Abstract
The short-term electrical load forecasting plays a significant role in the management of power system supply for countries and regions. A new model which combines the fuzzy logic control with an improved back propagation algorithm (FLC-IBP) is proposed in this paper to improve the accuracy of the short-term load forecasting (STLF). Specifically, the composite-error-function-based method and the dynamic learning rate approach are designed to achieve a better predictable result, which mainly applies the improved back propagation algorithm (BP). Besides, the fuzzy logic control theory is used to build up a good optimization process. Experimental results demonstrate that the proposed method can improve the accuracy of load prediction.
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Acknowledgements
This research was partially supported by the Guangxi Natural Science Foundation under Grant 2015GXNSFBA139256 and 2014GXNSFBA118271, the Research Project of Guangxi University of China under Grant ZD2014022, Guangxi Key Lab of Multi-source Information Mining & Security under Grant MIMS15-07, MIMS15-06 and MIMS14-04, Guangxi Key Lab of Wireless Wideband Communication & Signal Processing under Grant GXKL0614205, the State Scholarship Fund of China Scholarship Council under Grant [2014]3012, the National Natural Science Foundation of China under Grants No.11262004, and the grant from Guangxi Experiment Center of Information Science.
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© 2017 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Wan, L., Liu, J., Qiu, S., Cen, M., Luo, Y. (2017). Short-Term Electrical Load Forecasting Based on Fuzzy Logic Control and Improved Back Propagation Algorithm. In: Hu, J., Leung, V., Yang, K., Zhang, Y., Gao, J., Yang, S. (eds) Smart Grid Inspired Future Technologies. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 175. Springer, Cham. https://doi.org/10.1007/978-3-319-47729-9_9
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DOI: https://doi.org/10.1007/978-3-319-47729-9_9
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