Energy consumption prediction in smart factories using data-driven deep learning models
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deep learning
energy consumption prediction
data-driven
smart manufacturing
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Accurate energy consumption prediction plays a critical role in improving efficiency and sustainability in smart factories. This study develops a data-driven framework based on deep learning (DL) models for short-term energy consumption forecasting using multivariate time-series sensor data. Three DL models are employed: Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Deep Recurrent Neural Networks (DRNNs). To enhance model performance, Genetic Algorithm (GA) is integrated to optimize key hyperparameters related to temporal dependency, network configuration, and training strategy. Experimental results on a real-world smart manufacturing dataset demonstrate that GA-based DL models consistently outperform their non-optimized counterparts. GA-optimized models achieve an average reduction of approximately 8–15% in Mean Absolute Percentage Error (MAPE) on the test set, with the GA-CNN model demonstrating the best overall performance. These results confirm the effectiveness of combining evolutionary optimization with DL for robust and accurate energy consumption prediction in smart factory environments.
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Mar 31, 2026
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Cách trích dẫn
Nguyen, T. P. Q., T. M. H. Nguyen, T. C. Nguyen, và N. P. T. Nguyen. “Energy Consumption Prediction in Smart Factories Using Data-Driven Deep Learning Models”. Tạp Chí Khoa học Và Công nghệ Đại học Đà Nẵng, vol 24, số p.h 3, Tháng Ba 2026, tr 25-30, doi:10.31130/ud-jst.2026.24(3).087E.
