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Continuous Conditional Random Field Model for Predicting the Electrical Load of a Combined Cycle Power Plant

  • Ahn, Gilseung (Department of Industrial and Management Engineering, Hanyang University) ;
  • Hur, Sun (Department of Industrial and Management Engineering, Hanyang University)
  • Received : 2016.02.14
  • Accepted : 2016.06.05
  • Published : 2016.06.30

Abstract

Existing power plants may consume significant amounts of fuel and require high operating costs, partly because of poor electrical power output estimates. This paper suggests a continuous conditional random field (C-CRF) model to predict more precisely the full-load electrical power output of a base load operated combined cycle power plant. We introduce three feature functions to model association potential and one feature function to model interaction potential. Together, these functions compose the C-CRF model, and the model is transformed into a multivariate Gaussian distribution with which the operation parameters can be modeled more efficiently. The performance of our model in estimating power output was evaluated by means of a real dataset and our model outperformed existing methods. Moreover, our model can be used to estimate confidence intervals of the predicted output and calculate several probabilities.

Keywords

Continuous Conditional Random Field;Machine Learning;Combined Cycle Power Plant;Energy Saving;Prediction

Acknowledgement

Grant : 글로벌박사펠로우십사업

Supported by : 한양대학교(ERICA캠퍼스)

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