Continuous Conditional Random Field Model for Predicting the Electrical Load of a Combined Cycle Power Plant Gilseung Ahn, Sun Hur* Department of Industrial and Management Engineering, Hanyang University, Ansan, Korea (Received: February 14, 2016 / Revised: May 1, 2016 / Accepted: June 5, 2016) 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 poten- tial. 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 prob- abilities. Keywords: Continuous Conditional Random Field, Machine Learning, Combined Cycle Power Plant, Energy Saving, Prediction * Corresponding Author, E-mail: hursun@hanyang.ac.kr 1. INTRODUCTION As the demand for electric power has grown rap- idly during the past several decades, so has the interest in the combined cycle power plant (CCPP). This is be- cause CCPPs are known to be very efficient and require relatively low investment costs. A CCPP is composed of a gas turbine, steam turbine, and heat recovery system generators. The two turbines are combined in one cycle and the heat or gas flow transfers the energy from one of the turbines to the other. In general, a gas turbine ex- hausts gas that is used to produce heat, which is used to make the steam required by the steam turbine (Niu and Liu, 2008). Numerous control strategies have been developed to reduce CCPP operational costs, but still a more advan- ced control strategy is necessary to further reduce the entire operational cost. Tüfekci (2014) suggests that it is essential for a base load power plant to predict electrical power outputs correctly in order to attain a maximum profit. Existing plants, however, consume a significant amount of fuel and have high operating expenses partly because of poor prediction of electrical power output requirements. Particularly, the reliability and sustain- ability of the gas turbine are highly affected by the pre- diction of the power generation needs. Some studies adopting thermodynamic approaches to obtain an accurate prediction for the power generation have been done. In order to forecast the power genera- tion accurately with these approaches, however, many as- sumptions, such as the existence of some empirical rela- tionships, are necessary since they account for unpre- Industrial Engineering & Management Systems Vol 15, No 2, June 2016, pp.148-155 http://dx.doi.org/10.7232/iems.2016.15.2.148 ISSN 1598-7248│EISSN 2234-6473│ © 2016 KIIE