Explore the programs and courses offered by Industrial Networks Engineering and Artificial Intelligence
Browse Programs Admission InformationThe Smart Industrial Systems for Renewable Energy program is an advanced engineering discipline that bridges industrial automation, artificial intelligence (AI), and IoT-driven energy optimization. Designed to meet the demands of Industry 4.0, this program equips
students with the expertise to enhance renewable energy systems through intelligent monitoring, predictive analytics, and autonomous control.
By integrating AI algorithms, industrial IoT networks, and smart grid technologies, the curriculum addresses critical challenges in:
Energy Efficiency: AI-driven load forecasting and dynamic grid management.
System Reliability: Predictive maintenance for wind turbines, solar farms, and storage systems.
Sustainable Automation: Smart city infrastructures, industrial energy hubs, and distributed generation.
Graduates will pioneer innovations in intelligent energy management, contributing to Algeria’s renewable energy transition and global decarbonization efforts.
Networking and Systems
Industrial Wireless Networks
Systems and Network Security
Operating Systems and Computer Architecture
Programming and Development
Object-Oriented Programming
Network Programming and Web Services
N-tier Development & Software Engineering
Embedded and Industrial Systems
Industrial PLCs (Programmable Logic Controllers)
Embedded Systems for Energy Applications
Industrial Supervision and Control
AI and Machine Learning
Predictive Maintenance Algorithms
Big Data Analytics for Energy Systems
IoT and Industrial Communication
IoT Protocols (MQTT, LoRaWAN)
intelligent Robotics for Automation
Parallel Computing Security
Distributed Systems for Energy Grids
Cybersecurity in Industrial Networks
Applied Learning
Capstone Projects: AI-driven energy optimization for solar/wind farms.
Industrial Internship: Hands-on training with energy automation firms.
Specialized Labs:
Microcontroller programming for smart sensors.
Image processing for fault detection in renewables.
For preparatory classes: The minimum baccalaureate average is 14.
For engineering specializations: Admission is through a competitive exam for entry into the second year of higher schools.
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