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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics
Yahyaouy, Ali
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Journal : International Journal of Electrical and Computer Engineering

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A novel wind power prediction model using graph attention networks and bi-directional deep learning long and short term memory Mansoury, Ibtissame; Bourakadi, Dounia El; Yahyaouy, Ali; Boumhidi, Jaouad
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6847-6854

Abstract

Today, integrating wind energy forecasting is an important area of research due to the erratic nature of wind. To achieve this goal, we propose a new model of wind speed prediction based on graph attention networks (GAT), we added a new attention mechanism and a learnable adjacency matrix to the GAT structure to obtain attention scores for each weather variable. The results of the GAT-based model are merged with the bi-directional deep learning long and short-term memory (BiLSTM) layer to take advantage of the geographic and temporal properties of historical weather data. The experiments and analyzes are carried out using precise meteorological data collected from wind farms in the Moroccan city of Tetouan. We show that the proposed model can learn complex input-output correlations of meteorological data more efficiently than previous wind speed prediction algorithms. Due to the resulting attention weights, the model also provides more information about the main weather factors for the evaluated forecast work.
Enhancing hybrid renewable energy performance through deep Q-learning networks improved by fuzzy reward control Ameur, Chahinaze; Faquir, Sanaa; Yahyaouy, Ali; Abdelouahed, Sabri
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp4302-4314

Abstract

In a stand-alone system, the use of renewable energies, load changes, and interruptions to transmission lines can cause voltage drops, impacting its reliability. A way to offset a change in the nature of hybrid renewable energy immediately is to utilize energy storage without needing to turn on other plants. Photovoltaic panels, a wind turbine, and a wallbox unit (responsible for providing the vehicle’s electrical need) are the components of the proposed system; in addition to being a power source, batteries also serve as a storage unit. Taking advantage of deep learning, particularly convolutional neural networks, and this new system will take advantage of recent advances in machine learning. By employing algorithms for deep Q-learning, the agent learns from the data of the various elements of the system to create the optimal policy for enhancing performance. To increase the learning efficiency, the reward function is implemented using a fuzzy Mamdani system. Our proposed experimental results shows that the new system with fuzzy reward using deep Q-learning networks (DQN) keeps the battery and the wallbox unit optimally charged and less discharged. Moreover confirms the economic advantages of the proposed approach performs better approximate to +25% Moreover, it has dynamic response capabilities and is more efficient over the existing optimization approach using deep learning without fuzzy logic.
Co-Authors Abdelouahed, Sabri Ameur, Chahinaze Boumhidi, Jaouad Bourakadi, Dounia El El Bourakadi, Dounia EL Moubtahij, Hicham Faquir, Sanaa Fouadi, Hassan Jiber, Mouna Lamtougui, Hicham Mansoury, Ibtissame Mbarek, Abdelilah Sabri, Abdelouahed Touzani, Halima Drissi