Continuous Conditional Random Field Model for Predicting the Electrical Load of a Combined Cycle Power Plant
- Authors
- Ahn, Gilseung; Hur, Sun
- Issue Date
- Jun-2016
- Publisher
- KOREAN INST INDUSTRIAL ENGINEERS
- Keywords
- Continuous Conditional Random Field; Machine Learning; Combined Cycle Power Plant; Energy Saving; Prediction
- Citation
- INDUSTRIAL ENGINEERING AND MANAGEMENT SYSTEMS, v.15, no.2, pp.148 - 155
- Indexed
- SCOPUS
KCI
- Journal Title
- INDUSTRIAL ENGINEERING AND MANAGEMENT SYSTEMS
- Volume
- 15
- Number
- 2
- Start Page
- 148
- End Page
- 155
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/13633
- DOI
- 10.7232/iems.2016.15.2.148
- ISSN
- 1598-7248
- 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.
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