Predicting the power of a combined cycle power plant using machine learning methods

Alketbi, Salama; Nassif, Ali Bou; Eddin, Maha Alaa; Shahin, Ismail; Elnagar, Ashraf

doi:10.1109/ccci49893.2020.9256742

Cited by 8 publications

(5 citation statements)

References 16 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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“…Thambirajah Ravichandran et al [95] 2020 OLS + MCDM Short-and long-term degradation estimation Salama Alketbi et al [96] 2020 RF Electrical power prediction Maria Grazia De Giorgi and Marco Quarta [99] The performance can be improved by using more sophisticated kernels…”

Section: Comparative Analysis Of ML Modelsmentioning

confidence: 99%

Exploring the Landscape of Gas Turbine Health Monitoring through Machine Learning: A Comprehensive Survey

Yadav

2024

IJSREM

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Within today's fiercely competitive industrial landscape, effective condition monitoring, diagnostics, and prognostics play pivotal roles. The digitization of equipment has exponentially expanded data availability across industrial processes, driving the development of advanced techniques that significantly enhance machine performance. This paper delves into a decade- spanning survey of the evolution of condition monitoring, diagnostics, and prognostics, specifically focusing on machine learning (ML)-based approaches for optimizing gas turbine operational efficiency. Through an exhaustive literature review, this publication evaluates the performance of ML models and their applications in the realm of gas turbines. It also addresses key challenges and opportunities in gas turbine research. Ultimately, the synthesis of data collected from various sources coupled with ML techniques demonstrates promising potential in enhancing the accuracy, robustness, precision, and overall performance of industrial gas turbine systems.

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“…Metode ini memanfaatkan kemampuan algoritma untuk memproses dan memahami pola-pola kompleks dalam data lingkungan serta parameter operasional CCPP. Dengan menganalisis data historis, machine learning dapat membangun model prediktif yang mampu meramalkan kinerja CCPP berdasarkan variasi kondisi lingkungan [7]. Banyak penelitian terdahulu yang menggunakan machine learning dalam memprediksi daya listrik seperti penelitian [8] tentang prediksi daya output listrik pembangkit siklus gabungan berdasarkan faktor lingkungan.…”

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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Predicting the power of a combined cycle power plant using machine learning methods

Cited by 8 publications

References 16 publications

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

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

Exploring the Landscape of Gas Turbine Health Monitoring through Machine Learning: A Comprehensive Survey

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

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