Continuous Conditional Random Field Model for Predicting the Electrical Load of a Combined Cycle Power Plant
- Title
- Continuous Conditional Random Field Model for Predicting the Electrical Load of a Combined Cycle Power Plant
- Author
- 허선
- Keywords
- Continuous Conditional Random Field; Machine Learning; Combined Cycle Power Plant; Energy Saving; Prediction
- Issue Date
- 2016-06
- Publisher
- KOREAN INST INDUSTRIAL ENGINEERS
- Citation
- INDUSTRIAL ENGINEERING AND MANAGEMENT SYSTEMS, v. 15, NO. 2, Page. 148-155
- 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.
- URI
- http://koreascience.or.kr/article/JAKO201620853200476.pagehttps://repository.hanyang.ac.kr/handle/20.500.11754/182838
- ISSN
- 1598-7248;2234-6473
- DOI
- 10.7232/iems.2016.15.2.148
- Appears in Collections:
- COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > INDUSTRIAL AND MANAGEMENT ENGINEERING(산업경영공학과) > Articles
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