Short Term Load Forecasting Using Curve Fitting Prediction Optimized By Bacterial Foraging Optimization
(*) 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)
Short term load forecast plays an important role in electric power system planning, design and operation. Curve fitting prediction technique and time series models are used for hourly loads forecasting of the week days. In this paper a new approach for short-term load forecasting is introduced. This approach is curve fitting prediction technique combined with Bacterial Foraging optimization. BFO is used to get optimum parameters of the model describing the system with minimum error between actual and forecasted load. The suitability of the proposed approach is illustrated through an application to the actual load data of New England control area.
Copyright © Praise Worthy Prize - All rights reserved.
Farahat, M.A., Talaat, M., The using of curve fitting prediction optimized by genetic algorithms for short-term load forecasting, (2012) International Review of Electrical Engineering (IREE), 7 (6), pp. 6209-6215.
Khotanzad, E. Zhou, and H. Elragal, ,A Neuro-Fuzzy Approach to Short-Term Load Forecasting in a Price-Sensitive Environment, IEEE Transactions on Power Systems, Vol. 17, No. 4, (2002), Page(s). 1273-1282.
K. Methaprayoon, W. Lee, S. Rasmiddattaa, J. Lia and R. Ross, ,Multistage Artificial Neural Network Short-Term Load Forecasting Engine With Front-End Weather Forecast,” IEEE Industrial Applications, vol. 43, no. 6, Page(s). 1410- 1416, 2007.
W. Charytoniuk and M. Chen, ,Very Short-term Load Forecasting Using Artificial Neural Networks, IEEE Trans. Power Systems, vol. 15, no. 1, Page(s). 263- 268, 2000
Moghram and S. Rahman, ,Analysis and Evaluation of Five Short-Term Load Forecasting Techniques, IEEE Trans Power Syst., vol. 4, no. 4, Page(s). 1484–1491, Nov. 1989.
D. Papalexopoulos and T. C. Hesterberg,,A regression-based approach to short-term system load forecasting, IEEE Trans Power Syst., vol. 5, no. 4, Page(s). 1535–1547, Nov. 1990.
N. Amjady, ,Short-term hourly load forecasting using time-series odeling with peak load estimation capability, IEEE Trans Power Syst., vol. 15, no. 3, Page(s). 498–505, Aug. 2001.
W. Charytoniuk, M. S. Chen, and P. Van Olinda, Nonparametric regression based short-term load forecasting, IEEE Trans. on Power Systems, vol. 13, no. 3, Page(s). 725-730, Aug. 1998.
R. C. Bansal, ,Bibliography on the Fuzzy Set Theory Applications in Power Systems (1994–2001), IEEE Transac-tions on Power Systems, Vol. 18, No. 4, (2003), Page(s). 1291-1299.
H. Mao, X. Zeng, G. Leng, Y. Zhai, and J. A. Keane, ,Short-Term and Midterm Load Forecasting Using a Bilevel Optimization Model, IEEE Transactions on Power Systems, Vol. 24, No. 2, (2009), Page(s). 1080-1090.
S. H. Ling, F. Leung, H. K. Lam, and P. Tam, ,Short-Term Electric Load Forecasting Based on a Neural Fuzzy Network, IEEE Transactions on Industrial Electronics, Vol. 50, No. 6, (2003), Page(s). 1305-1316.
J. H. Holland, "Adaptation in Natural and Artificial Systems, The University of Michigan Press, Ann Arbor., (1975).
El-Zein, M. M. El Bahy and M. Talaat, ,A Simulation Model for Electrical Tree in Solid Insulation Using CSM Coupled with GAs, IEEE CEIDP Conference, October (2008), Page(s). 645-649.
Heng, E.T.H., Srinivasan, D., Liew, A.C., ,Short term load forecasting using genetic algorithm and neural networks, Energy Management and Power Delivery, 1998. Proceedings of EMPD '98. 1998 International Conference on, Vol. 2, 3-5 March 1998, Page(s):576 - 581 vol.2.
Worawit, T., Wanchai, C. Substation short term load forecasting using neural network with genetic algorithm, TENCON '02. Proceedings. 2002 IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering; Vol. 3, 28-31 Oct. 2002, Page(s):1787 - 1790.
Azzam-ul-Asar, ul Hassnain, S.R., Khan, A., Short term load forecasting using particle swarm optimization based ANN approach, Neural Networks, 2007. IJCNN 2007. International Joint Conference; 12-17 Aug. 2007,Page(s):1476 – 1481.
Wei Sun, Ying Zou, Short term load forecasting based on BP neural network trained by PSO, International Conference on Machine Learning and Cybernetics ;Vol. 5, 19-22 Aug. 2007, Page(s):2863 – 2868.
Bashir, Z.A., El-Hawary, Short-term load forecasting using artificial neural network based on particle swarm optimization algorithm, Electrical and Computer Engineering, 2007. CCECE 2007. Canadian Conference on; 22-26 April 2007, Page(s):272 – 275.
You Yong, Wang Sun'an, Sheng Wanxing, ,Short-term load forecasting using artificial immune network, Power System Technology, 2002. Proceedings. International PowerCon 2002. Vol. 4, 13-17 Oct. 2002,Page(s):2322 - 2325 vol.4.
Chengqun Yin, Lifeng Kang, Wei Sun, ,Hybrid neural network model for short term load forecasting, Third International Conference on Natural Computation, 2007.
Biswas, S. Das, A. Abraham, and S. Dasgupta, Analysis of the reproduction operator in an artificial bacterial foraging system, Applied Mathematics and Computation, vol. 215,Page(s). 3343-3355, 2010.
R.-A. Hooshmand, M. Parastegari, and M. J. Morshed, Emission, reserve and economic load dispatch problem with non-smooth and non-convex cost functions using the hybrid bacterial foraging-Nelder–Mead algorithm, Applied Energy,vol. 89, Page(s). 443-453, 2012.
Majhi, G. Panda, B. Majhi, and G. Sahoo, Efficient prediction of stock market indices using adaptive bacterial foraging optimization (ABFO) and BFO based techniques, Expert Systems with Applications, vol. 36, Page(s). 10097-10104,2009.
Y. Zhang and L. Wu, Bacterial chemotaxis optimization for protein folding model, in 5th International Conference on Natural Computation, ICNC 2009, August 14, 2009 – August 16, 2009, Tianjian, China, 2009, Page(s). 159-162.
- There are currently no refbacks.
Please send any question about this web site to email@example.com
Copyright © 2005-2023 Praise Worthy Prize