Journal of Korean Institute of Industrial Engineers (대한산업공학회지)

Korean Institute of Industrial Engineers (대한산업공학회)
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Electricity Price Prediction Based on Semi-Supervised Learning and Neural Network Algorithms

준지도 학습 및 신경망 알고리즘을 이용한 전기가격 예측

  • Kim, Hang Seok (Department of Industrial Engineering, Ajou University) ;
  • Shin, Hyun Jung (Department of Industrial Engineering, Ajou University)
  • 김항석 (아주대학교 산업공학과) ;
  • 신현정 (아주대학교 산업공학과)
  • Received : 2012.09.06
  • Accepted : 2013.01.03
  • Published : 2013.02.15
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Abstract

Predicting monthly electricity price has been a significant factor of decision-making for plant resource management, fuel purchase plan, plans to plant, operating plan budget, and so on. In this paper, we propose a sophisticated prediction model in terms of the technique of modeling and the variety of the collected variables. The proposed model hybridizes the semi-supervised learning and the artificial neural network algorithms. The former is the most recent and a spotlighted algorithm in data mining and machine learning fields, and the latter is known as one of the well-established algorithms in the fields. Diverse economic/financial indexes such as the crude oil prices, LNG prices, exchange rates, composite indexes of representative global stock markets, etc. are collected and used for the semi-supervised learning which predicts the up-down movement of the price. Whereas various climatic indexes such as temperature, rainfall, sunlight, air pressure, etc, are used for the artificial neural network which predicts the real-values of the price. The resulting values are hybridized in the proposed model. The excellency of the model was empirically verified with the monthly data of electricity price provided by the Korea Energy Economics Institute.

Keywords

References

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