Evolutionary Techniques Based Combined Artificial Neural Networks for Peak Load Forecasting
This paper presents a new approach using Combined Artificial Neural Network (CANN) module for daily peak load forecasting. Five different computational techniques –Constrained method, Unconstrained method, Evolutionary Programming (EP), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) – have been used to identify the CANN module for peak load forecasting. In this paper, a set of neural networks has been trained with different architecture and training parameters. The networks are trained and tested for the actual load data of Chennai city (India). A set of better trained conventional ANNs are selected to develop a CANN module using different algorithms instead of using one best conventional ANN. Obtained results using CANN module confirm its validity.
[1] S.E. Papadakis,J.B. Theocharis, S.J. Kiartzis and A.G. Bakirtzis, "A
novel approach to short-term load forecasting using fuzzy neural
networks", IEEE Transactions on Power Systems, Vol. 13, No. 2, pp.
480-492, 1998.
[2] Tomonobu Senjyu, Hitoshi Takara, Katsumi Uezato and Toshihisa
Funabashi, "One-Hour-Ahead load forecasting using fuzzy neural
network", IEEE Transactions on Power Systems, Vol. 17, No. 1, pp.
113-118, 2002.
[3] A.A.EI Desouky and M.M. EIKateb, "Hybrid adaptive techniques for
electric-load forecast using ANN and ARIMA", IEE Proceedings,
Gener. Transm. Distrib. Vol. 147, No. 4, pp. 213-217, 2000.
[4] A.G. Bakirtzis, J.B. Theocharis, S.J. Kiartzis and K.J. Satsios, "Short
term load forecasting using fuzzy neural networks", IEEE Transactions
on Power Systems, Vol. 10, No. 3, pp.1518-1524, 1995.
[5] S. Rahman and O. Hazim, "A generalized knowledge-based Short-Term
load forecasting technique", IEEE Transactions on Power Systems, Vol.
8, No. 2, pp.508-514, 1993.
[6] C.N. Lu, H.T. Wu and S. Vemuri, "Neural network based short term
load forecasting", IEEE Transactions on Power Systems, Vol. 8, No.1,
pp. 336-342, 1993.
[7] Alex D. Papalexopoulos, Shangyou Hao and Tie-Mao Peng, "An
Implementation of a neural network based load forecasting model for the
EMS", IEEE Transactions on Power Systems, Vol. 9, No. 4, pp. 1956-
1962, 1994.
[8] K.Y. Lee and J.H. Park, "Short-term load forecasting using an artificial
neural network", IEEE Transactions on Power Systems, Vol. 7, No. 1,
pp. 124-132, 1992.
[9] A.S. AlFuhaid, M.A. El-Sayed and M.S. Mahmoud, "Cascaded Artificial
Neural Networks for Short-term load forecasting", IEEE Transactions
on Power Systems, Vol.12, No. 4, pp. 1524-1529, 1997.
[10] I. Drezga and S. Rahman, "Short-Term load forecasting with local ANN
predictors", IEEE Transactions on Power Systems, Vol. 14, No. 3, pp.
844-858, 1998.
[11] Sherif Hashem and Bruce Schmeiser, "Improving model accuracy using
optimal linear combinations of trained neural networks", IEEE
Transactions on Neural Networks Vol.6,no.3, pp. 792-794,1995.
[12] I. Drezga and S. Rahman, "Input variable selection for ANN based
short-term load forecasting", IEEE Transactions on Power Systems, Vol.
13, No. 4, pp. 1238-1244,1998.
[13] Tetsuro Matsui, Tatsuya Iizaka and Yoshikazu Fukuyama, "Peak load
forecasting using analyzable structured neural network", Proceedings of
IEEE PES 2001, Winter Meeting.
[14] L.L.Lai, "Intelligent System Applications in Power Engineering -
Evolutionary Programming and Neural Networks", John Wiley and
Sons, Inc., New York, 1998.
[15] J. Kennedy, R. Eberhart, "Particle swarm optimization", Proceedings of
IEEE International Conference on Neural Networks (ICNNN-95),
Perth, Australia, Vol. IV, pp. 1942-1948, 1995.
[16] Konstantinos E. Parsopoulos and Michale N. Vrahatis, "Particle Swarm
Optimization method for constrained optimization problems",
Department of Mathematics, University of Patras Artificial Intelligence
Research Center (UPAIRC), Greece.
[17] Lawrence Davis, "Hand book of Genetic Algorithms", International
Thomson Computer Press, London.
[18] David E. Goldberg, "Genetic Algorithms in search, Optimization and
Machine learning", Addison-wesley, 2000.
[19] P.K. Dash &S. Dash and S. Rahman, "A Fuzzy Adaptive Correction
Scheme for Short term load forecasting using fuzzy layered neural
network", ANNPS-93,IEEE Proceedings of the second international
forum on application of neural networks to power systems, pp 432-437,
1993.
[1] S.E. Papadakis,J.B. Theocharis, S.J. Kiartzis and A.G. Bakirtzis, "A
novel approach to short-term load forecasting using fuzzy neural
networks", IEEE Transactions on Power Systems, Vol. 13, No. 2, pp.
480-492, 1998.
[2] Tomonobu Senjyu, Hitoshi Takara, Katsumi Uezato and Toshihisa
Funabashi, "One-Hour-Ahead load forecasting using fuzzy neural
network", IEEE Transactions on Power Systems, Vol. 17, No. 1, pp.
113-118, 2002.
[3] A.A.EI Desouky and M.M. EIKateb, "Hybrid adaptive techniques for
electric-load forecast using ANN and ARIMA", IEE Proceedings,
Gener. Transm. Distrib. Vol. 147, No. 4, pp. 213-217, 2000.
[4] A.G. Bakirtzis, J.B. Theocharis, S.J. Kiartzis and K.J. Satsios, "Short
term load forecasting using fuzzy neural networks", IEEE Transactions
on Power Systems, Vol. 10, No. 3, pp.1518-1524, 1995.
[5] S. Rahman and O. Hazim, "A generalized knowledge-based Short-Term
load forecasting technique", IEEE Transactions on Power Systems, Vol.
8, No. 2, pp.508-514, 1993.
[6] C.N. Lu, H.T. Wu and S. Vemuri, "Neural network based short term
load forecasting", IEEE Transactions on Power Systems, Vol. 8, No.1,
pp. 336-342, 1993.
[7] Alex D. Papalexopoulos, Shangyou Hao and Tie-Mao Peng, "An
Implementation of a neural network based load forecasting model for the
EMS", IEEE Transactions on Power Systems, Vol. 9, No. 4, pp. 1956-
1962, 1994.
[8] K.Y. Lee and J.H. Park, "Short-term load forecasting using an artificial
neural network", IEEE Transactions on Power Systems, Vol. 7, No. 1,
pp. 124-132, 1992.
[9] A.S. AlFuhaid, M.A. El-Sayed and M.S. Mahmoud, "Cascaded Artificial
Neural Networks for Short-term load forecasting", IEEE Transactions
on Power Systems, Vol.12, No. 4, pp. 1524-1529, 1997.
[10] I. Drezga and S. Rahman, "Short-Term load forecasting with local ANN
predictors", IEEE Transactions on Power Systems, Vol. 14, No. 3, pp.
844-858, 1998.
[11] Sherif Hashem and Bruce Schmeiser, "Improving model accuracy using
optimal linear combinations of trained neural networks", IEEE
Transactions on Neural Networks Vol.6,no.3, pp. 792-794,1995.
[12] I. Drezga and S. Rahman, "Input variable selection for ANN based
short-term load forecasting", IEEE Transactions on Power Systems, Vol.
13, No. 4, pp. 1238-1244,1998.
[13] Tetsuro Matsui, Tatsuya Iizaka and Yoshikazu Fukuyama, "Peak load
forecasting using analyzable structured neural network", Proceedings of
IEEE PES 2001, Winter Meeting.
[14] L.L.Lai, "Intelligent System Applications in Power Engineering -
Evolutionary Programming and Neural Networks", John Wiley and
Sons, Inc., New York, 1998.
[15] J. Kennedy, R. Eberhart, "Particle swarm optimization", Proceedings of
IEEE International Conference on Neural Networks (ICNNN-95),
Perth, Australia, Vol. IV, pp. 1942-1948, 1995.
[16] Konstantinos E. Parsopoulos and Michale N. Vrahatis, "Particle Swarm
Optimization method for constrained optimization problems",
Department of Mathematics, University of Patras Artificial Intelligence
Research Center (UPAIRC), Greece.
[17] Lawrence Davis, "Hand book of Genetic Algorithms", International
Thomson Computer Press, London.
[18] David E. Goldberg, "Genetic Algorithms in search, Optimization and
Machine learning", Addison-wesley, 2000.
[19] P.K. Dash &S. Dash and S. Rahman, "A Fuzzy Adaptive Correction
Scheme for Short term load forecasting using fuzzy layered neural
network", ANNPS-93,IEEE Proceedings of the second international
forum on application of neural networks to power systems, pp 432-437,
1993.
@article{"International Journal of Electrical, Electronic and Communication Sciences:52585", author = "P. Subbaraj and V. Rajasekaran", title = "Evolutionary Techniques Based Combined Artificial Neural Networks for Peak Load Forecasting", abstract = "This paper presents a new approach using Combined Artificial Neural Network (CANN) module for daily peak load forecasting. Five different computational techniques –Constrained method, Unconstrained method, Evolutionary Programming (EP), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) – have been used to identify the CANN module for peak load forecasting. In this paper, a set of neural networks has been trained with different architecture and training parameters. The networks are trained and tested for the actual load data of Chennai city (India). A set of better trained conventional ANNs are selected to develop a CANN module using different algorithms instead of using one best conventional ANN. Obtained results using CANN module confirm its validity.
", keywords = "Combined ANN, Evolutionary Programming, Particle
Swarm Optimization, Genetic Algorithm and Peak load forecasting.", volume = "2", number = "9", pages = "1858-7", }
