Prediction of Electrical Output Power of Combined Cycle Power Plant Using Regression ANN Model
[1]
Elkhawad Elfaki, Department of Mechanical Engineering, Bisha University, Bisha, Saudi Arabia.
[2]
Ahmed Hassan Ahmed Hassan, Department of Mechanical Engineering, Ondokuz Mayis University, Samsun, Turkey.
Recently, regression artificial neural networks are used to model various systems that have high dimensionality with nonlinear relations. The system under study must have enough dataset available to train the neural network. The aim of this work is to apply and experiment various options effects on feed-foreword artificial neural network (ANN) which used to obtain regression model that predicts electrical output power (EP) of combined cycle power plant based on 4 inputs. Dataset is obtained from an open online source. The work shows and explains the stochastic behavior of the regression neural, experiments the effect of number of neurons of the hidden layers. It shows also higher performance for larger training dataset size; at the other hand, it shows different effect of larger number of variables as input. In addition, two different training functions are applied and compared. Lastly, simple statistical study on the error between real values and estimated values using ANN is conducted, which shows the reliability of the model. This paper provides a quick reference to the effects of main parameters of regression neural networks.
Neural Networks, Regression, Combined Power Cycle, MATLAB Neural Networks Toolbox
[1]
U. Kesgin and H. Heperkan, “Simulation of thermodynamic systems using soft computing techniques,” Int. J. Energy Res., vol. 29, no. 7, pp. 581–611, 2005.
[2]
A. Dehghani Samani, “Combined cycle power plant with indirect dry cooling tower forecasting using artificial neural network,” Decis. Sci. Lett., vol. 7, no. 2, pp. 131–142, 2018.
[3]
P. R. Norvig and S. A. Intelligence, “A modern approach,” Manuf. Eng., vol. 74, no. 3, pp. 111–113, 1995.
[4]
E. Rich and K. Knight, “Artificial intelligence,” McGraw-Hill, New, 1991.
[5]
M. T. Hagan, H. B. Demuth, and M. H. Beale, “Orlando De Jesus,” Neural Netw. Des. 2nd Ed. Cengage Learn., 2014.
[6]
D. Jahed Armaghani, M. F. Mohd Amin, S. Yagiz, R. S. Faradonbeh, and R. A. Abdullah, “Prediction of the uniaxial compressive strength of sandstone using various modeling techniques,” Int. J. Rock Mech. Min. Sci., vol. 85, pp. 174–186, May 2016.
[7]
H. Moayedi and D. Jahed Armaghani, “Optimizing an ANN model with ICA for estimating bearing capacity of driven pile in cohesionless soil,” Eng. Comput., vol. 34, no. 2, pp. 347–356, Apr. 2018.
[8]
M. Khandelwal et al., “Implementing an ANN model optimized by genetic algorithm for estimating cohesion of limestone samples,” Eng. Comput., vol. 34, no. 2, pp. 307–317, Apr. 2018.
[9]
A. Baghban, F. Pourfayaz, M. H. Ahmadi, A. Kasaeian, S. M. Pourkiaei, and G. Lorenzini, “Connectionist intelligent model estimates of convective heat transfer coefficient of nanofluids in circular cross-sectional channels,” J. Therm. Anal. Calorim., vol. 132, no. 2, pp. 1–27, May 2017.
[10]
H. Khosravani, M. Castilla, M. Berenguel, A. Ruano, and P. Ferreira, “A Comparison of Energy Consumption Prediction Models Based on Neural Networks of a Bioclimatic Building,” Energies, vol. 9, no. 1, p. 57, Jan. 2016.
[11]
A. S. Jihad and M. Tahiri, “Forecasting the heating and cooling load of residential buildings by using a learning algorithm ‘gradient descent’, Morocco,” Case Stud. Therm. Eng., vol. 12, pp. 85–93, Sep. 2018.
[12]
S. Sholahudin and H. Han, “Heating Load Predictions using The Static Neural Networks Method,” Int. J. Technol., vol. 6, no. 6, p. 946, Dec. 2015.
[13]
S. M. A. N. R. Abadi, M. Mehrabi, and J. P. Meyer, “Prediction and optimization of condensation heat transfer coefficients and pressure drops of R134a inside an inclined smooth tube,” Int. J. Heat Mass Transf., vol. 124, pp. 953–966, Sep. 2018.
[14]
C. Wan, Z. Xu, P. Pinson, Z. Y. Dong, and K. P. Wong, “Optimal prediction intervals of wind power generation,” IEEE Trans. Power Syst., vol. 29, no. 3, pp. 1166–1174, May 2014.
[15]
F. Bizzarri, M. Bongiorno, A. Brambilla, G. Gruosso, and G. S. Gajani, “Model of photovoltaic power plants for performance analysis and production forecast,” IEEE Trans. Sustain. Energy, vol. 4, no. 2, pp. 278–285, Apr. 2013.
[16]
T. Mahmoud, Z. Y. Dong, and J. Ma, “An advanced approach for optimal wind power generation prediction intervals by using self-adaptive evolutionary extreme learning machine,” Renew. Energy, vol. 126, pp. 254–269, Oct. 2018.
[17]
A. Khosravi, S. Nahavandi, and D. Creighton, “Prediction intervals for short-term wind farm power generation forecasts,” IEEE Trans. Sustain. Energy, vol. 4, no. 3, pp. 602–610, Jul. 2013.
[18]
M. J. Embrechts, A. L. Schweizerhof, M. Bushman, and M. H. Sabatella, “Neural Network Modeling of Turbofan Parameters,” in Volume 4: Manufacturing Materials and Metallurgy; Ceramics; Structures and Dynamics; Controls, Diagnostics and Instrumentation; Education, 2000, p. V004T04A008.
[19]
C. Boccaletti, G. Cerri, and B. Seyedan, “A Neural Network Simulator of a Gas Turbine With a Waste Heat Recovery Section,” in Journal of Engineering for Gas Turbines and Power, 2001, vol. 123, no. 2, p. 371.
[20]
R. Bettocchi, P. Spina, and G. Torella, “Gas Turbine Health Indices Determination by Using Neural Networks,” in ASME Turbo Expo, 2002, pp. 1–7.
[21]
H. H. Erdem and S. H. Sevilgen, “Case study: Effect of ambient temperature on the electricity production and fuel consumption of a simple cycle gas turbine in Turkey,” Appl. Therm. Eng., vol. 26, no. 2–3, pp. 320–326, Feb. 2006.
[22]
I. Ceylan, O. Erkaymaz, E. Gedik, and A. E. Gurel, “The prediction of photovoltaic module temperature with artificial neural networks,” 2014.
[23]
P. Tüfekci, “Prediction of full load electrical power output of a base load operated combined cycle power plant using machine learning methods,” Int. J. Electr. Power Energy Syst., vol. 60, pp. 126–140, 2014.
[24]
L. X. Niu and X. J. Liu, “Multivariable generalized predictive scheme for gas turbine control in combined cycle power plant,” in 2008 IEEE Conference on Cybernetics and Intelligent Systems, 2008, pp. 791–796.
[25]
H. Kaya, P. Tüfekci, and S. F. Gürgen, “Local and Global Learning Methods for Predicting Power of a Combined Gas & Steam Turbine,” in International Conference on Emerging Trends in Computer and Electronics Engineering (ICETCEE 2012), 2012, pp. 13–18.
[26]
V. Ramireddy, “An Overview of Combined Cycle Power Plant,” 2015. [Online]. Available: http://electricalengineering-portal.com/an-overview-of-combined-cycle-power-plant.
[27]
M. H. B. M. T. H. H. B. Demuth, “Kalman Filtering and Neural Networks,” MathWorks, 2001.
[28]
H. Demuth, “Neural Network Toolbox,” Networks, vol. 24, no. 1. pp. 1–8, 2002.
[29]
R. POWER, “Combined Cycle Power Plant CCPP.” 2011.
[30]
Wikipedia, “68–95–99.7 Rule,” Wikipedia, 2015. [Online]. Available: https://en.wikipedia.org/wiki/68–95–99.7_rule.
[31]
“Six_Sigma.” [Online]. Available: https://en.wikipedia.org/wiki/Six_Sigma.
[32]
L. J. Kazmier, Schaum’s outline of theory and problems of business statistics, 4th ed. McGraw Hill Professional, 1996.
