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

Arferiandi, Yondha Dwika; Caesarendra, Wahyu; Nugraha, Herry

doi:10.3390/s21041022

Cited by 9 publications

(3 citation statements)

References 19 publications

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“…Te heat rate is incorporated as an output parameter using three input parameters, including the fuel gas heat rate (P1), the CO 2 percentage (P2), and the power output (P3), for the training/evaluation processes. Tis combination and the absence of the input parameters (P1, P2, and P3) achieved an improved heat rate with an assigned regression value of 0.995 [31].…”

Section: Literature Reviewmentioning

confidence: 99%

Modelling and Output Power Estimation of a Combined Gas Plant and a Combined Cycle Plant Using an Artificial Neural Network Approach

Xezonakis,

Samuel,

Enweremadu

2024

Journal of Engineering

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Artificial neural networks (ANNs) have gained prominence among contemporary computing techniques due to their capacity to handle complicated stochastic datasets and nonlinear modelling in combined gas and combined cycle power (COGAS) plants. Researchers, academicians, and stakeholders have been unable to predict, ensure effective operation, and prevent power outages in COGAS due to the nonlinearity. The first implementation of the simultaneous adoption of three types of ANNs using Levenberg–Marquardt (LM), Bayesian regularisation (BR), and scaled conjugate gradient (SCG) configurations for training and assessing a combined cycle power plant output is presented. The dataset used in this research is a 9568-unit full combined cycle power plant basis load dataset, accessible through the public UCI Machine Learning Repository. It incorporates ambient temperature, exhaust vacuum, ambient pressure, and relative humidity as input parameters to predict the electric output power. The most accurate and dependable electric power predictions could be identified for 70% of the total data, of which 6698 were trained, 15% were tested, and 15% were validated (2870). By using the three training techniques, namely, LM, BR, and SCG, the parameterized networks are studied, increasing the number of hidden layers from 20 to 500. The lowest root-mean-square error value for a multilayer perceptron (MLP) architecture is 3.631%, which is lower than the values of 4.17%, 4.35%, and 4.63% for comparable MLP structures (20 to 500), documented in the literature. The LM and BR algorithms outperform SCG. These adopted algorithms could be a cutting-edge application in the power plant industry and other real-world applications for reliable solutions, to satisfy emerging societal needs with environmental benefits.

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Section: Literature Reviewmentioning

confidence: 99%

Modelling and Output Power Estimation of a Combined Gas Plant and a Combined Cycle Plant Using an Artificial Neural Network Approach

Xezonakis,

Samuel,

Enweremadu

2024

Journal of Engineering

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“…The results reveal that WCA is more efficient than ALO and SBO for MLP optimization Arferiandi et.al. [15] Different ANN configurations were implemented for different combinations of features (total features = 3) to accurately predict the heat rate.…”

Section: Water Cycle (Wca) Ant Lion (Alo) and Satin Bowerbird (Sbo) O...mentioning

confidence: 99%

Electrical Energy Prediction of Combined Cycle Power Plant Using Gradient Boosted Generalized Additive Model

Pachauri

Ahn

2022

IEEE Access

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A combined cycle power plant (CCPP) employs gas and steam turbines to generate 50% more power while utilizing the same fuel as a normal single cycle plant. The performance of a CCPP under full load is affected by a variety of factors such as weather, process interactions, and coupling, which makes it challenging to operate. Therefore, a reliable assessment of the maximum output power of a CCPP is required to improve plant reliability and monetary performance. In this paper, a predictive model based on a generalized additive model (GAM) is proposed for the electrical power prediction of a CCPP at full load. In GAM, a boosted tree and gradient boosting algorithm are considered as shape function and learning technique for modeling a non-linear relationship between input and output attributes. Furthermore, predictive models based on linear regression (LR), M5/2 Gaussian process model (GPR), multilayer perceptron neural network (MLP), support vector regression (SVR), decision tree (DT), and bootstrapaggregated tree (BBT) are also designed for comparison purposes. Results reveal that GAM improves the RMSE by 74%, 68.8%, 70.3%, 54.8%, 21.2%, and 17.3% compared to LR, GPR, MLP, SVR, DT, and BBT, respectively. Finally, it can be concluded that the proposed method is effective, robust, and accurate for the assessment of the maximum output power of a CCPP to improve plant consistency and financial performance.

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“…Heat rate of a combined cycle power plant is a parameter that is typically used to assess how efficient a power plant is. In [27], the CCPP heat rate was predicted using an ANN method to support the maintenance team in monitoring the efficiency of the CCPP. The ANN method used fuel gas heat input, CO 2 percentage, and power output as input parameters.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Prediction of combined cycle power plant electrical output power using machine learning regression algorithms

Santarisi

Faouri

2021

EEJET

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In order to monitor the performance and related efficiency of a combined cycle power plant (CCPP), in addition to the best utilization of its power output, it is vital to predict its full load electrical power output. In this paper, the full load electrical power output of CCPP was predicted employing practically efficient machine learning algorithms, including linear regression, ridge regression, lasso regression, elastic net regression, random forest regression, and gradient boost regression. The original data came from an actual confidential power plant, which was working on a full load for 6 years, with four major features: ambient temperature, relative humidity, atmospheric pressure, and exhaust vacuum, and one target (electrical power output per hour). Different regression performance measures were used, including R2 (coefficient of determination), MAE (Mean Absolute Error), MSE (Mean Squared Error), RMSE (Root Mean Squared Error), and MAPE (Mean Absolute Percentage Error). Research results revealed that the gradient boost regression model outperformed other models with and without using the dimensionality reduction technique (PCA) with the highest R2 of 0.912 and 0.872, respectively, and had the lowest MAPE of 0.872 % and 1.039 %, respectively. Moreover, prediction performance dropped slightly after using the dimensionality reduction technique almost in all regression algorithms used. The novelty in this work is summarized in predicting electrical power output in a CCPP based on a few features using simpler algorithms than reported deep learning and neural networks algorithms combined. That means a lower cost and less complicated procedure as per each, however, resulting in practically accepted results according to the evaluation metrics used.

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

Cited by 9 publications

References 19 publications

Modelling and Output Power Estimation of a Combined Gas Plant and a Combined Cycle Plant Using an Artificial Neural Network Approach

Modelling and Output Power Estimation of a Combined Gas Plant and a Combined Cycle Plant Using an Artificial Neural Network Approach

Electrical Energy Prediction of Combined Cycle Power Plant Using Gradient Boosted Generalized Additive Model

Prediction of combined cycle power plant electrical output power using machine learning regression algorithms

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