A Real-Time Charge Predictive Model for Intelligent Networks
Received: 15 May 2024 | Revised: 3 June 2024 | Accepted: 12 June 2024 | Online: 9 October 2024
Corresponding author: Monia Bartouli
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, CNNDownloads
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Copyright (c) 2024 Monia Bartouli, Amina Msolli, Abdelhamid Helali, Hassen Fredj
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