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Bachelor of Science in Renewable Energy Engineering

 

:Bachelor of Science in Renewable Energy Engineering

The Bachelor of Renewable Energy Engineering program is offered by UHA University,
an American university accredited by the International Education Quality Management (IEQM) in the United States of America.
The university has also been listed in the Times Higher Education Impact Rankings 2025.

:Program Philosophy

The Bachelor of Science in Renewable Energy Engineering is not merely a program focused on solar and wind energy; it is a comprehensive engineering program built upon the integration of:

Renewable Energy + Power Systems + Energy Storage + Smart Grids + Artificial Intelligence + Digital Energy

The graduate remains, first and foremost, a renewable energy engineer, while programming and artificial intelligence serve as essential engineering tools for analysis, forecasting, control, optimization, and decision-making.

:Program Duration and Structure

 
 
Component Details
Duration 4 Years
Semesters 8 Semesters
Credit Hours 128 Credit Hours
Average Workload 15–17 Credit Hours per Semester
Industrial Training Mandatory (8–12 weeks, minimum 320 hours)
Graduation Project Two-semester engineering design project

:Definition of the Credit Hour
Definition of Credit Hour (Semester Credit Hours System)

  • 1 Lecture Hour per week throughout the semester ≈ 1 Credit Hour

  • 2–3 Laboratory Hours per week throughout the semester ≈ 1 Credit Hour (depending on course nature)

  • Each course syllabus specifies the theoretical and practical hours separately.

:Bachelor's Program Learning Outcomes

Upon graduation, the student should be able to:

  1. Apply mathematics, physics, and engineering sciences to energy-related problems.

  2. Identify, analyze, and solve complex engineering problems.

  3. Design renewable energy systems while considering technical, economic, and environmental constraints.

  4. Design and analyze solar and wind energy systems.

  5. Analyze electrical power systems and integrate renewable sources.

  6. Select and design energy storage systems.

  7. Apply power electronics and control systems in energy applications.

  8. Use Python, MATLAB, and engineering simulation tools.

  9. Analyze energy data and build predictive models.

  10. Apply machine learning and artificial intelligence techniques to energy applications.

  11. Work with smart grids and microgrids.

  12. Utilize IoT, SCADA, and digital monitoring technologies.

  13. Conduct experiments, analyze results, and draw conclusions.

  14. Assess the technical and economic feasibility of energy projects.

  15. Work effectively in multidisciplinary engineering teams.

  16. Communicate professionally and write technical reports.

  17. Adhere to safety, responsibility, and engineering ethics.

  18. Engage in lifelong learning and keep pace with developments in the energy sector.

Study Plan for the Bachelor's Program
Year 1: Scientific and Engineering Foundations

Semester I – 16 Credit Hours

 
 
Code Course Credits
MATH 101 Engineering Mathematics 1 4
PHYS 101 Engineering Physics 1 + Lab 4
REE 101 Introduction to Energy Engineering and Sustainability 2
PROG 101 Programming for Engineers using Python 3
REE 102 Engineering Drawing and CAD 2
EN 101 Academic and Technical English 1
  Total 16
  • :Engineering Mathematics 1
    This course covers functions, limits, continuity, differentiation, and their engineering applications. It establishes the foundational knowledge required for advanced courses in analysis and mathematical modeling.
  • :Engineering Physics 1
    This course covers mechanics, motion, forces, energy, work, momentum, vibrations, and the fundamental physical concepts used in engineering applications.
  • :Introduction to Sustainable Energy Engineering
    This course introduces students to the global energy system, conventional and renewable energy sources, solar energy, wind energy, hydropower, biomass, energy storage, electrical grids, and sustainability concepts.
  • :Programming for Engineers
    This course covers variables, conditionals, loops, functions, arrays, data processing, graphical plotting, and solving engineering problems using Python. This course is offered in collaboration with the Faculty of Computer Science and Artificial Intelligence, and makes extensive use of the PROG 1 course materials.

Semester II – 17 Credit Hours

 
 
Code Course Credits
MATH 102 Engineering Mathematics 2 4
PHYS 102 Electricity and Magnetism + Lab 4
CHEM 101 Chemistry and Energy Materials + Lab 4
EE 101 Electrical Circuits 1 + Lab 3
REE 103 Renewable Energy Sources and Energy Transition 2
  Total 17
  • Chemistry and Energy Materials
    This course establishes the fundamental principles of chemistry and their direct application to energy engineering. It focuses on the chemical and material science aspects essential for understanding and designing modern energy systems. Key topics include:

  • Batteries: Electrochemical principles, types, and performance characteristics.

  • Photovoltaic Cells: Semiconductor chemistry and material properties for solar energy conversion.

  • Fuel Cells: Electrochemical reactions, membrane technology, and system integration.

  • Hydrogen: Production, storage, and utilization as an energy carrier.

  • Corrosion: Mechanisms, prevention strategies, and material degradation in energy systems.

  • Energy Materials: Properties and selection of materials used in energy generation, conversion, and storage technologies.

Year 2: Core Engineering Sciences

Semester III – 16 Credit Hours

 
 
Code Course Credits
MATH 201 Differential Equations and Linear Algebra 4
STAT 201 Probability, Statistics, and Engineering Data Analysis 3
ME 201 Thermodynamics 3
ME 202 Engineering Mechanics and Energy Materials 3
ME 203 Fluid Mechanics 3
  Total 16
  • :Statistics and Engineering Data Analysis

This course is offered in collaboration with the Faculty of Computer Science and Artificial Intelligence, drawing on the foundational content of STAT 1. However, all applications, examples, and projects are specifically tailored to engineering data and energy systems.

Students learn to apply statistical methods to analyze and interpret real-world engineering data, with a focus on renewable energy and power system applications, including:

  • Solar Radiation: Statistical analysis of solar irradiance data for site assessment and performance prediction.

  • Wind Speed: Probability distributions (e.g., Weibull) and analysis for wind resource assessment.

  • Electrical Loads: Load profiling, demand patterns, and forecasting using statistical tools.

  • Battery Performance: Characterization, degradation analysis, and state estimation.

  • Energy Consumption: Consumption pattern analysis and anomaly detection.

  • Reliability: Statistical evaluation of system reliability and failure rates.

  • Uncertainty Analysis: Quantifying uncertainty in measurements, models, and predictions.

Semester IV – 15 Credit Hours

 
 
Code Course Credits
MATH 202 Numerical Methods and Computational Mathematics for Engineering 3
ME 204 Heat Transfer and Thermal Systems 3
EE 201 Electrical Machines 3
EE 202 Automatic Control Systems 3
EE 203 Power Electronics 3
  Total 15
  • :Numerical Methods

This course introduces students to computational techniques for solving engineering problems using Python and MATLAB. Emphasis is placed on the practical application of numerical methods to energy systems and engineering analysis.

Key topics include:

  • Nonlinear Equations: Solution techniques such as Newton-Raphson and fixed-point iteration.

  • Linear Systems: Direct and iterative methods for solving systems of linear equations.

  • Differential Equations: Numerical integration of ordinary and partial differential equations relevant to energy systems.

  • Numerical Integration: Approximate methods for definite integrals and their engineering applications.

  • Optimization Problems: Linear, nonlinear, and constrained optimization techniques used in system design and operation.

  • Energy Systems Modeling: Applying these numerical tools to model, simulate, and analyze renewable energy systems, power networks, and thermal processes.

Year 3: Renewable Energy Technologies

Semester V – 16 Credit Hours

 
 
Code Course Credits
REE 301 Photovoltaic Solar Energy Engineering 4
REE 302 Solar Thermal Energy and Heat Pumps 3
REE 303 Wind Energy and Turbine Engineering 3
REE 304 Energy Storage and Batteries 3
EE 301 Electrical Power Systems 3
  Total 16
  • Photovoltaic Solar Energy Engineering
    This course provides a comprehensive study of photovoltaic (PV) systems, from fundamental principles to system design and performance analysis.
    Key topics include:

PV Cells and Modules: Structure, operation, and characteristics.

I–V and P–V Curves: Current-voltage and power-voltage characteristics.

Maximum Power Point Tracking (MPPT): Algorithms and techniques for optimal power extraction.

Inverters: Types, operation, and grid integration.

Temperature Effects: Impact of temperature on PV performance.

Shading and Mismatch: Analysis of partial shading, mismatch losses, and bypass diodes.

System Design: Sizing and design of grid-connected and stand-alone PV systems.

Grid-Connected Systems: Integration with utility grids, net metering, and regulatory aspects.

Stand-Alone Systems: Battery sizing, charge controllers, and off-grid applications.

Loss Analysis: Identification and quantification of system losses.

Performance Indicators: Key metrics such as performance ratio, capacity factor, and yield.

PVsyst Software: Practical training in PV system simulation and design using PVsyst.

  • :Wind Energy Engineering

This course covers the engineering principles of wind energy conversion systems, from resource assessment to turbine design and farm planning.

Key topics include:

Wind Resources: Assessment of wind potential, site selection, and data analysis.

Weibull Distribution: Statistical modeling of wind speed data.

Aerodynamics: Principles of airfoil design, lift, drag, and rotor dynamics.

Power Curves: Understanding turbine power output as a function of wind speed.

Turbines: Types, components, and operation of wind turbines.

Generators: Electrical generators used in wind systems (synchronous, induction, and permanent magnet).

Control Systems: Pitch, yaw, and torque control for optimal performance.

Wind Farm Design: Layout optimization, wake effects, and array efficiency.

Wake Effect: Impact of upstream turbines on downstream performance.

Site Assessment: Environmental, technical, and economic evaluation of potential wind farm locations.

  • :Energy Storage and Batteries

This course provides an in-depth understanding of energy storage technologies, with a focus on electrochemical systems and their integration into renewable energy applications.

Key topics include:

Li-ion Batteries: Chemistry, performance, and applications.

Lead-Acid Batteries: Characteristics, maintenance, and use in energy systems.

Flow Batteries: Vanadium redox and other flow battery technologies.

Supercapacitors: Principles, characteristics, and hybrid applications.

Thermal Storage: Sensible, latent, and thermochemical storage systems.

Battery Management Systems (BMS): Monitoring, protection, and balancing.

State of Charge (SOC): Estimation techniques and accuracy.

State of Health (SOH): Degradation analysis, aging mechanisms, and health indicators.

Degradation: Capacity fade, impedance growth, and cycle life.

Safety: Thermal runaway, fire prevention, and safety standards for battery systems.

Semester VI – 16 Credit Hours

 
 
Code Course Credits
EE 302 Smart Grids and Microgrids 3
REE 305 Renewable Energy Integration and Power Quality 3
REE 306 Hydropower, Biomass, and Green Hydrogen 3
REE 307 Energy Efficiency and Building Energy Systems 3
AIREE 301 Machine Learning for Energy Systems 3
REE 308 Renewable Energy Systems Design Laboratory 1
  Total 16
  • Machine Learning for Energy Systems
    This course builds upon the foundational content of ML 1 offered by the Faculty of Computer Science and Artificial Intelligence, with a specific focus on energy sector applications. Students learn to apply machine learning techniques to solve real-world problems in renewable energy, power systems, and energy management.
    Topics Covered:
    Linear Regression .
    Polynomial Regression.
    Decision Trees.
    Random Forest.
    Gradient Boosting.
    XGBoost.
    Neural Networks.
    Clustering.
    Feature Engineering.
    Cross-Validation.
    Model Evaluation.
    Applications in Energy:
    Solar Forecasting.
    Wind Forecasting.
    Load Forecasting.
    Battery SOC Estimation.
    Fault Detection.
    Predictive Maintenance.
    Energy Consumption Prediction

  • :Industrial Training
    The industrial training is a mandatory graduation requirement, undertaken after the completion of Year 3.
    Duration:

8–12 weeks 
Minimum 320 actual training hours
Training Areas:
Solar energy companies
Electricity utilities and grid operators
Power generation plants
Wind farms
Control and automation companies
Industrial facilities
Energy efficiency firms
Engineering consulting offices
Research centers
Key Points:
Mandatory graduation requirement May be registered as professional training without adding credit hours to the 128-credit total

Year 4: Smart Energy Systems and Digital Transformation

Semester VII – 16 Credit Hours

 
 
Code Course Credits
AIREE 401 Artificial Intelligence and Forecasting in Energy Systems 3
REE 401 IoT, SCADA, and Digital Twins for Energy 3
EE 401 Protection and Security of Smart Electrical Grids 3
REE 402 Energy Economics, Markets, Policy, and Sustainability 3
ETH 401 Ethics, Safety, and Engineering Practice 2
GP 401 Graduation Project and Engineering Design 1 2
  Total 16
  • Artificial Intelligence and Forecasting in Energy Systems

This course focuses on advanced AI and machine learning techniques specifically applied to forecasting, optimization, and decision-making in energy systems. Students gain hands-on experience with state-of-the-art algorithms using real energy data.

Topics Covered:

Time-Series Forecasting – Statistical and machine learning approaches for temporal data.

Artificial Neural Networks – Fundamentals of neural computation.

Deep Learning – Advanced architectures for complex pattern recognition.

LSTM (Long Short-Term Memory) – Recurrent neural networks for sequence prediction.

GRU (Gated Recurrent Units) – Efficient recurrent architectures.

Ensemble Learning – Combining multiple models for improved accuracy.

Optimization – Techniques for model tuning and system optimization.

Explainable AI – Interpretability and transparency in AI models.

Uncertainty Quantification – Assessing prediction confidence and risk.

Applications in Energy:

Solar Forecasting – Predicting solar irradiance and PV output.

Wind Forecasting – Short-term and long-term wind power prediction.

Load Forecasting – Predicting electrical demand for grid management.

Electricity Price Forecasting – Modeling market prices for trading and operations.

Battery Degradation – Predicting capacity fade and remaining useful life.

Predictive Maintenance – Forecasting equipment failures and scheduling maintenance.

Fault Diagnosis – Identifying and classifying system faults.

Smart Energy Management – Optimizing energy usage in buildings and microgrids.

  • IoT, SCADA, and Digital Twins for Energy

This course covers the digital infrastructure of modern energy systems, from sensing and data acquisition to remote monitoring and virtual modeling.

Topics Covered:

Sensors – Types, characteristics, and selection for energy applications.

Data Acquisition – Methods and systems for collecting real-time data.

IoT Architecture – Design of Internet of Things systems for energy.

Microcontrollers – Embedded systems for control and monitoring (e.g., Arduino).

Raspberry Pi – Single-board computers for edge computing in energy.

Edge Computing – Processing data closer to the source for real-time applications.

Communication Protocols – Standards for data transmission (e.g., Modbus, MQTT, DNP3).

SCADA – Supervisory Control and Data Acquisition systems for grid management.

Cloud Monitoring – Remote data storage, visualization, and analytics.

Digital Twins – Virtual replicas of physical systems for simulation and analysis.

Condition Monitoring – Continuous assessment of equipment health.

Remote Energy Management – Control and optimization of distributed energy resources.

  • Protection and Security of Smart Electrical Grids

This course integrates power system engineering with cybersecurity to address the unique challenges of protecting modern smart grids.

Topics Covered:

Fault Analysis – Types, causes, and effects of faults in power systems.

Protection Relays – Principles, types, and coordination of protective devices.

Protection Coordination – Ensuring selective and reliable fault clearing.

Smart Meter Security – Securing advanced metering infrastructure.

SCADA Security – Protecting control systems from cyber threats.

Communication Security – Encryption and authentication in grid networks.

Cyber-Physical Energy Systems – Integration of cybersecurity with physical infrastructure.

Semester VIII – 16 Credit Hours

 
 
Code Course Credits
REE 403 Optimization and Operation of Renewable Energy Systems 3
REE 404 Energy Management, Innovation, and Entrepreneurship 3
REE EL1 Elective Course 1 3
REE EL2 Elective Course 2 3
GP 402 Graduation Project and Engineering Design 2 4
  Total 16
  • Course Description – Optimization and Operation of Renewable Energy Systems

This course provides students with the mathematical and computational tools required to optimize the design, operation, and management of renewable energy systems. Emphasis is placed on real-world energy problems involving generation, storage, and grid interaction.

Topics Covered:

Linear Programming – Fundamentals of optimization with linear constraints and objectives.

Mixed-Integer Optimization – Optimization problems involving discrete and continuous decision variables.

Multi-Objective Optimization – Balancing conflicting objectives such as cost, efficiency, and environmental impact.

Optimal Sizing – Determining the optimal capacity of generation and storage components in energy systems.

Energy Management – Strategies for optimizing energy flow in systems with multiple sources and loads.

Economic Dispatch – Minimizing generation costs while meeting demand and operational constraints.

Battery Dispatch – Optimizing charge/discharge cycles for energy storage systems.

Demand Response – Adjusting consumption patterns to improve grid stability and reduce costs.

Microgrid Optimization – Coordinating distributed energy resources within a local grid.

PV-Wind-Battery Hybrid Systems – Integrated design and operation of hybrid renewable energy systems.

Elective Courses (Bachelor's Level)
Energy Technologies Track

  • Advanced Photovoltaic Systems

  • Offshore Wind Energy

  • Advanced Energy Storage

  • Battery Management Systems

  • Green Hydrogen and Fuel Cells

  • Electric Vehicles and Charging Stations

  • Hybrid Energy Systems

  • Concentrated Solar Power

Digital Energy and AI Track

  • Deep Learning for Energy Systems

  • Energy Data Analytics

  • Time-Series Analysis

  • Advanced Digital Twins

  • Computer Vision for Solar and Wind Inspection

  • Edge AI

  • Reinforcement Learning for Energy Management

Power Systems Track

  • Advanced Microgrids

  • Power Quality and Harmonics

  • Advanced Power Electronics

  • HVDC Systems

  • Demand Response

  • Electricity Markets

  • Smart Grid Cybersecurity

Sustainability Track

  • Sustainable Buildings

  • Carbon Accounting

  • Life-Cycle Assessment

  • Energy Economics

  • Climate and Energy Policy

  • Circular Economy for Energy Technologies

Graduation Project

The graduation project is completed over two semesters:

GP 401 – Graduation Project 1 (2 Credits)

  • Problem identification

  • Literature review

  • Specification development

  • Methodology selection

  • Preliminary design

  • Implementation plan

GP 402 – Graduation Project 2 (4 Credits)

  • Implementation

  • Modeling and simulation

  • Design and prototyping

  • Experimentation (when possible)

  • Data analysis and validation

  • Economic analysis

  • Final report

  • Public defense

The project must be a genuine engineering design project, not merely theoretical research.

Example Projects:

  • Smart PV-Battery Microgrid System

  • Digital Twin for a Solar Plant

  • Solar Forecasting using AI

  • Predictive Maintenance for Wind Turbines

  • Optimization of PV-Wind-Battery Systems

  • Solar-Powered Smart Irrigation System

  • Green Hydrogen Production

  • Smart Building Energy Management System

Credit Hour Distribution

 
 
Domain Approximate Credits
Mathematics and Basic Sciences 30
Core Engineering Sciences ~30
Energy Engineering and Power Systems ~38
Programming, Data Analytics, and AI ~12
Economics, Sustainability, Ethics, and Management ~8
Design, Projects, and Electives Remaining up to 128
Total 128

Integration with the Faculty of Computer Science and Artificial Intelligence

The program leverages existing courses from the Faculty of Computer Science and Artificial Intelligence to avoid duplication of faculty resources.

Courses Recommended for Full Integration:

  • MATH 1

  • MATH 2

  • PROG 1

  • EN 1

  • STAT 1

Courses Recommended for Shared Theoretical Foundation (with Energy-Focused Labs/Projects):

  • Artificial Intelligence

  • Machine Learning

  • Data Analytics

  • Time-Series Analysis

  • Internet of Things

  • Cybersecurity

Courses NOT Recommended as Compulsory for Energy Engineering Students:

  • Web Development

  • Natural Language Processing

  • Software Engineering

  • User Interface Design

These may remain as general electives for interested students but should not be mandatory for Renewable Energy Engineering majors.

 

Career Opportunities for Bachelor's Graduates
Solar Energy & PV Systems
Solar Energy Systems Engineer
PV Engineer (Photovoltaic Systems Engineer)
Solar Plant Design Engineer
Wind Energy
Wind Farm Engineer
Wind Turbine Engineer
Wind Resource Analys
Energy Systems & Storage
Energy Engineer
Energy Storage Engineer
Battery Systems Engineer
Thermal Energy Storage Engineer
Smart Grids & Microgrids
Smart Grid Engineer
Microgrids Engineer
Grid Integration Engineer
Renewable Integration & Efficiency
Renewable Energy Integration Engineer
Energy Efficiency Engineer
Power Quality Engineer
Power Electronics & Control
Power Electronics Engineer
Control Systems Engineer
Automation Engineer
Operations & Maintenance
Operations and Maintenance Engineer (O&M)
Plant Performance Engineer
Data & Digital Energy
Energy Data Analyst
Energy Forecasting Engineer
Smart Energy Systems Engineer
AI Applications Engineer for Energy
Management & Consulting
Renewable Energy Project Manager
Energy and Sustainability Consultant
Energy Policy and Regulations Specialist
Research & Development
Energy Technologies Researcher
R&D Engineer in Clean Energy
Innovation Specialist in Energy Systems

What are the registration and certification fees for this program

  • Basic Tuition Fee: $3500 USD, divided into 10 installments over the study period until before the thesis defense Each installment is $350.
  • College Registration Fee and Issuance of Acceptance Letter: $75 (one-time payment upon registration).
  • Discounts:
    A 15% discount is granted upon enrollment at the university when paying tuition fees in installments at the beginning of each semester.
    A 25% discount is granted when paying the full tuition fees in one payment after receiving the admission letter from the university.

Registration and Contact:

 

To register, please fill out the application form or contact us through:

WhatsApp (Student Affairs):
+90 505 556 6619
+1 206 704 7858

Direct contact with the admissions coordinator:
+1 605 846 9439

Website: www.uha.edu.eu
Email: info@uha.edu.eu
Join our WhatsApp channel: Channel Link 

 

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