전기전자학회논문지 (Journal of IKEEE)

  • 제22권3호
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  • Pages.672-679
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  • 2018
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  • 1226-7244(pISSN)
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  • 2288-243X(eISSN)
한국전기전자학회 (Institute of Korean Electrical and Electronics Engineers)
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Short-term Load Forecasting of Buildings based on Artificial Neural Network and Clustering Technique

  • Ngo, Minh-Duc (Dept. of Electrical Engineering, Chonnam National University) ;
  • Yun, Sang-Yun (Dept. of Electrical Engineering, Chonnam National University) ;
  • Choi, Joon-Ho (Dept. of Electrical Engineering, Chonnam National University) ;
  • Ahn, Seon-Ju (Dept. of Electrical Engineering, Chonnam National University)
  • 투고 : 2018.09.05
  • 심사 : 2018.09.18
  • 발행 : 2018.09.30
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초록

Recently, microgrid (MG) has been proposed as one of the most critical solutions for various energy problems. For the optimal and economic operation of MGs, it is very important to forecast the load profile. However, it is not easy to predict the load accurately since the load in a MG is small and highly variable. In this paper, we propose an artificial neural network (ANN) based method to predict the energy use in campus buildings in short-term time series from one hour up to one week. The proposed method analyzes and extracts the features from the historical data of load and temperature to generate the prediction of future energy consumption in the building based on sparsified K-means. To evaluate the performance of the proposed approach, historical load data in hourly resolution collected from the campus buildings were used. The experimental results show that the proposed approach outperforms the conventional forecasting methods.

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참고문헌

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