Abstract
Based on similar day method and SVM, this paper proposes a new method for next day load forecasting. The new method uses the parameters of several similar days, instead of only selecting one similar day as in similar day method. The parameters of selected similar days are used as inputs to SVM for forecasting the loads of 24 points (one hour per point) of the next day. The method behaves the advantages of both similar day method and SVM method, Corresponding software was developed and used to forecast the next day load in a practical power system and the final forecasting error is low.
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Keywords
- Support Vector Machine
- Load Forecast
- Support Vector Machine Method
- Data Mining Approach
- Training Support Vector Machine
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Papalexopoulos, A.D., Hesterberg, T.C.: A Regression-based Approach to Short-Term Load Forecasting. IEEE Trans. Power Syst. 5, 1535–1550 (1990)
Haida, T., Muto, S.: Regression Based Peak Load Forecasting Using a Transformation Technique. IEEE Trans. Power Syst. 9, 1788–1794 (1994)
Rahman, S., Hazim, O.: A Generalized Knowledge-based Short Term Load-forecasting Technique. IEEE Trans. Power Syst. 8, 508–514 (1993)
Wu, H., Lu, C.: A Data Mining Approach for Special Modeling in Small Area Load Forecast. IEEE Trans. Power Syst. 17, 516–521 (2003)
Vapnik, V.N., Golowich, S.E., Smola, A.J.: Support Vector Machine for Function Approximation, Regression Estimation and Signal Procession. Adv. Neural Information Procession Syst. 9, 281–287 (1996)
Zhao, D.F., Wang, M.: A Support Vector Machine Approach for Short Term Load Forecasting. Proceedings of the CSEE 17, 26–30 (2002)
Yang, J.F., Cheng, H.Z.: Application of SVM to Power System Short Term Load Forecasting. Electric Power Automation Equipment 24, 30–32 (2004)
Li, Y.C., Fang, T.J., Zhang, G.X.: Wavelet Support Vector Machine for Short-term Load Forecasting. Journal of University of Science and Technology of China 3, 726–732 (2003)
Cheng, S.: A New Approach to Load Forecasting Based on Similar Day. In: Proceeding of Jiangsu Electrical Engineering Association, vol. 18, pp. 28–32 (1999)
Zhang, H.R., Han, Z.Z.: An Improved Sequential Minimal Optimization Learning Algorithm for Regression Support Vector Machine. Journal of Software 14, 2006–2013 (2003)
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© 2005 Springer-Verlag Berlin Heidelberg
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Li, X., Gong, D., Li, L., Sun, C. (2005). Next Day Load Forecasting Using SVM. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427469_101
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DOI: https://doi.org/10.1007/11427469_101
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25914-5
Online ISBN: 978-3-540-32069-2
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