Prediction of Power Output for Combined Cycle Power Plant Using Random Decision Tree Algorithms and ANFIS

Bandić, Lejla; Hasičić, Mehrija; Kevrić, Jasmin

doi:10.1007/978-3-030-24986-1_32

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…sedangkan tekanan atmosfer dapat mempengaruhi kerja turbin gas dan turbin uap. Turbin gas dapat mengalami penurunan efisiensi pada tekanan tinggi, sementara turbin uap membutuhkan tekanan yang cukup untuk menghasilkan energy [3]. Pemahaman yang baik tentang faktor lingkungan dapat membantu dalam menyesuaikan operasional untuk memaksimalkan kinerja kedua jenis turbin.…”

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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Section: Pendahuluanunclassified

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

Hendra

2023

EJI

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“…However, most of the existing statistical prediction models are linear models, rendering it difficult to forecast long-term electricity supply [6]. The performance of a power plant operating at full load can be affected by a variety of factors such as ambient temperature, atmospheric pressure, relative humidity, exhaust steam pressure, and so on [5], which makes it challenging to create a reliable mathematical model for CCPPs. Various techniques have been used to predict power generation, including physical, statistical, and machine learning methods.…”

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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“…The RMSE value obtained with the abovementioned MLP is 4.305, which is considerably less than the MLP provided in the existing literature, but still greater than numerous complicated algorithms, such as K star and tree-based algorithms. Bandić et al [22] described the random forest algorithm for estimating the output power of a CCPP at full load. The analysis is conducted between twofold where in the first fold all the features are utilized, whereas in the second fold only three features are used.…”

Section: Related Workmentioning

confidence: 99%

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

Siddiqui

Anwar

Ullah

et al. 2021

Wireless Communications and Mobile Computing

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Power prediction is important not only for the smooth and economic operation of a combined cycle power plant (CCPP) but also to avoid technical issues such as power outages. In this work, we propose to utilize machine learning algorithms to predict the hourly-based electrical power generated by a CCPP. For this, the generated power is considered a function of four fundamental parameters which are relative humidity, atmospheric pressure, ambient temperature, and exhaust vacuum. The measurements of these parameters and their yielded output power are used to train and test the machine learning models. The dataset for the proposed research is gathered over a period of six years and taken from a standard and publicly available machine learning repository. The utilized machine algorithms are K -nearest neighbors (KNN), gradient-boosted regression tree (GBRT), linear regression (LR), artificial neural network (ANN), and deep neural network (DNN). We report state-of-the-art performance where GBRT outperforms not only the utilized algorithms but also all the previous methods on the given CCPP dataset. It achieves the minimum values of root mean square error (RMSE) of 2.58 and absolute error (AE) of 1.85.

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Prediction of Power Output for Combined Cycle Power Plant Using Random Decision Tree Algorithms and ANFIS

Cited by 5 publications

References 12 publications

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

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

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

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