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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI)
Faquir, Sanaa
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Computer Science & IT Electrical & Electronics Engineering Engineering

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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.
Predicting the severity of road traffic accidents Morocco: a supervised machine learning approach Touzani, Halima Drissi; Faquir, Sanaa; Yahyaouy, Ali
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4461-4473

Abstract

Early prediction of road accidents fatality and injuries severity is one of the important subjects to road safety emphasizing the critical need to prevent serious consequences to reduce injuries and fatalities. This study uses real road accidents data set in Morocco. It represents the intersection between road safety and data science, aiming to employ machine learning techniques to provide valuable insights in accident’s severity prevention. The purpose of this paper is to study road accidents data in the country and combine results from statistical methods, spatial analysis, and machine learning models to determine which factors will mostly contribute to increase the accident’ severity in the country. A comparison of results obtained was also conducted in this paper using different metrics to evaluate the effectiveness of each method and determine the most important factors that contribute to increase the fatality or injuries severity in the specific context of accidents. The best prediction model was then injected into a proposed algorithm where more intelligent techniques are included to be implemented in a car engine to perform an early detection of severe accidents and therefore preventing crashes from happening.
Co-Authors Abdelouahed, Sabri Ameur, Chahinaze Touzani, Halima Drissi Yahyaouy, Ali