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A Regressive Convolution Neural Network and Support Vector Regression Model for Electricity Consumption Forecasting

  • Youshan ZhangEmail author
  • Qi Li
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 70)

Abstract

Electricity consumption forecasting has important implications for the mineral companies on guiding quarterly work, normal power system operation, and the management. However, electricity consumption prediction for the mineral company is difficult since electricity consumption can be affected by various factors. The problem is non-trivial due to three major challenges for traditional methods: insufficient training data, high computational cost and low prediction accuracy. To tackle these challenges, we firstly propose a Regressive Convolution Neural Network (RCNN) model, but RCNN still suffers from high computation overhead. Then we utilize RCNN to extract features from data and Regressive Support Vector Machine (SVR) trained with features to predict the electricity consumption. The experimental results show that RCNN-SVR model achieves higher accuracy than using the traditional RCNN or SVM alone. The MSE, MAPE, and CV-RMSE of RCNN-SVR model are 0.8564, 1.975, and 0.0687% respectively, which illustrates the low predicting error rate of the proposed model.

Keywords

Electricity consumption forecasting Regression convolution neural network Support vector machine 

References

  1. 1.
    Kavousi-Fard, A., Samet, H., Marzbani, F.: A new hybrid modified firefly algorithm and support vector regression model for accurate short term load forecasting. Expert Syst. Appl. 41(13), 6047–6056 (2014)CrossRefGoogle Scholar
  2. 2.
    Ding, S., Hipel, K.W., Dang, Y.: Forecasting China’s electricity consumption using a new grey prediction model. Energy 149, 314–328 (2018)CrossRefGoogle Scholar
  3. 3.
    Kaytez, F., Taplamacioglu, M.C., Cam, E., Hardalac, F.: Forecasting electricity consumption: a comparison of regression analysis, neural networks and least squares support vector machines. Int. J. Electr. Power Energy Syst. 67, 431–438 (2015)CrossRefGoogle Scholar
  4. 4.
    Zhang, Y., Guo, L., Li, Q., Li, J.: Electricity consumption forecasting method based on MPSO-BP neural network model. In: Proceedings of the 2016 4th International Conference on Electrical & Electronics Engineering and Computer Science (ICEEECS 2016), vol. 50, pp. 674–678 (2016)Google Scholar
  5. 5.
    Akay, D., Atak, M.: Grey prediction with rolling mechanism for electricity demand forecasting of Turkey. Energy 32(9), 1670–1675 (2007)CrossRefGoogle Scholar
  6. 6.
    Bianco, V., Manca, O., Nardini, S.: Electricity consumption forecasting in Italy using linear regression models. Energy 34(9), 1413–1421 (2009)CrossRefGoogle Scholar
  7. 7.
    Abdel-Aal, R.E., Al-Garni, A.Z.: Forecasting monthly electric energy consumption in Eastern Saudi Arabia using univariate time-series analysis. Energy 22(11), 1059–1069 (1997)CrossRefGoogle Scholar
  8. 8.
    Ekonomou, L.: Greek long-term energy consumption prediction using artificial neural networks. Energy 35(2), 512–517 (2010)CrossRefGoogle Scholar
  9. 9.
    Wang, S., Yu, L., Tang, L., Wang, S.: A novel seasonal decomposition based least squares support vector regression ensemble learning approach for hydropower consumption forecasting in China. Energy 36(11), 6542–6554 (2011)CrossRefGoogle Scholar
  10. 10.
    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 100, 384–390 (2016)CrossRefGoogle Scholar
  11. 11.
    Soubdhan, T., Ndong, J., Ould-Baba, H., Do, M.-T.: A robust forecasting framework based on the Kalman filtering approach with a twofold parameter tuning procedure: application to solar and photovoltaic prediction. Solar Energy 131, 246–259 (2016)CrossRefGoogle Scholar
  12. 12.
    Al-Hamadi, H.M., Soliman, S.A.: Short-term electric load forecasting based on Kalman filtering algorithm with moving window weather and load model. Electr. Power Syst. Res. 68(1), 47–59 (2004)CrossRefGoogle Scholar
  13. 13.
    Hu, Y.-C.: Electricity consumption prediction using a neural-network-based grey forecasting approach. J. Oper. Res. Soc. 68(10), 1259–1264 (2017)CrossRefGoogle Scholar
  14. 14.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  15. 15.
    Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)Google Scholar
  16. 16.
    Kalchbrenner, N., Grefenstette, E., Blunsom, P.: A convolutional neural network for modelling sentences. arXiv:1404.2188 (2014)
  17. 17.
    Kuo, P.-H., Huang, C.-J.: A high precision artificial neural networks model for short-term energy load forecasting. Energies 11(1), 213 (2018)CrossRefGoogle Scholar
  18. 18.
    Smola, A.J., Schölkopf, B.: A tutorial on support vector regression. Stat. Comput. 14(3), 199–222 (2004)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Basak, D., Pal, S., Patranabis, D.C.: Support vector regression. Neural Inf. Process.-Lett. Rev. 11(10), 203–224 (2007)Google Scholar
  20. 20.
    Tang, Y.: Deep learning using linear support vector machines. arXiv:1306.0239 (2013)

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Computer Science and EngineeringLehigh UniversityBethlehemUSA
  2. 2.Department of AutomationBOHAI UniversityJinzhouChina

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