A Real-Time Charge Predictive Model for Intelligent Networks

Authors

  • Monia Bartouli Laboratory of Micro-Optoelectronics and Nanostructure, University of Monastir, Monastir, Tunisia
  • Amina Msolli Laboratory of Micro-Optoelectronics and Nanostructure, University of Monastir, Monastir, Tunisia
  • Abdelhamid Helali Laboratory of Micro-Optoelectronics and Nanostructure, University of Monastir, Monastir, Tunisia
  • Hassen Fredj Laboratory of Micro-Optoelectronics and Nanostructure, University of Monastir, Monastir, Tunisia
Volume: 14 | Issue: 5 | Pages: 17091-17098 | October 2024 | https://doi.org/10.48084/etasr.7845

Abstract

A smart grid is a modern electrical system that uses information technology, including sensors, measurement tools and communication devices, to monitor and improve the efficiency of the power system. However, real-time forecasting remains a challenge due to its complexity.  This paper presents a forecasting framework that combines Convolutional Neural Networks (CNN) and Bidirectional Long-Short Term Memory (BiLSTM) for real-time load forecasting in smart grids. Compared to traditional methods like ARMA and Decision Trees (DTs), the proposed CNN-BiLSTM model demonstrates superior performance in terms of prediction accuracy, reaching up to 99% - higher than Long-Short Term Memory (LSTM) (93%) and Support Vector Machine (SVM) (84%). Additionally, the CNN-BiLSTM model requires fewer computational resources, with 90 Gigaflops (G) and 94 Million (M) parameters, compared to 151 (G) and 120 (G) for ARIMA and CNN-LSTM, respectively. These results indicate the proposed model's ability to accurately predict power system loads in real time with high computational efficiency.

Keywords:

smart grid, Bayesian optimization, load forecasting, BiLSTM, CNN

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How to Cite

[1]
Bartouli, M., Msolli, A., Helali, A. and Fredj, H. 2024. A Real-Time Charge Predictive Model for Intelligent Networks. Engineering, Technology & Applied Science Research. 14, 5 (Oct. 2024), 17091–17098. DOI:https://doi.org/10.48084/etasr.7845.

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