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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References
Gross, G., Galiana, F.D.: Short-term power load forecasting [J]. Proceedings of the IEEE, Vol. 75(12), pp. 1558 – 1573(1987)
Hamadi, H. M. Al.: Long-Term Electric Power Load Forecasting Using Fuzzy Linear Regression Technique. In Power Engineering and Automation Conference (PEAM), 2011 IEEE, pp. 96-99(2011)
Zhao, S.B., Zhang, F.S., Zhong, J.Y., Tian, H.: An Adaptive Differential Evolution Algorithm and Its Application in Reactive Power Optimization of Power System, Power System Technology, Vol. 34(6), pp. 169-174(2010)
Zhang, Q.: Research on Mid-long Load Forecasting Based on SVM and Wavelet Neural Network. International Conference on Machine Vision and Human-Machine Interface (MVHI), pp. 283-287(2010)
Hsu, Y.Y.: Fuzzy expert systems: an application to short-term power load forecasting [J]. IEE Proceedings C: Generation Transmission and Distribution, 139(6). 471-477 (1992)
Francis, E., Tay, H.: Application of support vector machines in financial time series forecasting [J]. Omega, Vol. 29(4). pp. 309-317(2001)
Rüping, S.: Incremental learning with support vector machines. Proceedings-IEEE International Conference on Data Mining, ICDM. pp. 641-642(2001)
Luo, X., Zhou, Y.H., Zhou, H.: Forecasting the daily load based on ANN. In: Control theory and application. pp. 1–4(2007)
Kim, C.I., Yu, I. K.: Kohonen neural network and transform based approach to short-term power load forecasting. Elect Elecr Power Syst Res 63(3). pp. 169–1765 (2002)
Qin, A.K., Huang, V.L., Suganthan, P.N.: Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization, IEEE Transactions on Evolutionary Computation, VOL. 13(2),pp. 398-417(2009)
Werbos, P.J.: Beyond regression:New tools for predictions and analysis in the behavioral science,Ph D Thesis,Harvard University(1974)
Storn, R., Price, K.V.: Differential evolution: A simple and efficient adaptive scheme for global optimization over continuous spaces, ICSI, Tech. Rep. TR-95-012(1995)
Price, K.V.: Differential evolution vs. the functions of the 2nd ICEO, in Proc. IEEE Int. Conf. Evol. Comput., pp. 153–157( 1997)
Price, K.V., Storn, R.: Differential evolution: A simple evolution strategy for fast optimization, Dr. Dobb’s J., vol. 22, no. 4, pp. 18–24(1997)
Price, K.V., Storn, R., Lampinen, J.A.: Differential Evolution : A Practical Approach to Global Optimization, in Natural Computing Series Berlin: Springer(2005)
Price, K.V.: Differential evolution vs. the functions of the 2nd ICEO, in Proc. IEEE Int. Conf. Evol. Comput., pp. 153–157(1997)
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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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