Alzheimer Teşhisi için Derin Öğrenme Tabanlı Morfometrik Analiz

ÇELEBİ, Selahattin Barış; EMİROĞLU, Bülent Gürsel

doi:10.21597/jist.1275669

Cited by 2 publications

(1 citation statement)

References 45 publications

(2 reference statements)

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“…DL, a subset of machine learning, is a relatively new technique developed to overcome the shortcomings of shallow learning models [38,39] DL-based methods have been successfully applied to classification [40] and prediction problems [41]. LSTM, a variant of RNN, can learn time-series information more accurately.…”

Section: Related Workmentioning

confidence: 99%

Multilayer LSTM Model for Wind Power Estimation in the Scada System

ÇELEBİ,

KARAMAN

2023

EJT

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Wind energy is clean energy that does not pollute the environment. However, the complex and variable operating environment of a wind turbine often makes it difficult to predict the instantaneous active power generated. In this study, a wind turbine active power estimation system based on a short-term memory network (LSTM) using time series analysis is proposed. The data obtained from the wind turbine SCADA system is used as input variables. In the proposed method, a multilayer LSTM architecture is designed to train the model. The first LSTM network consists of 64 units, and the second one consists of 32 units. This is followed by a dense layer consisting of 16 neurons. In the last layer, the architecture is finalized by using a linear activation function for the prediction process. The proposed deep learning (DL)-based LSTM prediction model takes into account environmental factors such as wind speed and wind direction for active power forecasting. The results show that the LSTM-based time series analysis method is capable of effectively capturing time series features among the data. Thus, the proposed architecture can realize high-accuracy active power forecasting.

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Section: Related Workmentioning

confidence: 99%

Multilayer LSTM Model for Wind Power Estimation in the Scada System

ÇELEBİ,

KARAMAN

2023

EJT

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RNN-Based Time Series Analysis for Wind Turbine Energy Forecasting

ÇELEBİ,

FİDAN

2024

International Journal of Engineering and Innovative Research

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One significant source of renewable energy is wind power, which has the potential to generate sustainable energy. However, wind turbines have many challenges, such as high initial investment costs, the dynamic nature of wind speed, and the challenge of locating wind-efficient energy regions. Wind power predicting is crucial for effective planning of wind power generation, optimization of power generation, grid integration, and security of supply. Therefore, highly accurate forecasts ensure the efficient and sustainable operation of the wind energy sector and contribute to energy security, economic stability, and environmental sustainability. This study proposes a deep learning (DL) approach based on recurrent neural networks (RNNs) for long-term wind power forecasting utilizing climatic data. The input data that forms the basis of this study is obtained directly from a wind turbine system operating under real-world conditions. The proposed model in this study is based on a multilayer back-propagation neural network (RNN) architecture specifically designed to effectively handle complex data sets and time-dependent series. The architecture of the model is built on an RNN consisting of four separate layers, each with 50 hidden neurons, carefully structured to increase its capacity to capture complex features. To improve the robustness of the model and avoid overlearning, each RNN layer is followed by a dropout (regularizing) layer that randomly deactivates 20% of the neurons to enhance the generalization ability of the network. To finalize the prediction capability of the model, a linear function was chosen in the last layer to directly match the actual values. Evaluating the model performance metrics, the proposed architecture achieved a prediction accuracy of 91% R2 on the test dataset. The findings indicate that proposed method based on multilayer RNN can successfully capture the relationships between the sequential data of the wind turbine.

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Alzheimer Teşhisi için Derin Öğrenme Tabanlı Morfometrik Analiz

Cited by 2 publications

References 45 publications

Multilayer LSTM Model for Wind Power Estimation in the Scada System

Multilayer LSTM Model for Wind Power Estimation in the Scada System

RNN-Based Time Series Analysis for Wind Turbine Energy Forecasting

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