• Title/Summary/Keyword: 예인포인트

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Proposal of a Step-by-Step Optimized Campus Power Forecast Model using CNN-LSTM Deep Learning (CNN-LSTM 딥러닝 기반 캠퍼스 전력 예측 모델 최적화 단계 제시)

  • Kim, Yein;Lee, Seeun;Kwon, Youngsung
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.21 no.10
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    • pp.8-15
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    • 2020

  • A forecasting method using deep learning does not have consistent results due to the differences in the characteristics of the dataset, even though they have the same forecasting models and parameters. For example, the forecasting model X optimized with dataset A would not produce the optimized result with another dataset B. The forecasting model with the characteristics of the dataset needs to be optimized to increase the accuracy of the forecasting model. Therefore, this paper proposes novel optimization steps for outlier removal, dataset classification, and a CNN-LSTM-based hyperparameter tuning process to forecast the daily power usage of a university campus based on the hourly interval. The proposing model produces high forecasting accuracy with a 2% of MAPE with a single power input variable. The proposing model can be used in EMS to suggest improved strategies to users and consequently to improve the power efficiency.

  • https://doi.org/10.5762/KAIS.2020.21.10.8 인용 PDF KSCI