Key Takeaways
- Predicts annual, monthly, and hourly energy output for a specific solar system design
- Uses Typical Meteorological Year (TMY) data or satellite-derived irradiance for the project location
- Accounts for all real-world losses: shading, temperature, soiling, wiring, and inverter efficiency
- Output feeds directly into financial models for ROI, payback, and cash flow projections
- Accuracy depends on input quality — especially irradiance data and shading analysis
- Modern solar software runs simulations in seconds, enabling rapid design iteration
What Is Performance Simulation?
Performance simulation is the process of computationally modeling how much electricity a solar PV system will produce over a given period. It takes a proposed system design — including panel layout, equipment specifications, and site conditions — and runs it through physics-based models to predict real-world energy output.
Every professional solar proposal relies on a performance simulation. Without it, energy production estimates are guesses. With it, designers can quantify expected output to within 3–7% accuracy on an annual basis, giving customers and financiers the confidence they need to proceed.
Performance simulation is what transforms a solar design into a bankable energy projection. It’s the bridge between “this is what we’ll install” and “this is what it will produce and save.”
How Performance Simulation Works
A performance simulation follows a structured pipeline, processing raw environmental data through a series of models to produce actionable energy projections.
Site Location & Weather Data
The simulation loads irradiance, temperature, and wind data for the project’s geographic coordinates. Most tools use TMY datasets or satellite-derived hourly data spanning 10–20 years of historical weather.
System Configuration
The designer inputs array layout — tilt, azimuth, module type, inverter model, string configuration, and DC/AC ratio. Each parameter affects how much energy the system captures and converts.
Irradiance Transposition
Horizontal irradiance data is converted to plane-of-array (POA) irradiance using transposition models (Perez, Hay-Davies). This accounts for the array’s specific tilt and orientation.
Shading Analysis Integration
3D shading models or horizon profiles are applied to reduce POA irradiance at each timestep based on obstructions. This is often the most significant site-specific loss factor. Accurate shadow analysis is critical at this stage.
DC Energy Calculation
Module-level DC output is calculated using the panel’s electrical characteristics (from PAN files), adjusted for cell temperature and irradiance level at each timestep.
Loss Factor Application
Systematic losses are applied sequentially: soiling, mismatch, wiring resistance, module degradation, and any other site-specific derates.
DC-to-AC Conversion
The inverter efficiency curve converts DC power to AC output, including clipping losses when DC input exceeds the inverter’s AC rating.
Output Aggregation
Hourly AC output is aggregated into monthly and annual totals. Results feed into financial models for savings calculations, payback analysis, and customer proposals.
Annual Output (kWh) = System Size (kWp) × Peak Sun Hours × Performance RatioKey Simulation Inputs
The accuracy of a performance simulation depends on the quality and specificity of its inputs.
| Input Category | Key Parameters | Impact Level |
|---|---|---|
| Weather Data | GHI, DNI, temperature, wind speed | High — drives total available energy |
| Array Configuration | Tilt, azimuth, row spacing | High — determines energy capture |
| Module Specs | Efficiency, temperature coefficients, PAN file | High — defines conversion capability |
| Inverter Specs | Efficiency curve, AC rating, MPPT range | Medium — affects DC-to-AC conversion |
| Shading Profile | 3D obstructions, horizon line | High — site-specific production loss |
| Loss Assumptions | Soiling, mismatch, wiring, degradation | Medium — cumulative effect on output |
Types of Performance Simulations
Annual Energy Yield
The most common simulation type. Processes 8,760 hourly timesteps using TMY data to produce annual and monthly kWh projections. Used in most residential and commercial proposals.
Hourly Production Profile
Provides hour-by-hour output curves for load matching, TOU rate optimization, and battery sizing. Shows when production peaks relative to consumption patterns.
25-Year Degradation Model
Projects output decline over the system’s lifetime using annual degradation rates. Feeds into long-term financial models, NPV calculations, and warranty evaluations.
Probabilistic (P50/P75/P90)
Runs simulations across multiple weather years to produce exceedance probability estimates. P90 output is required for project financing and utility-scale bankability assessments.
For residential projects, a standard annual simulation with TMY data is sufficient. For commercial projects with performance guarantees, run temperature-corrected simulations. For financed utility-scale projects, probabilistic simulations with P90 estimates are the industry standard.
Practical Guidance
Performance simulation affects every stakeholder in the solar project lifecycle.
- Run simulations before finalizing layout. Use iterative simulation to compare design alternatives — different tilt angles, azimuth orientations, or string configurations can yield 5–15% production differences.
- Validate shading inputs carefully. Shading is the largest source of simulation error. Use accurate 3D models and shadow analysis tools rather than rough estimates.
- Use manufacturer PAN/OND files. Generic module and inverter files introduce systematic errors. Always import the specific equipment files for the products being installed.
- Document all assumptions. Record weather data source, loss factors, and degradation rates. This makes the simulation auditable and defensible if production questions arise later.
- Compare actual production to simulation. After commissioning, monitor real output against the simulation. Deviations beyond 10% in year one warrant investigation.
- Report field condition changes. If installed tilt, azimuth, or shading conditions differ from design assumptions, flag the changes so the simulation can be updated.
- Use simulation data for troubleshooting. When a system underperforms, compare monthly actual vs. simulated output to isolate whether the issue is weather-related or equipment-related.
- Understand degradation assumptions. Know the annual degradation rate used in the simulation so you can set realistic long-term expectations with the customer.
- Present simulation results visually. Monthly production bar charts are more persuasive than raw numbers. Show seasonal variation so customers understand winter vs. summer differences.
- Explain the methodology briefly. Customers trust projections more when they know the simulation uses real weather data and actual equipment specs, not assumptions.
- Use conservative estimates. Present P75 or median estimates in proposals. Overpromising production damages trust and leads to customer complaints.
- Connect production to savings. Translate kWh numbers into dollar savings using local utility rates. SurgePV’s generation and financial tool does this automatically.
Run Performance Simulations in Seconds
SurgePV combines solar design software with integrated performance simulation — generate bankable energy projections without switching tools.
Start Free TrialNo credit card required
Common Simulation Errors and How to Avoid Them
| Error | Typical Impact | How to Avoid |
|---|---|---|
| Using wrong weather data source | 5–15% over/underestimate | Use TMY data from the nearest reliable station or satellite data for the exact coordinates |
| Ignoring near-shading | 5–25% overestimate | Model all obstructions within 50m using 3D tools |
| Generic equipment files | 3–8% error | Import actual PAN/OND files from the manufacturer |
| Unrealistic soiling assumption | 2–5% error | Use regional soiling data, not zero-loss defaults |
| Ignoring inverter clipping | 1–5% overestimate | Model the actual DC/AC ratio and inverter efficiency curve |
| No degradation applied | Cumulative 10–15% over 25 years | Apply 0.4–0.7% annual degradation per module warranty |
When a simulation result seems too good to be true, it probably is. Back-calculate the implied performance ratio — if it exceeds 90% for a residential rooftop system, at least one loss factor is probably underestimated.
Frequently Asked Questions
What is performance simulation in solar design?
Performance simulation is the computational process of predicting how much electricity a solar PV system will produce over time. It uses site-specific weather data, equipment specifications, array layout, and loss factors to generate hourly, monthly, and annual energy yield projections. These projections form the basis of financial analyses and customer proposals.
How accurate are solar performance simulations?
Well-configured simulations using site-specific weather data and accurate equipment specifications typically predict annual production within 3–7% of actual output. Monthly accuracy can vary more due to weather variability. The biggest sources of error are usually shading estimates and weather data quality rather than the simulation engine itself.
What data is needed to run a solar performance simulation?
At minimum, you need the project’s geographic coordinates, hourly weather data (irradiance and temperature), module and inverter specifications, array tilt and azimuth, and loss factor assumptions (soiling, shading, wiring). For higher accuracy, add 3D shading profiles, manufacturer PAN/OND equipment files, and consumption data for self-consumption analysis.
What is the difference between P50 and P90 in solar simulations?
P50 is the median estimate — there’s a 50% chance actual production will meet or exceed it. P90 is a conservative estimate with 90% confidence of being met or exceeded. Lenders and investors typically require P90 estimates because they need high confidence in minimum revenue. Residential proposals usually use P50 or slightly conservative estimates.
About the Contributors
Content Head · SurgePV
Rainer Neumann is Content Head at SurgePV and a solar PV engineer with 10+ years of experience designing commercial and utility-scale systems across Europe and MENA. He has delivered 500+ installations, tested 15+ solar design software platforms firsthand, and specialises in shading analysis, string sizing, and international electrical code compliance.
CEO & Co-Founder · SurgePV
Keyur Rakholiya is CEO & Co-Founder of SurgePV and Founder of Heaven Green Energy Limited, where he has delivered over 1 GW of solar projects across commercial, utility, and rooftop sectors in India. With 10+ years in the solar industry, he has managed 800+ project deliveries, evaluated 20+ solar design platforms firsthand, and led engineering teams of 50+ people.