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Deep Neural Network Model For Short-term Electric Peak Load Forecasting

단기 전력 부하 첨두치 예측을 위한 심층 신경회로망 모델

  • Hwang, Heesoo (Department of Electrical and Electronic Engineering, Halla University)
  • 황희수 (한라대학교 전기전자공학과)
  • Received : 2018.03.08
  • Accepted : 2018.05.20
  • Published : 2018.05.28

Abstract

In smart grid an accurate load forecasting is crucial in planning resources, which aids in improving its operation efficiency and reducing the dynamic uncertainties of energy systems. Research in this area has included the use of shallow neural networks and other machine learning techniques to solve this problem. Recent researches in the field of computer vision and speech recognition, have shown great promise for Deep Neural Networks (DNN). To improve the performance of daily electric peak load forecasting the paper presents a new deep neural network model which has the architecture of two multi-layer neural networks being serially connected. The proposed network model is progressively pre-learned layer by layer ahead of learning the whole network. For both one day and two day ahead peak load forecasting the proposed models are trained and tested using four years of hourly load data obtained from the Korea Power Exchange (KPX).

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