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http://dx.doi.org/10.7232/IEIF.2011.24.4.351

Electricity Demand Forecasting based on Support Vector Regression  

Lee, Hyoung-Ro (Dept. of Industrial Engineering, Ajou University)
Shin, Hyun-Jung (Dept. of Industrial Engineering, Ajou University)
Publication Information
IE interfaces / v.24, no.4, 2011 , pp. 351-361 More about this Journal
Abstract
Forecasting of electricity demand have difficulty in adapting to abrupt weather changes along with a radical shift in major regional and global climates. This has lead to increasing attention to research on the immediate and accurate forecasting model. Technically, this implies that a model requires only a few input variables all of which are easily obtainable, and its predictive performance is comparable with other competing models. To meet the ends, this paper presents an energy demand forecasting model that uses the variable selection or extraction methods of data mining to select only relevant input variables, and employs support vector regression method for accurate prediction. Also, it proposes a novel performance measure for time-series prediction, shift index, followed by description on preprocessing procedure. A comparative evaluation of the proposed method with other representative data mining models such as an auto-regression model, an artificial neural network model, an ordinary support vector regression model was carried out for obtaining the forecast of monthly electricity demand from 2000 to 2008 based on data provided by Korea Energy Economics Institute. Among the models tested, the proposed method was shown promising results than others.
Keywords
artificial neural network; energy; electricity demand forecasting; feature extraction; variable selection; support vector regression;
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Times Cited By KSCI : 6  (Citation Analysis)
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1 Park, C. K. (2006), Estimating Software Development Cost USING Support Vector Regression, The Korean Operations Research and Management Science Society, 23(1), 75-91.
2 Park, K., Hou, T., and Shin, H. (2011), Oil Price Forecasting Based on Machine Learning Techniques, Journal of the Korean Institute of Industrial Engineers, 37(1), 64-73.   DOI
3 Romera, E. G., Moran, M. A. J., and Fernandez, D. C. (2008), Monthly electric energy demand forecasting with neural networks and Fourier series, Energy Conversion and Management, 49(11), 3135-3142.   DOI   ScienceOn
4 Shin, J. H. and Hong, Y. C. (2006), GMDH Algorithm with Data Weighting Performance and Its Application to Power Demand Forecasting, Journal of Control Automation and Systems Engineering, 12(7).
5 Sholkopf, B. and Smola, A. (2002), Learning with Kernels, MIT Press, Cambridge MA.
6 Srinivasan, D. (2008), Energy Demand Prediction using GMDH networks, Nuerocomputing, 72(1-3), 625-629.   DOI   ScienceOn
7 Thissen, U., van Brakel, R., de Weijer A. P., Melssen, W. J., and Buydens, L. M. C. (2003), Using Support Vector Machines for time series prediction, Chemometrics and Intelligent Laboratory Systems, 69(1-2), 35-49.   DOI
8 Wenwu, H., Zhizhong, W., and Hui, J. (2008), Model Optimizing and Feature Selection for Support Vector Regression in time series forecasting, Neurocomputing, 72(1-3), 600-611.   DOI   ScienceOn
9 Wi, Y. M., Moon, G. H., Lee, J. H., Joo, S. K., and Song, K. B. (2007), Load Forecasting for the Holidays using a Polynomial Regression Incorporating Temperature Effect, The Korean Institute of Electrical Engineers, 29-30.
10 Yang, J., Rivard, H., and Zmeureanu, R. (2005), On-line building energy prediction using adaptive artificial neural networks, Energy and Buildings, 37(12), 1250-1259.   DOI   ScienceOn
11 Hong, W. C. (2009), Electric Load Forecasting by Support Vector Model, Applied Mathematical Modelling, 33(5), 2444-2454.   DOI   ScienceOn
12 Khotanzad, A., Davis, M. H., Abaye, A., and Maratukulam, D. J. (1996), An Artificial Neural Network Hourly Temperature Forecaster with Applications in Load Forecasting, Journal of Power Systems, 11(2), 870-876.   DOI   ScienceOn
13 Kim, J. H., Lee, J. G., and Cha, J. M. (2009), Load forecasting and demand management considering with renewable energy, The Korean Institute of Electrical Engineers, 2260-2261.
14 Kitagawa, G. and Gersch, W. (1996), Smoothness Priors Analysis o] Time Series, Lecture Notes in Statistics, Springer-Verlag.
15 Kong, D. S., Kwak, Y. H., and Huh, J. H. (2010), Artificial Neural Network Based Energy Demand Prediction for the Urban District Energy Planning, Journal of the Architectural Institute of Korea, 26(2), 221-230.
16 Kwon, S. K. (2004), The Present Situation of District Heating in Korea and Foreign Countries, The Society of Air-conditioning and Refrigerating Engineers of Korea, 1217-1222.
17 Mirasgedis, S., Sarafidis, Y., Georgo Poulou, E., Lalas, D. P., Moschovits, M. Karagiannis, F., and Papakonstantinou, D. (2006), Models for mid-term electricity demand forecasting incorporating weather influences, Energy, 31(2-3), 208-227.   DOI   ScienceOn
18 Nam, B. W., Song, K. B., Kim, K. H., and Cha, J. M. (2008), The Spatial Electric Load Forecasting Algorithm using the Multiple Regression Analysis Method, Journal of the Korean Institute of Illuminating and Electrical Installation Engineers, 22(2), 63-70.   DOI
19 Pai, P. F. and Hong, W. C. (2005), Support vector machines with simulated annealing algorithms in electricity load forecasting, Energy Conversion and Management, 46(17), 2669-2688.   DOI   ScienceOn
20 Nrgaard, M. and Norgaard, P. M. (2006), Neural Networks for Modelling and Control of Dynamic Systems : A Practitioner's Handbook(Advanced Textbooks in Control and Signal Processing), Springer.
21 Pappas, S. Sp., Ekonomou, L., Karampelas, P., Karamousantas, D. C., Katsikas, S. K., Chatzarakis, G. E., and Skafidas, P. D. (2010), Electricity demand load forecasting of the Hellenic power system using an ARMA model, Electric Power Systems Research, 80(3), 256-264.   DOI   ScienceOn
22 Bishop, C. M. (2006), Pattern recognition and machine learning, Springer.
23 Choi, N. H., Son, K. M., and Lee, T. G. (2001), Daily peak load forecasting considering the load trend and temperature, Journal of the Korean Institute of Illuminating and Electrical Installation Engineers, 6(6), 35-42.
24 Cristianini, N. and Taylor, J. S. (2000), An Introduction to support vector machines and other kernel based learning methods, Cambridge University Press.
25 Harvey, A. C. and Koopmand, S. J. (1993), Forecasting Hourly Electricity Demand using Time-varying splines, Journal of the American Statistical Association, 88(424), 1228-1236.   DOI   ScienceOn
26 Hall, M. A. (1999), Correlation-based Feature Selection for Machine Learning, The University of Waikato Press.
27 Han, C. H., Lee, J. W., and Lee, K. K. (2009), Analyzing Information Value of Temperature Forecast for the Electricity Demand Forecasts, The Korean Operations Research and Management Science Society, 26(1), 79-91.
28 Han, S. M. (2009), A study on the development of the generation expansion planning system using multi-criteria decision making rule, Ph.D diss, Hongik University.
29 Haykin, S. (1999), Neural networks: a comprehensive foundation, Prentice-Hall, Inc.