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صفحه اصلی
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دهمین کنفرانس بین المللی فناوری و مدیریت انرژی
Data-Driven Model for Predicting Power Generation in Integrated Turbine Units
نویسندگان :
Mohammad Mahdi Avazpour
1
Hosein Mohammadi
2
1- دانشگاه شیراز
2- دانشگاه شیراز
کلمات کلیدی :
Data-driven approach،Energy optimization،Hybrid deep learning،Machine learning،LSTM،CNN
چکیده :
Given the increasing demand for energy and the challenges posed by climate change, the depletion of fossil fuel resources, and rising energy demand, optimizing power generation has become a critical priority in the energy industry. Climate change, excessive consumption, and inadequate infrastructure exacerbate energy imbalances. To address these challenges, this paper presents a data-driven approach to predicting power output. We evaluate and compare various state-of-the-art data-driven forecasting models to identify the most reliable approach for both short-term and long-term predictions. In this study, we propose a hybrid deep learning model combining Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN). This model excels at capturing time dependencies and nonlinear, complex patterns in the data. The results show that the hybrid LSTM-CNN model outperforms other machine learning approaches, including single-layer LSTM and Random Forest, with prediction accuracy up to 98%. This high level of accuracy makes the proposed model a reliable and effective solution for predicting power generation in combined-cycle power plants.
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بیشتر
ثمین همایش، سامانه مدیریت کنفرانس ها و جشنواره ها - نگارش 43.7.1
