An Empirical Study of Knowledge Discovery on Daily Electrical Peak Load Using Decision Tree

Abstract:

Load forecasting plays an important role in effective energy management and system reliability. However, difficulty in load forecasting arises due to its nonlinear and irregular variation. This paper presents a case study in which the effect of weather elements on public utility system peak demand is first studied to find the most decisive weather index. The average temperature and previous load demand data are then selected as input variables to predict next-day peak load. The empirical results indicate that the forecasting performance can be improved by applying support vector regression machines.

• Periodical: Advanced Materials Research (Volumes 433-440)
• Pages: 4898-4902
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.433-440.4898
• Online since: January 2012
• Authors: Zhi Yong Li
• Keywords: Association Analysis, Data Mining (DM), Decision Tree, Peak Load Forecasting, Support Vector Machine (SVM)

References

[1] Chen B-J, Chang M-W, Lin C-J. Load Forecasting Using Support Vector Machines: A Study on EUNITE Competition 2001 [J]. IEEE Transactions on Power Systems, 2004 (4): 1821-1830.

DOI: 10.1109/tpwrs.2004.835679

[2] Senjyu T, Mandal P, Uezato K, Funabashi T. Next Day Load Curve Forecasting Using Hybrid Correction Method [J]. IEEE Transactions on Power Systems, 2005 (1): 102-109.

DOI: 10.1109/tpwrs.2004.831256

[3] Saini L M, Soni M K. Artificial Neural Network-Based Peak Load Forecasting Using Conjugate Gradient Methods [J]. IEEE Transactions on Power Systems, 2002 (3): 907-912.

DOI: 10.1109/tpwrs.2002.800992

[4] Chu W-C, Chen Y-P, Xu Z-W, Lee W-J. Multiregion Short-Term Load Forecasting in Consideration of HI and Load/Weather Diversity [J]. IEEE Transactions on Industry Applications, 2011 (1): 232-237.

DOI: 10.1109/tia.2010.2090440

[5] Wang Y, Xia Q, Kang C. Secondary Forecasting Based on Deviation Analysis for Short-Term Load Forecasting [J]. IEEE Transactions on Power Systems, 2011 (2): 500-507.

DOI: 10.1109/tpwrs.2010.2052638

[6] Drezga I, Rahman S. Input Variable Selection for ANN-Based Short-Term Load Forecasting [J]. IEEE Transactions on Power Systems, 1998 (4): 1238-1244.

DOI: 10.1109/59.736244

[7] Haida T, Muto S. Regression Based Peak Load Forecasting Using A Transformation Technique [J]. IEEE Transactions on Power Systems, 1994 (4): 1788-1794.

DOI: 10.1109/59.331433

[8] Senjyu T, Mandal P, Uezato K, Funabashi T. Next Day Load Curve Forecasting Using Recurrent Neural Network Structure [J]. IEE Proceedings on Generation, Transmission and Distribution, 2004 (3): 388-394.

DOI: 10.1049/ip-gtd:20040356

[9] Ling S H, Leung F H F, Lam H K, Tam P K S. Short-Term Electric Load Forecasting Based on a Neural Fuzzy Network [J]. IEEE Transactions on Industrial Electronics, 2003 (6): 1305-1316.

DOI: 10.1109/tie.2003.819572

[10] Fan S, Chen L. Short-Term Load Forecasting Based on an Adaptive Hybrid Method [J]. IEEE Transactions on Power Systems, 2006 (1): 392-401.

DOI: 10.1109/tpwrs.2005.860944

[11] Kim K-H, Youn H-S, Kang Y-C. Short-Term Load Forecasting for Special Days in Anomalous Load Conditions Using Neural Networks and Fuzzy Inference Method [J]. IEEE Transactions on Power Systems, 2000 (2): 559-565.

DOI: 10.1109/59.867141

[12] Chen Y, Luh P B, Guan C, Zhao Y, Michel L D, Coolbeth M A, Friedland P B, Stephen J. Rourke. Short-Term Load Forecasting: Similar Day-Based Wavelet Neural Networks [J]. IEEE Transactions on Power Systems, 2010 (1): 322-330.

DOI: 10.1109/tpwrs.2009.2030426

[13] Maksimovich S M, Shiljkut V M. The Peak Load Forecasting Afterwards Its Intensive Reduction [J]. IEEE Transactions on Power Delivery, 2009 (3): 1552-1559.

DOI: 10.1109/tpwrd.2009.2014267

[14] Zhang M G. Short-Term Load Forecasting Based on Support Vector Machines Regression. Proc. 2005 Int. Conf. Machine Learning and Cybernetics. 2005: 4310-4314.

DOI: 10.1109/icmlc.2005.1527695

[15] Li G, Cheng C T, Lin J Y, Zeng Y. Short-Term Load Forecasting Using Support Vector Machine With SCE-UA Algorithm. Proc. 3rd Int. Conf. Natural Computation (ICNC 2007). 2007: 290-294.

DOI: 10.1109/icnc.2007.660

[16] Breiman L, Friedman J H, Olshen R. Classification and Regression Tree [M]. CRC Press, (1984).

[17] LI Z-Y, WU W-L. Classification of power quality combined disturbances based on phase space reconstruction and support vector machines [J]. Journal of Zhejiang Univ. - Sci. A, 2008 (2): 173-181.

DOI: 10.1631/jzus.a071261

[18] Cristianini N, Shawe-Taylor J. An introduction to support vector machines and other kernel-based learning methods [M]. Cambridge: Cambridge University Press, (2000).

DOI: 10.1017/cbo9780511801389