Journal of Integrative Natural Science (통합자연과학논문집)

The Basic Science Institute Chosun University (조선대학교 기초과학연구원)
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Comparison of Different Deep Learning Optimizers for Modeling Photovoltaic Power

  • Poudel, Prasis (Department of Multimedia Engineering, Mokpo Nat'l University) ;
  • Bae, Sang Hyun (Department of Computer Science & Statistics, Chosun University) ;
  • Jang, Bongseog (Department of Multimedia Engineering, Mokpo Nat'l University)
  • Received : 2018.12.10
  • Accepted : 2018.12.15
  • Published : 2018.12.30
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Abstract

Comparison of different optimizer performance in photovoltaic power modeling using artificial neural deep learning techniques is described in this paper. Six different deep learning optimizers are tested for Long-Short-Term Memory networks in this study. The optimizers are namely Adam, Stochastic Gradient Descent, Root Mean Square Propagation, Adaptive Gradient, and some variants such as Adamax and Nadam. For comparing the optimization techniques, high and low fluctuated photovoltaic power output are examined and the power output is real data obtained from the site at Mokpo university. Using Python Keras version, we have developed the prediction program for the performance evaluation of the optimizations. The prediction error results of each optimizer in both high and low power cases shows that the Adam has better performance compared to the other optimizers.

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References

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