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


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.


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


Grant : 글로벌박사펠로우십사업

Supported by : 한양대학교(ERICA캠퍼스)


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