Power load forecasting based on double mapping improved frog leaping algorithm

Ying Lan; Qibin Hu; Libing Xue

doi:10.1117/12.2689416

7 September 2023 Power load forecasting based on double mapping improved frog leaping algorithm

Ying Lan, Qibin Hu, Libing Xue

Proceedings Volume 12790, Eighth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2023); 127904H (2023) https://doi.org/10.1117/12.2689416
Event: 8th International Conference on Electromechanical Control Technology and Transportation (ICECTT 2023), 2023, Hangzhou, China

Abstract

Power load prediction plays a very important role in power systems, and improving the accuracy of power load prediction has become a very important research problem. To increase the accuracy of LSTM neural network forecasting power, double mapping is introduced to improve the leapfrog algorithm to optimize the parameter optimization of the LSTM neural network. Given the problem that the leapfrog algorithm is prone to fall into local optimization, the DE difference algorithm and evolutionary efficiency judgment mechanism are introduced to improve the optimization ability of the leapfrog algorithm. The comparison with the power load prediction results of the traditional leapfrog algorithm shows that the improved leapfrog algorithm can improve the accuracy and accuracy of power load prediction of the LSTM neural network.

Citation Download Citation

Ying Lan, Qibin Hu, and Libing Xue "Power load forecasting based on double mapping improved frog leaping algorithm", Proc. SPIE 12790, Eighth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2023), 127904H (7 September 2023); https://doi.org/10.1117/12.2689416

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