Data Mining Techniques Applied to Power Plant Performance Monitoring

Flynn, Damian; Ritchie, J. W. K.; Cregan, Michael

doi:10.3182/20050703-6-cz-1902.01637

Cited by 17 publications

(9 citation statements)

References 14 publications

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“…PLS derives its usefulness from its ability to analyze data with many, noisy, collinear, and even incomplete variables in both X and Y. PLS has the desirable property that the precision of the model parameters improves with the increasing number of relevant variables and observations. An example of the use of PLS-based models can be found in [43,44] where a comparative study of soft sensors derived using PLS and an extended Kalman filter is presented. The procedure proposed allows nonlinear characteristics to be removed from the data by using suitable transformations and, hence, PLS to be adapted to a nonlinear problem.…”

Section: Data Driven Modelsmentioning

confidence: 99%

Data driven models for a PEM fuel cell stack performance prediction

Napoli

Ferraro

Sergi

et al. 2013

International Journal of Hydrogen Energy

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Section: Data Driven Modelsmentioning

confidence: 99%

Data driven models for a PEM fuel cell stack performance prediction

Napoli

Ferraro

Sergi

et al. 2013

International Journal of Hydrogen Energy

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“…Another hybrid methodology using ANN and PLS (Partial Least Squares) has been proposed by Flynn et al [37] for predicting the NO x emissions of a CC.…”

Section: Introductionmentioning

confidence: 99%

A methodology for energy savings verification in industry with application for a CHP (combined heat and power) plant

Rossi

Velázquez

2015

Energy

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“…Smrekar et al [ 12 ] predicted power generated by a coal-fired power plant. Thermal efficiency and power plant pollutant were also predicted with ANNs [ 13 , 14 , 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

Heat Rate Prediction of Combined Cycle Power Plant Using an Artificial Neural Network (ANN) Method

Arferiandi¹,

Caesarendra

Nugraha³

2021

Sensors

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Heat rate of a combined cycle power plant (CCPP) is a parameter that is typically used to assess how efficient a power plant is. In this paper, the CCPP heat rate was predicted using an artificial neural network (ANN) method to support maintenance people in monitoring the efficiency of the CCPP. The ANN method used fuel gas heat input (P1), CO2 percentage (P2), and power output (P3) as input parameters. Approximately 4322 actual operation data are generated from the digital control system (DCS) in a year. These data were used for ANN training and prediction. Seven parameter variations were developed to find the best parameter variation to predict heat rate. The model with one input parameter predicted heat rate with regression R2 values of 0.925, 0.005, and 0.995 for P1, P2, and P3. Combining two parameters as inputs increased accuracy with regression R2 values of 0.970, 0.994, and 0.984 for P1 + P2, P1 + P3, and P2 + P3, respectively. The ANN model that utilized three parameters as input data had the best prediction heat rate data with a regression R2 value of 0.995.

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Data Mining Techniques Applied to Power Plant Performance Monitoring

Cited by 17 publications

References 14 publications

Data driven models for a PEM fuel cell stack performance prediction

Data driven models for a PEM fuel cell stack performance prediction

A methodology for energy savings verification in industry with application for a CHP (combined heat and power) plant

Heat Rate Prediction of Combined Cycle Power Plant Using an Artificial Neural Network (ANN) Method

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