Browse > Article
http://dx.doi.org/10.5370/KIEE.2017.66.7.1001

Short-term Electric Load Forecasting in Winter and Summer Seasons using a NARX Neural Network  

Jeong, Hee-Myung (Dept. of Electrical Engineering, Pusan National University)
Park, June Ho (School of Electrical and Computer Engineering, Pusan National University)
Publication Information
The Transactions of The Korean Institute of Electrical Engineers / v.66, no.7, 2017 , pp. 1001-1006 More about this Journal
Abstract
In this study the NARX was proposed as a novel approach to forecast electric load more accurately. The NARX model is a recurrent dynamic network. ISO-NewEngland dataset was employed to evaluate and validate the proposed approach. Obtained results were compared with NAR network and some other popular statistical methods. This study showed that the proposed approach can be applied to forecast electric load and NARX has high potential to be utilized in modeling dynamic systems effectively.
Keywords
NARX neural network; Short-term electric load forecasting; Temperature data;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Chow TWS and Leung CT. "Neural network based shortterm load forecasting using weather compensation", IEEE Trans Power Syst, vol. 11(4), pp. 1736-1742. 1996.   DOI
2 Metaxiotis K, Kagiannas A, Askounis D and Psarras J. "Artificial intelligence in short term electric load forecasting: a state-of-the-art survey for the researcher", Energy Convers Manage, vol. 44(9), pp. 1525-1534. 2003.   DOI
3 Lu CN, Wu HT, and Vemuri S. "Neural network based short term load forecasting", IEEE Trans Power Syst., vol. 8(1), pp. 336-342. 1993.   DOI
4 Hong WC. "Electric load forecasting by support vector model", Appl Math Model, vol. 33(5), pp. 2444-2454. 2009.   DOI
5 Hahn H, Meyer-Nieberg S, and Pickl S. "Electric load forecasting methods: tools for decision making", Eur J Oper Res., vol. 199(3), pp. 902-907. 2009.   DOI
6 Narendra, K.S. and Parthasarathy, K., "Identification and control of dynamical systems using neural networks", IEEE Transactions, Vol. 1 No. 1, pp. 4-27. 1990.
7 Chen, S., Billings, S.A. and Grant, P.M., "Non-linear system identification using neural networks", International Journal of Control, Vol. 51 No. 6, pp. 1191-1214. 1990.   DOI
8 Horne, B.G. and Giles, C.L., "An experimental comparison of recurrent neural networks", Proceedings of the Conference Neural Information Processing Systems 1994, MIT Press, Denver, pp. 697-704. 1995.
9 [Online]. Available http://iso-ne.com/markets/hstdata/znl_info/hourly/index.html
10 Omer F. E. "Forecasting electricity load by a novel recurrent extreme learning machines apprach", Int. J. Elect. Power Energy Syst., vol. 78, pp. 429-435. 2016.   DOI
11 Ekonomou, L., Christodoulou, C. and Mladenov, V., "A Short-Term Load Forecasting Method Using Artificial Neural Networks and Wavelet Analysis", Int. J. Power Syst., vol. 1, pp. 64-68. 2016.
12 Raza, M. and Khosravi, A. "A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings", Renew. Sustain. Energy Rev., vol. 50, pp. 1352-1372. 2015.   DOI
13 Venturini, M. "Simulation of compressor transient behavior through recurrent neural network models". J. Turbomach. Trans. ASME, vol. 128, pp. 444-454. 2006.   DOI
14 Hippert HS, Pedreira CE, and Souza RC. "Neural networks for short-term load forecasting: a review and evaluation". IEEE Trans Power Syst., vol. 16(1), pp. 44-55, 2001.   DOI
15 T. Hong, J. Wilson, and J. Xie, "Long term probabilistic load forecasting and normalization with hourly information", IEEE Trans. Smart Grid, vol. 5, no. 1, pp. 456-462, Jan. 2014.   DOI
16 D. Singhal and K. Swarup, "Electricity price forecasting using artificial neural networks", Int. J. Elect. Power Energy Syst., vol. 33, no. 3, pp. 550-555, Mar. 2011.   DOI
17 A. Karsaz, H. R. Mashhadi, and M. M. Mirsalehi, "Market clearing price and load forecasting using cooperative co-evolutionary approach", Int. J. Elect. Power Energy Syst., vol. 32, no. 5, pp. 408-415, Jun. 2010.   DOI
18 Hernandez, L., Baladron, C., Aguiar, J.M., Calavia, L., Carro, B., Sanchez-Esguevillas, A., Sanjuan, J., Gonzalez, L. and Lloret, J. "Improved short-term load forecasting based on two-stage predictions with artificial neural networks in a microgrid environment". Energies, vol. 6, pp. 4489-4507, 2016.
19 N. Amjady and F. Keynia, "Short-term load forecasting of power systems by combination of wavelet transform and neuro-evolutionary algorithm," Energy, vol. 34, no. 1, pp. 46-57, Jan. 2009.   DOI
20 Kermanshahi B. "Recurrent neural network for forecasting next 10 years loads of nine Japanese utilities". Neurocomputing, vol. 23, pp. 125-133, 1998.   DOI
21 Pai P-F and Hong W-C. "Forecasting regional electricity load based on recurrent support vector machines with genetic algorithms", Electric Power Syst Res., vol. 74, pp. 417-425, 2005.   DOI
22 Pai PF. and Hong WC. "Support vector machines with simulated annealing algorithms in electricity load forecasting", Energy Convers Manage, vol. 46(17), pp. 2669-2688. 2005.   DOI