Weighted Time-Variant Slide Fuzzy Time-Series Models for Short-Term Load Forecasting


Short-term load forecast plays an important role in the day-to-day operation and scheduling of generating units. Season and temperature are the most important factors that affect the load change, but random factors such as big sport events or popular TV shows can change demand consumption in particular hours, which will lead to sudden load changes. A weighted time-variant slide fuzzy time-series model (WTVS) for short-term load forecasting is proposed to improve forecasting accuracy. The WTVS model is divided into three parts, including the data preprocessing, the trend training and the load forecasting. In the data preprocessing phase, the impact of random factors will be weakened by smoothing the historical data. In the trend training and load forecasting phase, the seasonal factor and the weighted historical data are introduced into the Time-variant Slide Fuzzy Time-series Models (TVS) for short-term load forecasting. The WTVS model is tested on the load of the National Electric Power Company in Jordan. Results show that the proposed WTVS model achieves a significant improvement in load forecasting accuracy as compared to TVS models.

Share and Cite:

X. Liu, E. Bai and J. Fang, "Weighted Time-Variant Slide Fuzzy Time-Series Models for Short-Term Load Forecasting," Journal of Intelligent Learning Systems and Applications, Vol. 4 No. 4, 2012, pp. 285-290. doi: 10.4236/jilsa.2012.44030.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] M. T. Hagan and S. M. Behr, “The Time Series Approach to Short Term Load Forecasting,” IEEE Transactions on Power Systems, Vol. 2, No. 3, 1987, pp. 785-791. doi:10.1109/TPWRS.1987.4335210
[2] H. Hahn, S. M. Nieberg and S. Pickl, “Electric Load Forecasting Methods: Tools for Decision Making,” European Journal of Operational Research, Vol. 199, No. 3, 2009, pp. 902-907. doi:10.1016/j.ejor.2009.01.062
[3] J. W. Taylor and R. Buizza, “Neural Networks Load Forecasting with Whether Ensemble Predictions,” IEEE Transactions on Power Systems, Vol. 17, No. 3, 2002, pp. 626-630. doi:10.1109/TPWRS.2002.800906
[4] Y. Chen, P. B. Luh and C. Guan, “Short-term Load Forecasting: Similar Day-Based Wavelet Neural Networks,” IEEE Transactions on Power Systems, Vol. 25, No. 1, 2010, pp. 322-330. doi:10.1109/TPWRS.2009.2030426
[5] A. S. Pandey, D. Singh and S. K. Sinha, “Intelligent Hybrid Wavelet Models for Short-Term Load Forecasting,” IEEE Transactions on Power Systems, Vol. 25, No. 3, 2010, pp. 1266-1273. doi:10.1109/TPWRS.2010.2042471
[6] A. Khotanzad, Z. Enwang, and H. Elragal, “A Neurofuzzy Approach to Short-term Load Forecasting in a Price-sensitive Environment,” IEEE Transactions on Power Systems, Vol. 17, No. 4, 2002, pp. 1273-1282. doi:10.1109/TPWRS.2002.804999
[7] V. H. Hinojosa and A. Hoese, “Short-term Load Forecasting Using Fuzzy Inductive Reasoning and Evolutionary Algorithms,” IEEE Transactions on Power Systems, Vol. 25, No. 1, 2010, pp. 565-574. doi:10.1109/TPWRS.2009.2036821
[8] C. Bo-juen, C. Ming-Wei and L. Chih-jen, “Load Forecasting Using Support Vector Machines: A Study on EUNITE Competition 2001,” IEEE Transactions on Power Systems, Vol. 19, No. 4, 2004, pp. 1821-1830. doi:10.1109/TPWRS.2004.835679
[9] D. Fay and J. V. Ringwood, “On the Influence of Weather Forecast Errors in Short-Term Load Forecasting Models,” IEEE Transactions on Power Systems, Vol. 25, No.3, 2010, pp. 1571-1758. doi:10.1109/TPWRS.2009.2038704
[10] K. B. Song, S. K. Ha and J. W. Park, “Hybrid Load Forecasting Method with Analysis of Temperature Sensitivities,” IEEE Transactions on Power Systems, Vol. 21, No. 2, 2006, pp. 869-876. doi:10.1109/TPWRS.2006.873099
[11] S. Fan and L. U. Chen, “Short-Term Load Forecasting Based on an Adaptive Hybrid Method,” IEEE Transactions on Power Systems, Vol. 21, No. 1, 2006, pp. 392-401. doi:10.1109/TPWRS.2005.860944
[12] R. Mamlook, O. Badran and E. Abclulhadi, “A Fuzzy Inference Model for Short Term Load Forecasting,” Energy Policy, Vol. 37, No. 4, 2009, pp. 1239-1248. doi:10.1016/j.enpol.2008.10.051
[13] X. J. Liu, E. J. Bai and J. Fang, “Time-Variant Slide Fuzzy Time-series Method for Short-Term Load Forecasting,” Proceeding of 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems, Xiamen, 29-31 October 2010, pp. 65-68.
[14] H. T. Liu, N. C. Wei and C. G. Yang, “Improved TimeVariant Fuzzy Time Series Forecast,” Fuzzy Optimization and Decision Making, Vol. 8, No. 1, 2009, pp. 45-65. doi:10.1007/s10700-009-9051-8
[15] C. A. Maia and M. Goncalves, “Application of Switched Adaptive System to Load Forecasting,” Electric Power Systems Research, Vol. 78, No. 4, 2008, pp. 721-727. doi:10.1016/j.epsr.2007.05.014

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.