Pattern-based Long Short-term Memory for Mid-term Electrical Load Forecasting

@article{Pelka2020PatternbasedLS,
  title={Pattern-based Long Short-term Memory for Mid-term Electrical Load Forecasting},
  author={Pawel Pelka and G. Dudek},
  journal={2020 International Joint Conference on Neural Networks (IJCNN)},
  year={2020},
  pages={1-8}
}
  • Pawel Pelka, G. Dudek
  • Published 2020
  • Computer Science, Engineering, Mathematics
  • 2020 International Joint Conference on Neural Networks (IJCNN)
    • This work presents a Long Short-Term Memory (LSTM) network for forecasting a monthly electricity demand time series with a one-year horizon. The novelty of this work is the use of pattern representation of the seasonal time series as an alternative to decomposition. Pattern representation simplifies the complex nonlinear and nonstationary time series, filtering out the trend and equalizing variance. Two types of patterns are defined: x-pattern and y-pattern. The former requires additional… CONTINUE READING
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    3ETS+RD-LSTM: A New Hybrid Model for Electrical Energy Consumption Forecasting

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