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Energy Consumption Forecasting for the Nonferrous Metallurgy Industry Using Hybrid Support Vector Regression with an Adaptive State Transition Algorithm

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Abstract

The nonferrous metallurgy industry is a major energy consumer in China, and accurate energy consumption forecasting for the nonferrous metallurgy industry can help government policymakers with energy planning. For this purpose, a hybrid support vector regression (HSVR) with an adaptive state transition algorithm (ASTA) named ASTA-HSVR is proposed to forecast energy consumption in the nonferrous metallurgy industry. The proposed support vector regression (SVR) model consists of a linear weighting of 𝜖-SVR and ν-SVR. The ASTA was developed to optimize the parameters of the HSVR. Two cases of energy consumption from the nonferrous metallurgy industry in China are used to demonstrate the performance of the proposed method. The results indicate that the ASTA-HSVR method is superior to other methods. In this study, a hybrid support vector regression with an adaptive state transition algorithm (ASTA-HSVR) was developed and successfully applied to energy consumption forecasting for the nonferrous metallurgy industry. However, it should be noted that the outliers were not considered in this study. In the future, we expect to extend the ASTA-HSVR method to include energy consumption forecasting problems with outliers.

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References

  1. Qiu X, Suganthan PN, Amaratunga GA. Ensemble incremental learning random vector functional link network for short-term electric load forecasting. Knowledge-Based Systems 2018;145:182–96. 6

    Google Scholar 

  2. Aljarah I, Al-Zoubi AM, Faris H, Hassonah MA, Mirjalili S, Saadeh H. Simultaneous feature selection and support vector machine optimization using the grasshopper optimization algorithm. Cogn Comput 2018;10(3):478–95.

    Google Scholar 

  3. Chen Y, Tan H. Short-term prediction of electric demand in building sector via hybrid support vector regression. Appl Energy 2017;204:1363–74.

    Google Scholar 

  4. Gao F, Huang T, Sun J, Wang J, Hussain A, Yang E. 2018. A new algorithm of SAR image target recognition based on improved deep convolutional neural network. Cogn Comput. https://doi.org/10.1007/s12559-018-9563-z.

    CAS  Google Scholar 

  5. Han J, Yang C, Zhou X, Gui W. Dynamic multi-objective optimization arising in iron precipitation of zinc hydrometallurgy. Hydrometallurgy 2017;173:134–48.

    CAS  Google Scholar 

  6. Han J, Yang C, Zhou X, Gui W. A new multi-threshold image segmentation approach using state transition algorithm. Appl Math Model 2017;44:588–601.

    Google Scholar 

  7. Huang M, Zhou X, Huang T, Yang C, Gui W. 2017. Dynamic optimization based on state transition algorithm for copper removal process. Neural Comput & Applic. https://doi.org/10.1007/s00521-017-3232-0.

    Google Scholar 

  8. Huang Z, Yang C, Zhou X, Gui W. A novel cognitively inspired state transition algorithm for solving the linear bi-level programming problem. Cogn Comput 2018;10(5):816–26.

    Google Scholar 

  9. Huang Z, Yang C, Zhou X, Huang T. 2018. A hybrid feature selection method based on binary state transition algorithm and relieff. IEEE J Biomed Health Inform. https://doi.org/10.1109/JBHI.2018.2872811.

    PubMed  Google Scholar 

  10. Limanond T, Jomnonkwao S, Srikaew A. Projection of future transport energy demand of thailand. Energy Policy 2011;39(5):2754–63.

    Google Scholar 

  11. Liu D, Niu D, Wang H, Fan L. Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm. Renew Energy 2014;62:592–7.

    Google Scholar 

  12. Ma Y, Peng H, Khan T, Cambria E, Hussain A. Sentic lstm: a hybrid network for targeted aspect-based sentiment analysis. Cogn Comput 2018;10(4):639–50.

    Google Scholar 

  13. Meng M, Niu D. Annual electricity consumption analysis and forecasting of China based on few observations methods. Energy Convers Manag 2011;52(2):953–7.

    Google Scholar 

  14. Mohamed AW, Hadi AA, Fattouh AM, Jambi KM. Lshade with semi-parameter adaptation hybrid with cma-es for solving cec 2017 benchmark problems. 2017 IEEE Congress on evolutionary computation (CEC); 2017. p. 145–152.

  15. Mohandes M. Support vector machines for short-term electrical load forecasting. Int J Energy Res 2002;26(4):335–45.

    Google Scholar 

  16. Ruiz L, Rueda R, Cuellar M, Pegalajar M. Energy consumption forecasting based on elman neural networks with evolutive optimization. Expert Systems with Applications 2018;92:380–9.

    Google Scholar 

  17. Schölkopf B, Smola AJ, Williamson RC, Bartlett PL. New support vector algorithms. Neural Comput 2000;12(5):1207–45.

    PubMed  Google Scholar 

  18. Shrivastava NA, Khosravi A, Panigrahi BK. Prediction interval estimation of electricity prices using pso-tuned support vector machines. IEEE Trans Ind Inf 2015;11(2):322–31.

    Google Scholar 

  19. Szoplik J. Forecasting of natural gas consumption with artificial neural networks. Energy 2015;85:208–220.

    Google Scholar 

  20. Tanabe R, Fukunaga AS. Improving the search performance of shade using linear population size reduction. 2014 IEEE Congress on evolutionary computation (CEC); 2014. p. 1658– 65.

  21. Tanveer M. Robust and sparse linear programming twin support vector machines. Cogn Comput 2015;7(1):137–49.

    Google Scholar 

  22. Thissen U, van Brakel R, de Weijer A, Melssen W, Buydens L. Using support vector machines for time series prediction. Chemom Intell Lab Syst 2003;69(1):35–49.

    CAS  Google Scholar 

  23. Vapnik V. The nature of statistical learning theory. New York: Springer; 1995.

    Google Scholar 

  24. Xiao J, Li Y, Xie L, Liu D, Huang J. A hybrid model based on selective ensemble for energy consumption forecasting in china. Energy 2018;159:534–46.

    Google Scholar 

  25. Xu Y, Guo R, Wang L. A twin multi-class classification support vector machine. Cogn Comput 2013;5(4):580–8.

    Google Scholar 

  26. Yuan C, Liu S, Fang Z. Comparison of china’s primary energy consumption forecasting by using arima (the autoregressive integrated moving average) model and gm(1,1) model. Energy 2016;100:384–90.

    Google Scholar 

  27. Zhang F, Yang C, Zhou X, Gui W. Fractional-order pid controller tuning using continuous state transition algorithm. Neural Comput & Applic 2018;29(10):795–804.

    Google Scholar 

  28. Zhou X, Gao DY, Simpson AR. Optimal design of water distribution networks by a discrete state transition algorithm. Eng Optim 2016;48(4):603–28.

    Google Scholar 

  29. Zhou X, Gao DY, Yang C, Gui W. Discrete state transition algorithm for unconstrained integer optimization problems. Neurocomputing 2016;173:864–74.

    Google Scholar 

  30. Zhou X, Shi P, Lim CC, Yang C, Gui W. A dynamic state transition algorithm with application to sensor network localization. Neurocomputing 2018;273:237–50.

    Google Scholar 

  31. Zhou X, Yang C, Gui W. State transition algorithm. Journal of Industrial and Management Optimization 2012;8(4):1039–56.

    Google Scholar 

  32. Zhou X, Yang C, Gui W. Nonlinear system identification and control using state transition algorithm. Appl Math Comput 2014;226:169–79.

    Google Scholar 

  33. Zhou X, Yang C, Gui W. 2018. A statistical study on parameter selection of operators in continuous state transition algorithm. IEEE Transactions on Cybernetics. https://doi.org/10.1109/TCYB.2018.2850350.

    PubMed  Google Scholar 

  34. Zhou X, Zhou J, Yang C, Gui W. Set-point tracking and multi-objective optimization-based PID control for the goethite process. IEEE Access 2018;6:36683–98.

    Google Scholar 

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Funding

This study was funded by the National Natural Science Foundation of China (Grant Nos. 61873285, 61533020, 61751312), the 111 Project (Grant No. B17048), the Innovation-Driven Plan in Central South University (Grant No. 2018CX012), and the Hunan Provincial Natural Science Foundation of China (Grant No. 2018JJ3683).

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Authors and Affiliations

  1. School of Automation, Central South University, Changsha, 410083, China

    Zhaoke Huang, Chunhua Yang & Xiaojun Zhou

  2. School of Computer Science and Informatics, De Montfort University, Leicester, UK

    Shengxiang Yang

Authors
  1. Zhaoke Huang
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  2. Chunhua Yang
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  3. Xiaojun Zhou
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  4. Shengxiang Yang
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Correspondence to Xiaojun Zhou.

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Huang, Z., Yang, C., Zhou, X. et al. Energy Consumption Forecasting for the Nonferrous Metallurgy Industry Using Hybrid Support Vector Regression with an Adaptive State Transition Algorithm. Cogn Comput 12, 357–368 (2020). https://doi.org/10.1007/s12559-019-09644-0

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