Abstract
Ultra-short-term power load forecasting, which is a complex and nonlinear optimization problem, is an important problem in power system. Self-adaptive Differential Evolution (SaDE), whose control parameter (mutation factor F, crossover factor CR) and mutation strategy are changed gradually and adaptively according to the previous search performance, has been a widely used optimization algorithm among so many improved Differential Evolutions for its strong ability of global numerical optimization and good convergence characteristic. SaDE is employed to optimize a two-layer Neural Network (NN) for the problem of Ultra-short-term power load forecasting. The result shows that SaDE has higher accuracy comparing with Back Propagation (BP) when it is applied in Ultra-short-term power load forecasting.
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Liu, W., Song, H., Liang, J.J., Qu, B., Qin, A.K. (2014). Neural Network Based on Self-adaptive Differential Evolution for Ultra-Short-Term Power Load Forecasting. In: Huang, DS., Han, K., Gromiha, M. (eds) Intelligent Computing in Bioinformatics. ICIC 2014. Lecture Notes in Computer Science(), vol 8590. Springer, Cham. https://doi.org/10.1007/978-3-319-09330-7_47
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DOI: https://doi.org/10.1007/978-3-319-09330-7_47
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