NOx prediction of gas turbine based on Dual Attention and LSTM

Guo, Lijin; Zhang, Shaojie; Huang, Qilan

doi:10.1109/ccdc55256.2022.10033914

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…The SCADA data of Goldwind's 8MW unit collected by the monitoring control and data acquisition system (SCADA) of the offshore wind farm in a province in the southeast coast of China was taken as the research object.Comprehensively considering the influencing factors of various aspects, the following main indicators were preliminarily selected: average reactive power on the grid side, average wind speed, average absolute wind direction, average instantaneous value of generator speed, cumulative number of yaw actions, average ambient temperature, and average ambient humidity [11,12] .…”

Section: Model Random Forestsmentioning

confidence: 99%

Short-term offshore wind power prediction based on RF-BiLSTM neural network

Sun,

Jia,

Lan

et al. 2024

Ninth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2023)

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In order to improve the operation efficiency of offshore wind turbines, improve the prediction accuracy of short-term offshore wind power value, and meet the future development needs of offshore wind turbines, In this paper, the data of supervisory control and data acquisition (SCADA) system are input into the neural network as test materials to predict the power of offshore wind turbines. To fully reflect the correlation between different components of offshore wind turbines and the coupling between multi-state information of SCADA data.In order to improve the timeliness of power prediction and speed up short-term real-time prediction, this paper adopts random forest (RF) to select features from high-dimensional SCADA data. To simplify the bidirectional-long short erm memory (Bi-LSTM) recurrent neural network structure, accelerate neural network convergence, and improve training speed. The results of a practical offshore wind farm in China show that the proposed prediction method has the highest prediction accuracy. Compared with the other three prediction models, the root-mean-square error of evaluation index, the average absolute error and the average absolute percentage error are 17.60, 17.48 and 0.21%, respectively. The short-term offshore wind power forecasting method proposed in this paper is expected to provide decision-making guidance for the future development and planning layout of offshore wind power operation and maintenance.

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Section: Model Random Forestsmentioning

confidence: 99%

Short-term offshore wind power prediction based on RF-BiLSTM neural network

Sun,

Jia,

Lan

et al. 2024

Ninth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2023)

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“…Guo et al [22] developed a NOx prediction model based on attention mechanisms, LSTM, and LightGBM. The attention mechanisms were introduced into the LSTM model to deal with the sequence length limitation LSTM faces.…”

Section: Machine Learningmentioning

confidence: 99%

Tabular Machine Learning Methods for Predicting Gas Turbine Emissions

Potts

Hackney

Leontidis

2023

MAKE

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Predicting emissions for gas turbines is critical for monitoring harmful pollutants being released into the atmosphere. In this study, we evaluate the performance of machine learning models for predicting emissions for gas turbines. We compared an existing predictive emissions model, a first-principles-based Chemical Kinetics model, against two machine learning models we developed based on the Self-Attention and Intersample Attention Transformer (SAINT) and eXtreme Gradient Boosting (XGBoost), with the aim to demonstrate the improved predictive performance of nitrogen oxides (NOx) and carbon monoxide (CO) using machine learning techniques and determine whether XGBoost or a deep learning model performs the best on a specific real-life gas turbine dataset. Our analysis utilises a Siemens Energy gas turbine test bed tabular dataset to train and validate the machine learning models. Additionally, we explore the trade-off between incorporating more features to enhance the model complexity, and the resulting presence of increased missing values in the dataset.

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