Power Prediction of Combined Cycle Power Plant (CCPP) Using Machine Learning Algorithm‐Based Paradigm

Siddiqui, Raheel; Anwar, Hafeez; Ullah, Farman; Ullah, Rehmat; Rehman, Muhammad Abdul; Jan, Naveed; Zaman, Fawad

doi:10.1155/2021/9966395

Cited by 13 publications

(6 citation statements)

References 32 publications

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“…Using the recursive k-means tool ( Miniak-Gorecka, Podlaski & Gwizdalla, submitted ), we divide the response set (EP) into three classes. There are many references to the classification issue related to these data in the literature i.e., ( Saleel, 2021 ; Santarisi & Faouri, 2021 ; Alketbi et al, 2020 ; Siidiqui et al, 2021 ).…”

Section: Experiments and Resultsmentioning

confidence: 99%

Self-optimizing neural network in the classification of real valued data

Miniak-Górecka

Podlaski

Gwizdałła

2022

PeerJ Computer Science

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The classification of multi-dimensional patterns is one of the most popular and often most challenging problems of machine learning. That is why some new approaches are being tried, expected to improve existing ones. The article proposes a new technique based on the decision network called self-optimizing neural networks (SONN). The proposed approach works on discretized data. Using a special procedure, we assign a feature vector to each element of the real-valued dataset. Later the feature vectors are analyzed, and decision patterns are created using so-called discriminants. We focus on how these discriminants are used and influence the final classifier prediction. Moreover, we also discuss the influence of the neighborhood topology. In the article, we use three different datasets with different properties. All results obtained by derived methods are compared with those obtained with the well-known support vector machine (SVM) approach. The results prove that the proposed solutions give better results than SVM. We can see that the information obtained from a training set is better generalized, and the final accuracy of the classifier is higher.

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Section: Experiments and Resultsmentioning

confidence: 99%

Self-optimizing neural network in the classification of real valued data

Miniak-Górecka

Podlaski

Gwizdałła

2022

PeerJ Computer Science

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“…Pembangkit Listrik Tenaga Siklus Gabungan (Combined Cycle Power Plant atau CCPP) merupakan pembangkit listrik yang menggunakan kombinasi turbin gas (GT) dan turbin uap (ST) dalam satu siklus untuk menghasilkan listrik. Konsep ini dirancang untuk meningkatkan efisiensi penggunaan bahan bakar dan mengoptimalkan pemanfaatan panas yang dihasilkan dalam proses pembangkit listrik [1]. CCPP merupakan teknologi yang terus berkembang dalam upaya meningkatkan efisiensi konversi energi dari bahan bakar menjadi listrik.…”

Section: Pendahuluanunclassified

Prediksi Daya Listrik Pada Pembangkit Listrik Siklus Gabungan Berdasarkan Kondisi Lingkungan Menggunakan Metode Machine Learning

Hendra

2023

EJI

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The utilization of machine learning methods in energy simulation enables the optimization of energy use and improves energy efficiency. In this research, the modeling of predicting power output was conducted under full load conditions in a Combined Cycle Power Plant (CCPP) based on the surrounding environmental conditions. Historical data of CCPP operation were used to model and predict power output under various environmental conditions. In this study, four machine learning algorithms, namely Linear Regression (LR), Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN), were compared and evaluated for their performance. The evaluation metrics used to measure the model performance were Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-Squared. The research results indicate that the Random Forest (RF) model achieved the best performance compared to other models with MAE of 2.314, RMSE of 3.372, and R-squared of 0.961. Additionally, the RF model also performed the best compared to other models in external testing with new data, where RF obtained values of MAE 2.579, RMSE 3.315, and R-squared 0.957. These results are consistent with the previous testing, indicating that RF has stable and reliable performance in predicting larger and more diverse datasets. This research contributes to understanding the potential application of machine learning in the power generation industry, especially in CCPP.

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“…According to [20,22], K nearest neighbors (K-NN), Linear Regression, and RANSAC regressions can achieve better performance than Simple Linear Regression, Bayesian Linear Regression, Decision Tree, and Gaussian Naïve Bayesian Regression algorithms based on the CCPP dataset. K-NN is a type of instance-based learning where new data points are classified or predicted based on their similarity to the training data.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting Power Generation from a Combined Cycle Power Plant Using Transformer Encoders with DNN

2023

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With the development of the Smart Grid, accurate prediction of power generation is becoming an increasingly crucial task. The primary goal of this research is to create an efficient and reliable forecasting model to estimate the full-load power generation of a combined-cycle power plant (CCPP). The dataset used in this research is a subset of the publicly available UCI Machine Learning Repository. It contains 9568 items of data collected from a CCPP during its full load operation over a span of six years. To enhance the accuracy of power generation forecasting, a novel forecasting method based on Transformer encoders with deep neural networks (DNN) was proposed. The proposed model exploits the ability of the Transformer encoder to extract valuable information. Furthermore, bottleneck DNN blocks and residual connections are used in the DNN component. In this study, a series of experiments were conducted, and the performance of the proposed model was evaluated against other state-of-the-art machine learning models based on the CCPP dataset. The experimental results illustrated that using Transformer encoders along with DNN can considerably improve the accuracy of predicting CCPPs power generation (RMSE = 3.5370, MAE = 2.4033, MAPE = 0.5307%, and R2 = 0.9555).

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Power Prediction of Combined Cycle Power Plant (CCPP) Using Machine Learning Algorithm‐Based Paradigm

Cited by 13 publications

References 32 publications

Self-optimizing neural network in the classification of real valued data

Self-optimizing neural network in the classification of real valued data

Prediksi Daya Listrik Pada Pembangkit Listrik Siklus Gabungan Berdasarkan Kondisi Lingkungan Menggunakan Metode Machine Learning

Predicting Power Generation from a Combined Cycle Power Plant Using Transformer Encoders with DNN

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