Short-Term Load Forecasting (STLF) plays an important role for the economic and secure operation of power systems. In this paper, Continuous Genetic Algorithm (CGA) is employed to evolve the optimum large neural networks structure and connecting weights for one-day ahead electric load forecasting problem. This study describes the process of developing three layer feed-forward large neural networks for load forecasting and then presents a heuristic search algorithm for performing an important task of this process, i.e. optimal networks structure design. The proposed method is applied to STLF of the local utility. Data are clustered due to the differences in their characteristics. Special days are extracted from the normal training sets and handled separately. In this way, a solution is provided for all load types, including working days and weekends and special days. We find good performance for the large neural networks. The proposed methodology gives lower percent errors all the time. Thus, it can be applied to automatically design an optimal load forecaster based on historical data.
[1] A. K. Topalli, I. Erkmen, I. Topalli, Intelligent short-term load
forecasting in Turkey, Electrical Power and Energy Systems, Vol. 28,
2006, pp. 437-447.
[2] K.-H. Kim, H.-S. Youn and Y.-C. Kang, Short-term load forecasting for
special days in Anomalous load conditions using neural networks and
fuzzy inference method, IEEE Trans. on Power Systems, Vol. 15, No. 2,
2000, pp. 559-565.
[3] H. shayeghi, H. A. Shayanfar, A. Jalili, M. Porabasi, A Neural network
based short-term load forecasting, Proc. of the 2007 Internatinal Conf.
on Artificial Intelligence (ICAI 07), Las Vegas, U.S.A., June 2007.
[4] G. Box, G.M. Jenkins, Time series analysis, forecasting and control,
San Francisco: Holden-Day; 1970.
[5] J.H. Park, Y.M. Park and K.Y. Lee, Composite modeling for adaptive
short-term load forecasting, IEEE Trans. on Power Systems, Vol. 6,
1991, pp. 450-457.
[6] A. D. Papalexopoulos, T. C. Hesterberg, A regression-based approach
to short-term system load forecasting, IEEE Trans on Power Systems,
Vol. 4, No. 4, 1990, pp. 1535-1547.
[7] J.W. Taylor, R. Buizza, Using weather ensemble predictions in
electricity demand forecasting, International Journal of Forecastingt,
Vol. 19, 2003., pp. 57-70.
[8] A. D. Trudnowski et al., Real-Time very short-term load prediction for
power system automatic control, IEEE Trans. Control Systems
Technology, Vol. 9, No. 2, 2001, pp. 254-260.
[9] M. S. S. Rao, S. A. Soman, B. L. Menezes, P. Chawande, P. Dipti, T.
Ghanshyam, An expert system approach to short-term load forecasting
for reliance energy limited, IEEE PES Meeting, Mumbai, 2006.
[10] S. Rahman and R. Bhatnagar, An expert system based algorithm for
short-term load forecasting, IEEE Trans. on Power Systems, Vol. 3, No.
2, pp. 392-399. 1988.
[11] T. P. Tsao, G. C. L. Iao, S. H. Chen, Short-term load forecasting using
neural networks and evolutionary programming, Proc. of Fifth
International Power Engineering Conference, Singapore, 2001, pp.
743-748.
[12] S. H. Ling, F. H. F. Leung, H. K. Lam, Y.-S. Lee, P. K. S. Tam, "A
novel genetic-algorithm-based neural network for short-term load
forecasting, IEEE Trans. on Industrial Electronics, Vol. 50, No. 4,
2003, pp. 793-799.
[13] S. Sheng, C. Wang, Integrating radial basis function neural network
with fuzzy control for load forecasting in power system, IEEE/PES
Transmission and Distribution Conference & Exhibition: Asia and
Pacific, Dalian, China, 2005, pp. 1-5.
[14] D. Srinivasan, Evolving artificial neural networks for short term load
forecasting, Neurocomputing, Vol. 23, 1998, pp. 265-276.
[15] J. A. Momoh Y. Wang, M. Elfayoumy, Artificial neural network based
load forecasting, IEEE International Conference on Systems, Man and
Cybernetics, Computational Cybernetics and Simulation, 1997, pp.
3443-3451.
[16] W. Charytoniuk, M.S. Chen, Neural network design short-term load
forecasting, Proc. of IEEE International Conference on Electric Utility
Deregulation and Restructuring and Power Technologies, City
Univercity, London, 2000, pp. 554-561.
[17] C. Sun, D. Gong, Support vector machines with PSO algorithm for
short-term load forecasting, Proc. of IEEE International Conference on
Networking, Sensing and Control, 2006, pp. 676-680.
[18] N. O. Attoh-Okine, Application of genetic-based neural network to
lateritic soil strength modeling, Construction and Building Materials,
2004, Vol. 18, pp. 619- 623.
[19] H. Shayeghi, A. Jalili and H. A. Shayanfar, Robust modified GA based
multi-stage fuzzy LFC, Energy Conversion and Management, Vol. 48,
pp. 1656-1670. 2007.
[20] M. Shahidehpour, H. Yamin, Z. Li, Market operations in electric power
systems: forecasting, scheduling, and risk management, Wiley-
Interscience Publication, 2002.
[21] H. S. Hippert, C. E. Pedreira, R. C. Souza, Neural networks for shortterm
load forecasting: A review and evaluation, IEEE Trans. on Power
Systems, Vol. 16, No. 1, 2001, pp. 44-55.
[22] H.S. Hippert, D.W. Bunn, R.C. Souza, Large neural networks for
electricity load forecasting: are they overfitted, International Journal of
Forecasting, 2005, Vol. 21, pp. 425-434.
[23] J.-R. Zhang, J. Zhang, T.-M. Lok, M. R. Lyu, A hybrid particle swarm
optimization-back propagation algorithm for feed-forward neural
network training, Applied Mathematics and Computation, Vol. 185,
2007, pp. 1026-1037,.
[24] A. Konar, Artificial intelligence and soft computing: behavioral and
cognitive modeling of the human brain, CRC Press, 1999.
[25] E. D. Goldberg, Genetic algorithms in search and machine learning,
reading, MA: Addison-Wesley; 1989.
[26] D. T. Pham and D. Karaboga, Intelligent optimization techniques,
genetic algorithms, Tabu search, simulated annealing and neural
networks, Berlin, Germany: Springer, 2000.
[27] S. Amin, J. L. Fernandez-Villacanas, Dynamic local search, Proc. of the
2nd International Conference on Genetic Algorithms in Engineering
Systems: Innovations and Applications, 1997, pp.129-132.
[28] R. L. Haupt, S. E. Haupt, Practical genetic algorithms, Wiley-
Interscience publication, 2004.
[1] A. K. Topalli, I. Erkmen, I. Topalli, Intelligent short-term load
forecasting in Turkey, Electrical Power and Energy Systems, Vol. 28,
2006, pp. 437-447.
[2] K.-H. Kim, H.-S. Youn and Y.-C. Kang, Short-term load forecasting for
special days in Anomalous load conditions using neural networks and
fuzzy inference method, IEEE Trans. on Power Systems, Vol. 15, No. 2,
2000, pp. 559-565.
[3] H. shayeghi, H. A. Shayanfar, A. Jalili, M. Porabasi, A Neural network
based short-term load forecasting, Proc. of the 2007 Internatinal Conf.
on Artificial Intelligence (ICAI 07), Las Vegas, U.S.A., June 2007.
[4] G. Box, G.M. Jenkins, Time series analysis, forecasting and control,
San Francisco: Holden-Day; 1970.
[5] J.H. Park, Y.M. Park and K.Y. Lee, Composite modeling for adaptive
short-term load forecasting, IEEE Trans. on Power Systems, Vol. 6,
1991, pp. 450-457.
[6] A. D. Papalexopoulos, T. C. Hesterberg, A regression-based approach
to short-term system load forecasting, IEEE Trans on Power Systems,
Vol. 4, No. 4, 1990, pp. 1535-1547.
[7] J.W. Taylor, R. Buizza, Using weather ensemble predictions in
electricity demand forecasting, International Journal of Forecastingt,
Vol. 19, 2003., pp. 57-70.
[8] A. D. Trudnowski et al., Real-Time very short-term load prediction for
power system automatic control, IEEE Trans. Control Systems
Technology, Vol. 9, No. 2, 2001, pp. 254-260.
[9] M. S. S. Rao, S. A. Soman, B. L. Menezes, P. Chawande, P. Dipti, T.
Ghanshyam, An expert system approach to short-term load forecasting
for reliance energy limited, IEEE PES Meeting, Mumbai, 2006.
[10] S. Rahman and R. Bhatnagar, An expert system based algorithm for
short-term load forecasting, IEEE Trans. on Power Systems, Vol. 3, No.
2, pp. 392-399. 1988.
[11] T. P. Tsao, G. C. L. Iao, S. H. Chen, Short-term load forecasting using
neural networks and evolutionary programming, Proc. of Fifth
International Power Engineering Conference, Singapore, 2001, pp.
743-748.
[12] S. H. Ling, F. H. F. Leung, H. K. Lam, Y.-S. Lee, P. K. S. Tam, "A
novel genetic-algorithm-based neural network for short-term load
forecasting, IEEE Trans. on Industrial Electronics, Vol. 50, No. 4,
2003, pp. 793-799.
[13] S. Sheng, C. Wang, Integrating radial basis function neural network
with fuzzy control for load forecasting in power system, IEEE/PES
Transmission and Distribution Conference & Exhibition: Asia and
Pacific, Dalian, China, 2005, pp. 1-5.
[14] D. Srinivasan, Evolving artificial neural networks for short term load
forecasting, Neurocomputing, Vol. 23, 1998, pp. 265-276.
[15] J. A. Momoh Y. Wang, M. Elfayoumy, Artificial neural network based
load forecasting, IEEE International Conference on Systems, Man and
Cybernetics, Computational Cybernetics and Simulation, 1997, pp.
3443-3451.
[16] W. Charytoniuk, M.S. Chen, Neural network design short-term load
forecasting, Proc. of IEEE International Conference on Electric Utility
Deregulation and Restructuring and Power Technologies, City
Univercity, London, 2000, pp. 554-561.
[17] C. Sun, D. Gong, Support vector machines with PSO algorithm for
short-term load forecasting, Proc. of IEEE International Conference on
Networking, Sensing and Control, 2006, pp. 676-680.
[18] N. O. Attoh-Okine, Application of genetic-based neural network to
lateritic soil strength modeling, Construction and Building Materials,
2004, Vol. 18, pp. 619- 623.
[19] H. Shayeghi, A. Jalili and H. A. Shayanfar, Robust modified GA based
multi-stage fuzzy LFC, Energy Conversion and Management, Vol. 48,
pp. 1656-1670. 2007.
[20] M. Shahidehpour, H. Yamin, Z. Li, Market operations in electric power
systems: forecasting, scheduling, and risk management, Wiley-
Interscience Publication, 2002.
[21] H. S. Hippert, C. E. Pedreira, R. C. Souza, Neural networks for shortterm
load forecasting: A review and evaluation, IEEE Trans. on Power
Systems, Vol. 16, No. 1, 2001, pp. 44-55.
[22] H.S. Hippert, D.W. Bunn, R.C. Souza, Large neural networks for
electricity load forecasting: are they overfitted, International Journal of
Forecasting, 2005, Vol. 21, pp. 425-434.
[23] J.-R. Zhang, J. Zhang, T.-M. Lok, M. R. Lyu, A hybrid particle swarm
optimization-back propagation algorithm for feed-forward neural
network training, Applied Mathematics and Computation, Vol. 185,
2007, pp. 1026-1037,.
[24] A. Konar, Artificial intelligence and soft computing: behavioral and
cognitive modeling of the human brain, CRC Press, 1999.
[25] E. D. Goldberg, Genetic algorithms in search and machine learning,
reading, MA: Addison-Wesley; 1989.
[26] D. T. Pham and D. Karaboga, Intelligent optimization techniques,
genetic algorithms, Tabu search, simulated annealing and neural
networks, Berlin, Germany: Springer, 2000.
[27] S. Amin, J. L. Fernandez-Villacanas, Dynamic local search, Proc. of the
2nd International Conference on Genetic Algorithms in Engineering
Systems: Innovations and Applications, 1997, pp.129-132.
[28] R. L. Haupt, S. E. Haupt, Practical genetic algorithms, Wiley-
Interscience publication, 2004.
@article{"International Journal of Information, Control and Computer Sciences:64947", author = "H. Shayeghi and H. A. Shayanfar and G. Azimi", title = "Intelligent Neural Network Based STLF", abstract = "Short-Term Load Forecasting (STLF) plays an important role for the economic and secure operation of power systems. In this paper, Continuous Genetic Algorithm (CGA) is employed to evolve the optimum large neural networks structure and connecting weights for one-day ahead electric load forecasting problem. This study describes the process of developing three layer feed-forward large neural networks for load forecasting and then presents a heuristic search algorithm for performing an important task of this process, i.e. optimal networks structure design. The proposed method is applied to STLF of the local utility. Data are clustered due to the differences in their characteristics. Special days are extracted from the normal training sets and handled separately. In this way, a solution is provided for all load types, including working days and weekends and special days. We find good performance for the large neural networks. The proposed methodology gives lower percent errors all the time. Thus, it can be applied to automatically design an optimal load forecaster based on historical data.
", keywords = "Feed-forward Large Neural Network, Short-TermLoad Forecasting, Continuous Genetic Algorithm.", volume = "3", number = "4", pages = "1237-11", }
