Abstract
To address the shortcomings of traditional grey forecasting model GM (1, 1) in terms of poor forecasting on fast-growing power load, this paper proposes a chaotic co-evolutionary PSO algorithm which has better efficiency than the particle swarm optimization algorithm. Combined with the GM (1, 1) model, a chaotic co-evolutionary particle swarm optimization algorithm has been used to solve the values of two parameters in GM (1, 1) model. In this way, we have designed a CCPSO algorithm-based grey model. Results of case simulation on the power consumption in 3 regions show that the CCPSO-GM model is optimal over the other 4 forecasting models, proving that the CCPSO-GM model has a wide applicable scope and high forecasting accuracy.
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Hao, L., Ouyang, A., Liu, L. (2014). Method for Forecasting Medium and Long-Term Power Loads Based on the Chaotic CPSO-GM. In: Pan, L., Păun, G., Pérez-Jiménez, M.J., Song, T. (eds) Bio-Inspired Computing - Theories and Applications. Communications in Computer and Information Science, vol 472. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45049-9_26
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DOI: https://doi.org/10.1007/978-3-662-45049-9_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45048-2
Online ISBN: 978-3-662-45049-9
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