy is a green source of electricity that is growing faster than other renewable energies. However, dependent mainly on wind speed, this source is characterized by the randomness and fluctuation that makes challenging optimal management. In order to remedy this inconvenience, it is essential to predict meteorological data or power produced by generators. In this paper, we present a wind power forecasting approach based on regularized extreme learning machine algorithm (R-ELM), particle swarm optimization method (PSO), and AutoEncoder network (AE) so-called AutoEncoder-optimal regularized extreme learning machine (AE-ORELM). Firstly, we train the AE model by the ELM algorithm. Then, the output weights resulting are used as the input weights of the R-ELM model. Furthermore, the PSO method is used to optimally select hyperparameters of the whole model, namely the regularization parameter and the number of hidden nodes in the hidden layer. The simulation results show that the proposed AE-ORELM can achieve better testing accuracy with a faster training time compared to related models.
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References
El Bourakadi D, Yahyaouy A, Boumhidi J (2018) Multi-agent system based on the extreme learning machine and fuzzy control for intelligent energy management in microgrid. J Intell Syst 29(1):877–893. https://doi.org/10.1515/jisys-2018-0125
Duman S, Li J, Wu L, Guvenc U (2020) Optimal power flow with stochastic wind power and FACTS devices: a modified hybrid PSOGSA with chaotic maps approach. Neural Comput Appl 32(12):8463–8492. https://doi.org/10.1007/s00521-019-04338-y
Hao M, Zhang W, Wang Y, Lu G, Wang F, Vasilakos AV (2020) Fine-grained powercap allocation for power-constrained systems based on multi-objective machine learning. IEEE Trans Parallel Distrib Syst. https://doi.org/10.1109/TPDS.2020.3045983
Zhu R, Liao W, Wang Y (2020) Short-term prediction for wind power based on temporal convolutional network. Energy Rep 6:424–429. https://doi.org/10.1016/j.egyr.2020.11.219
Bera B, Saha S, Das AK, Vasilakos AV (2021) Designing blockchain-based access control protocol in IoT-enabled smart-grid system. IEEE Internet Things J 8(7):5744–5761. https://doi.org/10.1109/JIOT.2020.3030308
Cassola F, Burlando M (2012) Wind speed and wind energy forecast through Kalman filtering of numerical weather prediction model output. Appl Energy 99:154–166. https://doi.org/10.1016/j.apenergy.2012.03.054
Elamine DO, Serraji M, Nfaoui EH, Boumhidi J (2016) Multi-agent architecture for optimal energy management of a smart micro-grid using a weighted hybrid BP-PSO algorithm for wind power prediction. Int J Technol Intell Planning 11(1):20–35. https://doi.org/10.1504/IJTIP.2016.074228
Lujano-Rojas JM, Bernal-Agustín JL, Dufo-López R, Domínguez-Navarro JA (2011) Forecast of hourly average wind speed using ARMA model with discrete probability transformation. In: Zhu M (ed) Electrical engineering and control. Berlin, Heidelberg, pp 1003–1010. https://doi.org/10.1007/978-3-642-21765-4_125
Riahy GH, Abedi M (2008) Short term wind speed forecasting for wind turbine applications using linear prediction method. Renew Energy 33(1):35–41. https://doi.org/10.1016/j.renene.2007.01.014
Hervás-Martínez C, Salcedo-Sanz S, Gutiérrez PA, Ortiz-García EG, Prieto L (2012) Evolutionary product unit neural networks for short-term wind speed forecasting in wind farms. Neural Comput Appl 21(5):993–1005. https://doi.org/10.1007/s00521-011-0582-x
Ulkat D, Günay ME (2018) Prediction of mean monthly wind speed and optimization of wind power by artificial neural networks using geographical and atmospheric variables: case of Aegean Region of Turkey. Neural Comput Appl 30(10):3037–3048. https://doi.org/10.1007/s00521-017-2895-x
Chen N, Qian Z, Nabney IT, Meng X (2014) Wind power forecasts using Gaussian processes and numerical weather prediction. IEEE Trans Power Syst 29(2):656–665. https://doi.org/10.1109/TPWRS.2013.2282366
el Bourakadi D, Yahyaouy A, Boumhidi J (2019) Multi-agent system based sequential energy management strategy for Micro-Grid using optimal weighted regularized extreme learning machine and decision tree. Intell. Decis. Technol. 13(4):479–494. https://doi.org/10.3233/IDT-190003
El Bourakadi D, Ali Y, Jaouad B (2017) Multi-agent system based on the fuzzy control and extreme learning machine for intelligent management in hybrid energy system,” In: 2017 intelligent systems and computer vision (ISCV), pp 1–6. https://doi.org/10.1109/ISACV.2017.8054922
Wang H, Lei Z, Zhang X, Zhou B, Peng J (2019) A review of deep learning for renewable energy forecasting. Energy Convers Manage 198:111799. https://doi.org/10.1016/j.enconman.2019.111799
Singh SN, 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
Yang D (2019) On post-processing day-ahead NWP forecasts using Kalman filtering. Sol Energy 182:179–181. https://doi.org/10.1016/j.solener.2019.02.044
Wang Y, Wang J, Wei X (2015) A hybrid wind speed forecasting model based on phase space reconstruction theory and Markov model: a case study of wind farms in northwest China. Energy 91:556–572. https://doi.org/10.1016/j.energy.2015.08.039
Mat Daut MA, Hassan MY, Abdullah H, Rahman HA, Abdullah MP, Hussin F (2017) Building electrical energy consumption forecasting analysis using conventional and artificial intelligence methods: a review. Renew Sustain Energy Rev 70:1108–1118. https://doi.org/10.1016/j.rser.2016.12.015
Shi J, Guo J, Zheng S (2012) Evaluation of hybrid forecasting approaches for wind speed and power generation time series. Renew Sustain Energy Rev 16(5):3471–3480. https://doi.org/10.1016/j.rser.2012.02.044
Gangui Y et al (2012) The ultra-short term prediction of wind power based on chaotic time series. Energy Procedia 17:1490–1496. https://doi.org/10.1016/j.egypro.2012.02.271
Sun G et al (2018) Short-term wind power forecasts by a synthetical similar time series data mining method. Renew Energy 115:575–584. https://doi.org/10.1016/j.renene.2017.08.071
Ren Y, Suganthan PN (2014) Empirical mode decomposition-k nearest neighbor models for wind speed forecasting. JPEE 02(04):176–185. https://doi.org/10.4236/jpee.2014.24025
Jiang P, Qin S, Wu J, Sun B (2015) Time series analysis and forecasting for wind speeds using support vector regression coupled with artificial intelligent algorithms. Math Probl Eng 2015:e939305. https://doi.org/10.1155/2015/939305
Heinermann J, Kramer O (2016) Machine learning ensembles for wind power prediction. Renew Energy 89:671–679. https://doi.org/10.1016/j.renene.2015.11.073
Yang X, Fu G, Zhang Y, Kang N, Gao F (2017) A naive bayesian wind power interval prediction approach based on rough set attribute reduction and weight optimization. Energies. https://doi.org/10.3390/en10111903
Masoumi A, Jabari F, Mohammadi-ivatloo B (2017) Wind speed forecasting using back propagation artificial neural networks in North of Iran. J Energy Manag Technol 1:21–25. https://doi.org/10.22109/JEMT.2017.91014.1026
Yu C, Li Y, Bao Y, Tang H, Zhai G (2018) A novel framework for wind speed prediction based on recurrent neural networks and support vector machine. Energy Convers Manage 178:137–145. https://doi.org/10.1016/j.enconman.2018.10.008
Huang GB et al (2019) Extreme learning machine: A new learning scheme of feedforward neural networks, Accessed: Nov. 20, 2019. [Online]. Available: https://www.scienceopen.com/document?vid=111340da-844e-4307-a5b3-4e718da26e28
Pan C, Park D, Yang Y, Yoo H (2012) Leukocyte image segmentation by visual attention and extreme learning machine. Neural Comput Appl NCA 21:1–11. https://doi.org/10.1007/s00521-011-0522-9
Minhas R, Baradarani A, Seifzadeh S, Jonathan Wu QM (2010) Human action recognition using extreme learning machine based on visual vocabularies. Neurocomputing 73(10):1906–1917. https://doi.org/10.1016/j.neucom.2010.01.020
Roul R, Nanda A, Patel V, Sahay S (2015) Extreme learning machines in the field of text classification. https://doi.org/10.1109/SNPD.2015.7176204
Wan C, Xu Z, Pinson P, Dong ZY, Wong KP (2014) Probabilistic forecasting of wind power generation using extreme learning machine. IEEE T Power Syst. https://doi.org/10.1109/TPWRS.2013.2287871
Lazarevska E (2016) Wind speed prediction with extreme learning machine, In: 2016 IEEE 8th international conference on intelligent systems (IS), 154–159. https://doi.org/10.1109/IS.2016.7737415
Li N, He F, Ma W (2019) Wind power prediction based on extreme learning machine with kernel mean p-power error loss. Energies. https://doi.org/10.3390/en12040673
Acikgoz H, Yildiz C, Sekkeli M (2020) An extreme learning machine based very short-term wind power forecasting method for complex terrain. Energy Source Part A Recover Util Environ Eff 42(22):2715–2730. https://doi.org/10.1080/15567036.2020.1755390
Pantazi XE, Moshou D, Bochtis D (2020) Artificial intelligence in agriculture, In: Intelligent data mining and fusion systems in agriculture, Elsevier, 17–101. https://doi.org/10.1016/B978-0-12-814391-9.00002-9
Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1):489–501. https://doi.org/10.1016/j.neucom.2005.12.126
Deng W, Zheng Q, Chen L (2009) Regularized extreme learning machine, In: 2009 IEEE symposium on computational intelligence and data mining, 389–395. https://doi.org/10.1109/CIDM.2009.4938676
Kasun L, Zhou H, Huang G-B, Vong C-M (2013) Representational learning with ELMs for big data. IEEE Intell Syst 28:31–34
Hanifi S, Liu X, Lin Z, Lotfian S (2020) A critical review of wind power forecasting methods—past, present and future. Energies. https://doi.org/10.3390/en13153764
Wang Z, Wang F, Su S (2011) Solar irradiance short-term prediction model based on BP neural network. Energy Procedia 12:488–494. https://doi.org/10.1016/j.egypro.2011.10.065
Xing D, Qin B, Li C (2017) Short-term wind speed forecasting using regularization extreme learning machine. DEStech Trans Engl Technol Res. https://doi.org/10.12783/dtetr/icmme2017/9082
Miranda L (2021) ljvmiranda921/pyswarms. 2021. Accessed: Sep. 21, 2021. [Online]. https://github.com/ljvmiranda921/pyswarms
Liu X et al (2021) Privacy and security issues in deep learning: a survey. IEEE Access 9:4566–4593. https://doi.org/10.1109/ACCESS.2020.3045078
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El Bourakadi, D., Yahyaouy, A. & Boumhidi, J. Improved extreme learning machine with AutoEncoder and particle swarm optimization for short-term wind power prediction. Neural Comput & Applic 34, 4643–4659 (2022). https://doi.org/10.1007/s00521-021-06619-x
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DOI: https://doi.org/10.1007/s00521-021-06619-x