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Short Term Electricity Load Forecasting with a Nonlinear Autoregressive Neural Network with Exogenous Variables (NarxNet)

  • Ibrahim YaziciEmail author
  • Leyla Temizer
  • Omer Faruk Beyca
Conference paper
Part of the Lecture Notes in Management and Industrial Engineering book series (LNMIE)

Abstract

Electricity load forecasting and planning have vital importance for suppliers as well as other stakeholders in the industry. Forecasting and planning are relevant issues that they provide feedback to each other to increase the efficiency of management. Accurate predictions lead to more efficient planning. Many methods are used for electricity load forecasting depending on characteristics of the system such as stationariness, non-linearity, and heteroscedasticity of data. On the other hand, in electricity load forecasting, forecasting horizons are important issues for modeling time series. In general, forecasting horizons are classified into 3 categories; long-term, mid-term and short-term load forecasting. In this paper, we dealt with short-term electricity load forecasting for Istanbul, Turkey. We utilized one of the efficient nonlinear dynamic system identification tools to make one-step ahead prediction of hourly electricity loads in Istanbul. In the final, the obtained results were discussed.

Keywords

Prediction NarxNet Short-term electricity load forecasting 

References

  1. Cao, X., Dong, S., Wu, Z., Jing, Y. (2015) A data-driven hybrid optimization model for short-term residential load forecasting. In 2015 International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing (CIT/IUCC/DASC/PICOM), pp. 283–287. IEEE.Google Scholar
  2. Chao, G., Jing-chun, Z., Yan-bin, S., & Li-ying, S. (2010, June). Application of dynamic recurrent neural network in power system short-term load forecasting. In 2010 International Conference on Computing, Control and Industrial Engineering (CCIE), Vol. 1, pp. 378–381. IEEE.Google Scholar
  3. Chaturvedi, D. K., Sinha, A. P., & Malik, O. P. (2015). Short-term load forecast using fuzzy logic and wavelet transform integrated generalized neural network. Electrical Power and Energy Systems, 67, 230–237.CrossRefGoogle Scholar
  4. Che, J. X., & Wang, J. Z. (2014). Short-term load forecasting using a kernel based support vector regression combination model. Applied Energy, 132, 602–609.CrossRefGoogle Scholar
  5. Chitsaz, H., Shaker, H., Zareipour, H., Wood, D., & Amjady, N. (2015). Short-term electricity load forecasting of buildings in microgrids. Energy and Buildings, 99, 50–60.CrossRefGoogle Scholar
  6. Dedinec, A., Filiposka, S., Dedinec, A., & Kocarev, L. (2016). Deep belief network based electricity load forecasting: An analysis of Macedonian case. Energy, 115(13), 1688–1700.CrossRefGoogle Scholar
  7. Deihimi, A., & Showkati, H. (2012). Application of echo state networks in short-term electric load forecasting. Energy, 39, 327–340.CrossRefGoogle Scholar
  8. Din, G. M. U., & Marnerides, A. K. (2017, January). Short term power load forecasting using deep neural networks. In 2017 International Conference on Computing, Networking and Communications (ICNC), pp. 594–598. IEEE.Google Scholar
  9. Dudek, G. (2016). Pattern-based local linear regression models for shortterm load forecasting. Electric Power Systems Research, 130, 139–147.CrossRefGoogle Scholar
  10. Ghofrani, M., Ghayekhloo, M., Arabali, M., Ghayekhloo, A. (2015). A hybrid short-term load forecasting with a new input selection framework. Energy, 81, 777–786.CrossRefGoogle Scholar
  11. Kalaitzakis, K., Stavrakakis, G. S., & Anagnostakis, E. M. (2002). Short-term load forecasting based on artificial neural networks parallel implementation. Electric Power Systems Research, 63(3), 185–196.CrossRefGoogle Scholar
  12. Khan, G. M., Zafari, F., & Mahmud, S.A. (2013). Very short term load forecasting using Cartesian genetic programming evolved recurrent neural networks (CGPRNN). In Proceeding of the 12th International Conference on Machine Learning and Applications, pp 152–155.Google Scholar
  13. Kong, W., Zhao, Y. D., Jia, Y., Hill, J. H., Xu, Y., & Zhang, Y. (2017). Short-term residential load forecasting based on LSTM recurrent neural network. IEEE Transactions on Smart Grid, 99.  https://doi.org/10.1109/tsg.2017.2753802.CrossRefGoogle Scholar
  14. Labde, S., Patel, S., (2017). Time series regression model for prediction of closing values of the stock using an adaptive NARX neural network. International Journal of Computer Applications, 158(10), 0975–8887.CrossRefGoogle Scholar
  15. Leontaritis, I. J., & Billings, S. A. (1985). Input-output parametric models for nonlinear systems—Part I: Deterministic nonlinear systems. International Journal of Control, 41(2), 303–328.MathSciNetCrossRefGoogle Scholar
  16. Li, G., Li, B. J., Yu, X. G., & Cheng, C. T. (2015). Echo state network with Bayesian regularization for forecasting short-term power production of small hydropower plants. Energies, 8(10), 12228–12241.CrossRefGoogle Scholar
  17. Marin, F. J., Garcia-Lagos, F., Joya, G., & Sandoval, F. (2002). Global model for short-term load forecasting using artificial neural networks. IEEE Proceedings-Generation, Transmission and Distribution, 149(2), 121–125.CrossRefGoogle Scholar
  18. Marino, D. L., Amarasinghe, K., Manic, M., (2016). Building energy load forecasting using Deep Neural Networks. In IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society, pp. 7046–7051.Google Scholar
  19. Marvuglia, A., & Messineo, A. (2012). Using recurrent artificial neural networks to forecast household electricity consumption. Energy Procedia, 14, 45–55.CrossRefGoogle Scholar
  20. Mehmood, S. T., & El-Hawary, M. (2014, November). Performance evaluation of new and advanced neural networks for short term load forecasting. In Electrical Power and Energy Conference (EPEC), pp. 202–207. IEEE.Google Scholar
  21. Menezes, J. P. M., Barreto, G. A., (2006). On recurrent neural networks for auto-similar traffic prediction: A performance evaluation, VI International Telecommunications Symposium (ITS2006), september 3–6, Fortaleza-CE, BrazilGoogle Scholar
  22. Mocanu, E., Nguyen, P. H., Gibescu, M., & Kling, W. L. (2016). Deep learning for estimating building energy consumption. Sustainable Energy, Grids and Networks, 6, 91–99.CrossRefGoogle Scholar
  23. Nataraja, C., Gorawar, B., & Shilpa, G. N. (2012). Short term load forecasting using time series analysis: A case study for Karnataka, India. International Journal of Engineering Science and Innovation Technology, 1(2), 45–53.Google Scholar
  24. Nie, H., Liu, G., & Liu, X. (2012). Hybrid of ARIMA and SVMs for short-term load forecasting. Energy Procedia, 16, 1455–1460.CrossRefGoogle Scholar
  25. Niu, D. X., Shi, H. F., & Wu, D. D. (2012). Short-term load forecasting using bayesian neural networks learned by hybrid Monte Carlo algorithm. Applied Soft Computing, 12(6), 1822–1827.CrossRefGoogle Scholar
  26. Norgaard, M., Ravn, O., Poulsen, N. K., & Hansen, L. K. (2000). Neural networks for modelling and control of dynamic systems. Berlin: Springer.CrossRefGoogle Scholar
  27. Pang, Q., Min, Z. (2010) Very short-term load forecasting based on neural network and rough set. In 2010 International Conference on Intelligent Computation Technology and Automation (ICICTA), pp. 1132–1135.Google Scholar
  28. Regawad, A. P., & Soanawane, V. L. (2009). Artificial neural network based short term load forecasting. In International Conference on Power Systems, pp. 1–7.Google Scholar
  29. Ryu, S., Noh, J., & Kim, H. (2016). Deep neural network based demand side short term load forecasting. Energies, 10(1), 3.CrossRefGoogle Scholar
  30. Sharif, S. S., & Taylor, J. H. (2000). Real-time load forecasting by Artificial neural networks. In IEEE Power Engineering Society Summer Meeting (Vol. 1, pp. 496–501).Google Scholar
  31. Showkati, H., Hejazi, A. H., & Elyasi, S. (2010, July). Short term load forecasting using echo state networks. In The 2010 International Joint Conference on Neural Networks (IJCNN), pp. 1–5. IEEE.Google Scholar
  32. Shrivastava, A., & Bhandakkar, A. (2013). Short-term load forecasting using artificial neural network techniques. Journal of Engineering Research Application, 3(5), 1524–1527.Google Scholar
  33. Siddarameshwara, N., Yelamali, A., & Byahatti, K. (2010, October). Electricity short term load forecasting using elman recurrent neural network. In 2010 International Conference on Advances in Recent Technologies in Communication and Computing (ARTCom), pp. 351–354. IEEE.Google Scholar
  34. Siegelmann, H. T., Horne, B. G., & Giles, C. L. (1997). Computational capabilities of recurrent NARX neural networks. IEEE Transactions On Systems, Man, and Cybernetics, B, 27(2), 208–215.CrossRefGoogle Scholar
  35. Singh, D., & Singh, S. P. (2001). A self-selecting neural network for short-term load forecasting. Electric Power Components and Systems, 29(2), 117–130.MathSciNetCrossRefGoogle Scholar
  36. Song, Q., Zhao, X., & Feng, Z. (2011). Hourly electric load forecasting algorithm based on echo state neural network (pp. 3893–3897).Google Scholar
  37. Vermaak, J., & Botha, E. C. (1998). Recurrent neural networks for short-term load forecasting. IEEE Transactions on Power Systems, 13, 126–132.CrossRefGoogle Scholar
  38. Yang, H. T., Huang, C. M., & Huang, C. L. (1996). Identification of armax model for short term load forecasting: an evolutionary programming approach. IEEE Transactions on Power Systems, 11, 403–408.CrossRefGoogle Scholar
  39. Yoo, H., & Pimmel, R. L. (1999). Short term load forecasting using a selfsupervised adaptive neural network. IEEE Transactions on Power System, 14(2).CrossRefGoogle Scholar
  40. Zhang, B., Wu, J. L., & Chang, P. C. (2017). A multiple time series-based recurrent neural network for short-term load forecasting. Soft Computing, 1–14.Google Scholar
  41. Zheng, J., Xu, C., Zhang, Z., & Li, X. (2017a). Electric load forecasting in smart grids using long-short-term-memory based recurrent neural network. In Proceedings of IEEE 51st Annual Conference on Information Sciences and System, Baltimore, MD, USA, March, 2017.Google Scholar
  42. Zheng, J., Xu, C., Zhang, Z., & Li, X. (2017b, March). Electric load forecasting in smart grids using long-short-term-memory based recurrent neural network. In 2017 51st Annual Conference on Information Sciences and Systems (CISS), pp. 1–6. IEEE.Google Scholar
  43. Zhang, R., Dong, Z. Y., Xu, Y., Meng, K., Wong, K. P., (2013). Short-term load forecasting of Australian National Electricity Market by an ensemble model of extreme learning machine. IET Generation, Transmission & Distribution, 7, 391–397.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ibrahim Yazici
    • 1
    Email author
  • Leyla Temizer
    • 2
  • Omer Faruk Beyca
    • 1
  1. 1.Industrial Engineering Department, Faculty of ManagementIstanbul Technical UniversityIstanbulTurkey
  2. 2.Industrial Engineering Department, Engineering FacultyIstanbul UniversityIstanbulTurkey

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