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Research on a hybrid LSSVM intelligent algorithm in short term load forecasting

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Abstract

For enhancing the prediction accuracy of power load forecasting, a support vector machine (SVM) prediction model based on wavelet transform and the mutant fruit fly parameter optimization intelligent algorithm (WT-MFOA-LSSVM) was presented. The load data were pretreated by wavelet transform, and the load curves were decomposed into different scales, in order to strengthen the regularity and randomness of historical data. Aiming at overcoming the problems of low convergence precision and easily relapsing into local extreme in basic fruit fly optimization algorithm (FOA), judge whether the intelligent algorithm was trapped in local extreme from the fitness variance of the population and the current optimal. Then, it was conducted by optimal individual perturbation and Gauss mutation operation and the mutant fruit flies were second times optimized, which made the accuracy of prediction model be obviously enhanced. The next few days of historical load data of a certain area of Henan Province, China, in 2015 were predicted by using WT-MFOA-LSSVM, and then the prediction results were compared with the results predicted by the SVM model and by the SVM model based on particle swarm optimization model. The results showed that WT-MFOA-LSSVM had high precision in short term load forecasting, and it had a very good practical significance.

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References

  1. Chalal, M.L., Benachir, M. , White, M., et al.: Energy planning and forecasting approaches for supporting physical improvement strategies in the building sector: a review. Renew. Sustain. Energy Rev. (2016). https://doi.org/10.1016/j.rser.2016.06.040

  2. Friedrich, L., Afshari, A.: Short-term forecasting of the Abu Dhabi electricity load using multiple weather variables. Energy Procedia 75, 3014–3026 (2015)

    Article  Google Scholar 

  3. Feng, J.: A new method for ionospheric short-term forecast using similar-day modeling. In: International Symposium on Antennas, Propagation and EM Theory, pp. 472–474. IEEE (2012)

  4. Kayacan, E., Ulutas, B., Kaynak, O.: Grey system theory-based models in time series prediction. Expert Syst. Appl. 37(2), 1784–1789 (2010)

    Article  Google Scholar 

  5. Dooley, Z.A., Turner, A.W.L., Corwall, G.B.: Research on photovoltaic power station short-term power forecast model based on the linear regression algorithm. Renew. Energy Resour. 2(2), 189–203 (2013)

    Google Scholar 

  6. Zeng, J., Qiao, W.: Short-term solar power prediction using a support vector machine. Renew. Energy 52(2), 118–127 (2013)

    Article  Google Scholar 

  7. Yang, X.S., Deb, S., Fong, S.: Accelerated particle swarm optimization and support vector machine for business optimization and applications. Commun. Comput. Inf. Sci. 136(13), 53–66 (2011)

    Google Scholar 

  8. Li, Z.Y., Xu, Z.Y., Ye, H.C., et al.: A reliability forecasting method for distribution systems based on support vector machine with chaotic particle swarm optimization algorithm. In: Power Engineering Conference, pp. 1–5 (2013)

  9. Kang, F., Li, J.S., Li, J.J.: System reliability analysis of slopes using least squares support vector machines with particle swarm optimization. Neurocomputing 209, 46–56 (2016)

    Article  Google Scholar 

  10. Abdoos, A., Hemmati, M., Abdoos, A.A.: Short term load forecasting using a hybrid intelligent method. Knowl. Based Syst. 76, 139–147 (2016)

    Article  Google Scholar 

  11. Coelho, V.N., Coelho, I.M., Coelho, B.N., et al.: A self-adaptive evolutionary fuzzy model for load forecasting problems on smart grid environment. Appl. Energy 169, 567–584 (2016)

    Article  Google Scholar 

  12. Xiao, L., Shao, W., Liang, T., et al.: A combined model based on multiple seasonal patterns and modified firefly algorithm for electrical load forecasting. Appl. Energy 167, 135–153 (2016)

    Article  Google Scholar 

  13. Khan, A.R., Mahmood, A., Safdar, A., et al.: Load forecasting, dynamic pricing and DSM in smart grid: a review. Renew. Sustain. Energy Rev. 54, 1311–1322 (2016)

    Article  Google Scholar 

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Acknowledgements

The authors acknowledge the High Level Talent Research Starting Project of North China University of Water Resources and Electric Power (Grant 4001-40475).

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Authors and Affiliations

  1. School of Electric Power, North China University of Water Resources and Electric Power, Zhengzhou, 450045, China

    Junhua Xiong & Tingling Wang

  2. Research Center, State Grid Xuji Group Corporation, Xuchang, 461001, China

    Junhua Xiong & Ruisheng Li

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  1. Junhua Xiong
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  2. Tingling Wang
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  3. Ruisheng Li
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Correspondence to Junhua Xiong.

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Xiong, J., Wang, T. & Li, R. Research on a hybrid LSSVM intelligent algorithm in short term load forecasting. Cluster Comput 22 (Suppl 4), 8271–8278 (2019). https://doi.org/10.1007/s10586-018-1740-z

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  • DOI: https://doi.org/10.1007/s10586-018-1740-z

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