Industrial Engineering and Management Systems

  • 제15권2호
  • /
  • Pages.148-155
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  • 2016
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  • 1598-7248(pISSN)
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  • 2234-6473(eISSN)
대한산업공학회 (Korean Institute of Industrial Engineers)
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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)
  • 투고 : 2016.02.14
  • 심사 : 2016.06.05
  • 발행 : 2016.06.30
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초록

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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참고문헌

  1. AlRashidi, M. R. and El-Naggar, K. M. (2010), Long term electric load forecasting based on particle swarm optimization, Applied Energy, 87(1), 320-326. https://doi.org/10.1016/j.apenergy.2009.04.024
  2. Clifton, A., Kilcher, L., Lundquist, J. K., and Fleming, P. (2013), Using machine learning to predict wind turbine power output, Environmental research letters, 8(2), 0204009.
  3. Fan, G. F., Peng, L. L., Hong, W. C., and Sun, F. (2016) Electric load forecasting by the SVR model with differential empirical mode decomposition and auto regression, Neurocomputing, 173, 958-970. https://doi.org/10.1016/j.neucom.2015.08.051
  4. Kaya, H., Tufekci, P., and Gurgen, F. S. (2012), Local and global learning methods for predicting power of a combined gas and steam turbine, in: International conference on emerging trends in computer and electronics engineering.
  5. Kesgin, U. and Heperkan, H. (2005), Simulation of thermodynamic systems using soft computing techniques, International journal of energy research, 29(7), 581-611. https://doi.org/10.1002/er.1095
  6. Kumar, S. and Hebert, M. (2003), Discriminative random fields: A discriminative framework for contextual interaction in classification, in: computer Vision, Proceedings Ninth IEEE International Conference on, 1150-1157.
  7. Lafferty, J., McCallum, A., and Pereira, F. C. (2011), Conditional random fields: Probabilistic models for segmenting and labelling sequence data.
  8. Liu, Y., Carbonell, J., Klein-Seetharaman, J., and Gopalakrishnan, V. (2004), Comparison of probabilistic combination methods for protein secondary structure prediction, Bioinformatics, 20(17), 3099-3107. https://doi.org/10.1093/bioinformatics/bth370
  9. McCallum, A. (2002), Efficiently inducing features of conditional random fields, in: proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence, 403-410.
  10. Niu, L. X. and Liu, X. J. (2008), Multivariable generalized predictive scheme for gas turbine control in combined cycle power plant, in: Cybernetics and Intelligent Systems, IEEE Conference on, 791-796.
  11. Prokop, L., Misak, S., Snasel, V., Platos, J., Kromer, P. (2013), Supervised learning of photovoltaic power plant output prediction models, Neural Network World, 23(4), 321-338. https://doi.org/10.14311/NNW.2013.23.020
  12. Radosavljevic, V., Vucetic, S., and Obradovic, Z. (2010), Continuous Conditional Random Fields for Regression in Remote Sensing, in: ECAI, 809-814.
  13. Tufekci, P. (2014), Prediction of full load electrical power output of a base load operated combined cycle power plant using machine learning methods, International Journal of Electrical Power and Energy Systems, 60, 126-140. https://doi.org/10.1016/j.ijepes.2014.02.027
  14. Xie, J. and Hong, T. (2015), GEF Com 2014 probabilistic electric load forecasting: An integrated solution with forecast combination and residual, simulation, International Journal of Forecasting.
  15. Yadav, V. and Srinivasan, D. (2011), A SOM-based hybrid linear-neural model for short-term load forecasting, Neurocomputing, 74(17), 2874-2885. https://doi.org/10.1016/j.neucom.2011.03.039
  16. Yu, F. and Xu, X. (2014), A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network, Applied Energy, 134, 102-113. https://doi.org/10.1016/j.apenergy.2014.07.104

피인용 문헌

  1. Probabilistic Graphical Framework for Estimating Collaboration Levels in Cloud Manufacturing vol.9, pp.2, 2017, https://doi.org/10.3390/su9020277
  2. Power Prediction of Combined Cycle Power Plant (CCPP) Using Machine Learning Algorithm-Based Paradigm vol.2021, pp.None, 2021, https://doi.org/10.1155/2021/9966395