Application of Online SVR in Very Short-Term Load Forecasting
(*) Corresponding author
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)
Very short-term load forecasting (VSTLF) plays a very important role in an efficient planning and operation of power systems. In order to overcome the disadvantages of inefficiency in online settings existed in support vector machines, online SVR is first introduced into VSTLF and used to construct a 5-minute load forecasting model in this paper. The proposed method can efficiently update the trained forecasting model whenever a sample is added to or removed from the training set without retraining the whole training data from scratch every time. Moreover, it in fact forms a negative feedback with the use of the rolling window technique, which greatly improves the prediction accuracy. The effectiveness of the proposed method is validated by the simulation results on the real-world dataset obtained from NYISO website
Copyright © 2013 Praise Worthy Prize - All rights reserved.
S.H. Henrique, E.P. Charlos, C.S. Reinaldo, Neural Networks for Short-Term Load Forecasting: A Review and Evaluation, IEEE Transactions on Power Systems, vol. 16 n. 1, February 2001, pp. 44 – 55.
Shu Fan and Luonan Chen, Short-term Load Forecasting Based on an Adaptive Hybrid Method, IEEE Transactions on Power Systems, vol. 21 n. 1, February 2006, pp. 392 – 401.
D. Bunn and E. Farmer, Economic and operational context of electric load prediction, In D. Bunn and E. Farmer (Eds.), Comparative models for electrical load forecasting (New York: Wiley, 1985, 3-11).
G.J. Tsekouras, N.D. Hatziargiriou, E.N. Dialynas, An Optimized Adaptive Neural Network for Annual Midterm Energy Forecasting, IEEE Transactions on Power Systems, vol. 21 n. 1, February 2006, pp. 385 – 391.
A.G. Bakirtzis, J.B. Theoharis, S.J. Kiartzis, and K. J. Satsios, Short-term Load Forecasting Using Fuzzy Neural Networks, IEEE Transactions on Power Systems, vol. 10 n. 3, August 1995, pp. 1518 – 1524.
K. Liu, K.S. Subbarayan, R.R. Shoults, M.T. Manry, C. Kwan, F.L. Lewis, and J. Naccarino, Comparison of Very Short-term Load Forecasting Techniques, IEEE Transactions on Power Systems, vol. 11 n. 2, May 1996, pp. 877 – 882.
W. Charytoniuk and M.S. Chen, Very Short Term Load Forecasting Using Artificial Neural Networks, IEEE Transactions on Power Systems, vol. 15 n. l, Feb. 2000, pp. 263 – 268.
A. Motto, F. Galiana, A. Conejo, and J. Arroyo, Network-constrained Multiperiod Auction for a Pool-based Electricity Market, IEEE Transactions on Power Systems, vol. 17 n. 3, August 2002, pp. 646 – 653.
B.J. Chen, M.W. Chang, and C.J. Lin, Load Forecasting Using Support Vector Machines: A Study on EUNITE Competition 2001, IEEE Transactions on Power Systems, vol. 19 n. 4, November 2004, pp. 1821 – 1830.
Stojanović, M.B., Božić, M.M., Stanković, M.M., Stajić, Z.P., Adaptive least squares support vector machines method for short-term load forecasting based on mutual information for inputs selection, (2012) International Review of Electrical Engineering(IREE), 7 (1), pp. 3574-3585.
Türkay, B.E., Demren, D., Electrical load forecasting using support vector machines: A case study, (2011) International Review of Electrical Engineering (IREE), 6 (5), pp. 2411-2418.
Moshari, A., Ebrahimi, A., A comprehensive model for zonal short term load forecasting, (2011) International Review of Electrical Engineering (IREE), 6 (1), pp. 346-354.
Ashour, Z.H., Farahat, M.A., A new artificial neural network approach with selected inputs for short term electric load forecasting, (2008) International Review of Electrical Engineering (IREE), 3 (1), pp. 32-36.
H. Yang, H. Ye, G. Wang, and T. Hu, Fuzzy Neural Very-short-term Load Forecasting Based on Chaotic Dynamics Reconstruction, Chaos Solitons Fractals, vol. 29 n. 2, July 2006, pp. 462 – 469.
S. Kawauchi, H. Sugihara, and H. Sasaki, Development of Very-short Term Load Forecasting Based on Chaos Theory, Electrical Engineering in Japan, vol. 148 n. 2, July 2004, pp. 55 – 63.
C. Guan, P.B. Luh, L.D. Michel, M.A. Coolbeth, and P.B. Friedland, Hybrid Kalman algorithms for very short-term load forecasting and confidence interval estimation, IEEE PES General Meeting~PES 2010~, July 25-29, 2010, Minneapolis, United States.
C. Cortes and V. Vapnik, Support vector networks, Machine Learning, vol. 20 n. 3, September 1995, pp. 273 – 297.
V. N. Vapnik, Statistical Learning Theory (Wiley Interscience, 1998).
K. Seethalekshmi, S.N. Singh, and S.C. Srivastava, A Classification Approach Using Support Vector Machines to Prevent Distance Relay Maloperation Under Power Swing and Voltage Instability, IEEE Transactions on Power Delivery, vol. 27 n. 3, July 2012, pp. 1124 – 1133.
Yongqian Liu, Jie Shi, Yongping Yang, and Wei-Jen Lee, Short-Term Wind-Power Prediction Based on Wavelet Transform–Support Vector Machine and Statistic-Characteristics Analysis, IEEE Transactions on Industry Applications, vol. 48 n. 4, August 2012, pp. 1136 – 1141.
Ma J, Theiler J, Perkins S, Accurate On-line Support Vector Regression, Neural Computing, vol. 15 n. 11, November 2003, pp. 2683 – 2703.
F. Parrella, Online support vector regression, Master dissertation, Department of Information Science, University of Genoa, Liguria, 2007.
Floris Ernst and Achim Schweikard, Forecasting Respiratory Motion with Accurate Online Support Vector Regression (SVRpred), International Journal of Computer Assisted Radiology and Surgery, vol. 4 n. 5, September 2009, pp. 439 – 447.
A.J. Smola, B. Schölkopf, A Tutorial on Support Vector Regression, Statistics and Computing, vol. 14 n. 3, August 2004, pp. 199 – 222.
New York independent system operator (NYISO). [Online]. Available: http://www.nyiso.com..
- There are currently no refbacks.
Please send any question about this web site to email@example.com
Copyright © 2005-2023 Praise Worthy Prize