Two solar arrays that look identical on paper can produce noticeably different amounts of energy over a year, and the gap usually traces back to how carefully their performance was modelled before a single panel went up. Getting the model right is what separates a forecast investors can trust from one that quietly falls apart in year three.

This Photovoltaic Performance Modelling and Yield Forecasting Training Course by Arab British Fellowship Training Academy takes participants through the tools, mathematics and software used to predict, simulate and optimise how a PV system will actually perform once it is in the ground.

Who Should Attend?

• Engineers and technicians involved in designing, installing and maintaining PV systems.
• Energy analysts and consultants who assess the viability and performance of solar projects.
• Researchers and students studying renewable energy technologies who want hands-on modelling skills.

Knowledge and Benefits:

After completing the program, participants will be able to:

• Explain the fundamentals of photovoltaic technology and how energy conversion works.
• Apply different modelling techniques to predict solar energy yield accurately.
• Identify the factors that most affect PV performance and where optimisation is possible.
• Use simulation software to model, interpret and act on PV system performance data.

Module 1: The Basics — Solar Cells, Modules and Energy Conversion

• Why photovoltaic systems matter in the broader push to cut carbon emissions.
• Monocrystalline, polycrystalline and thin-film cells, and how panels are built.
• The photovoltaic effect and the role semiconductor materials play in it.

Module 2: Static or Dynamic? Choosing a Modelling Approach

• The difference between static and dynamic performance models, and when to use each.
• Single-diode and double-diode mathematical models for predicting yield.
• Reading the I-V curve and what it reveals about system behaviour.

Module 3: Simulation Software — PVsyst, SAM and Beyond

• Popular modelling platforms and what each is best suited for.
• Matching software functionality to a project's specific modelling needs.
• Practical considerations when choosing a simulation tool.

Module 4: Environmental Variables That Drive Performance

• How temperature affects PV efficiency, and why temperature coefficients matter.
• Solar irradiance measurement techniques and seasonal or geographic variation.
• The angle of incidence, tilt optimisation and the role of shading analysis.

Module 5: From Site Data to Array Layout

• Conducting site assessments that feed directly into a performance model.
• Fixed versus tracking array configurations and their effect on output.
• Selecting inverters and batteries for compatibility and overall efficiency.

Module 6: Running the Simulation and Reading the Results

• Methodologies for simulating PV system performance accurately.
• The weight input parameters carry, and how to validate a simulation.
• Key performance indicators and common mistakes in interpreting output.

Module 7: Stress-Testing the Model — Sensitivity Analysis

• Techniques for testing how sensitive a model is to changes in input variables.
• Identifying which parameters actually drive uncertainty in the forecast.
• Using sensitivity results to sharpen, not just double-check, a design.

Module 8: Why Continuous Monitoring Matters

• How ongoing performance monitoring catches issues a one-off model cannot.
• Components and functions of a data acquisition system.
• Using data visualisation to support faster, better-informed decisions.

Module 9: Maintenance Strategies Informed by Data

• Building a maintenance schedule around real monitoring data, not fixed intervals.
• Why cleaning and inspection routines directly affect modelled performance.
• Closing the loop between predicted and actual system output.

Module 10: AI and Stochastic Methods in Modern Modelling

• How artificial intelligence and machine learning are being used to refine yield predictions.
• Monte Carlo simulations and other stochastic methods for handling uncertainty.
• Incorporating variability in resource availability for more realistic forecasts.

Module 11: Modelling Hybrid Systems

• The added complexity of modelling solar alongside storage or other sources.
• Benefits and challenges specific to hybrid system performance analysis.
• Where hybrid modelling diverges from standalone PV modelling.

Module 12: Turning Models into Money — Cost-Benefit and Incentives

• Applying cost-benefit analysis to a modelled PV project.
• Tax credits, rebates and financing options such as power purchase agreements and loans.
• Building financial models that connect performance forecasts to investment decisions.

Module 13: LCOE and the Metrics That Matter to Investors

• What levelized cost of energy actually measures, and why investors ask for it first.
• Other financial performance metrics relevant to PV project evaluation.
• Using performance modelling and system design choices to optimise LCOE.