Energy Forecasting
Energy forecasting is the process of predicting how much electricity a solar PV system will generate over a specific time period—hourly, daily, monthly, yearly, or over the lifetime of the system. It combines weather data, irradiance analysis, system design parameters, shading conditions, and equipment performance characteristics to estimate expected solar energy output.
In solar design, energy forecasting is one of the most essential engineering tasks. It determines system size, financial returns, payback periods, utility interaction, and customer savings.
Modern platforms like Solar Designing and shading engines such as Shadow Analysis rely heavily on forecasting models to create accurate production estimates for residential, commercial, and utility-scale projects.
Energy forecasting directly influences proposals, system sizing, utility interconnection, financial modeling, and long-term operational planning.
Key Takeaways
- Energy forecasting predicts how much electricity a solar system will produce using irradiance, shading, temperature, and system characteristics.
- It drives financial modeling, proposal accuracy, and system sizing.
- Forecasting requires accurate weather data, shading analysis, POA irradiance, and loss modeling.
- Modern solar design platforms automate forecasting to improve speed and reliability.
- Accurate forecasts improve customer trust, ROI accuracy, and long-term system performance.
What Is Energy Forecasting?
Energy forecasting is the analytical process used to estimate how much power a solar PV system will generate based on environmental inputs and system characteristics. It incorporates variables such as:
- Solar irradiance
- Panel efficiency
- Tilt & azimuth
- Shading patterns
- Temperature
- System losses
- DC/AC ratio
- Inverter efficiency
- Weather patterns
- Soiling and degradation
The objective is to produce an accurate prediction of kilowatt-hour (kWh) generation, which is foundational for performance modeling, financing, and customer ROI calculations.
Related concepts include Energy Production Forecasting, POA Irradiance, and Performance Ratio.
How Energy Forecasting Works
1. Collect Irradiance & Weather Data
Forecasting begins with historical and typical meteorological year (TMY) data for:
- Global Horizontal Irradiance (GHI)
- Direct Normal Irradiance (DNI)
- Diffuse Horizontal Irradiance (DHI)
- Temperature
- Albedo
- Weather patterns
These drive POA irradiance calculations and yield estimation.
2. Analyze Shading Conditions
Tools such as Shadow Analysis help identify shading losses from trees, rooftops, and nearby structures.
3. Convert Irradiance to Energy Production
Models compute the plane-of-array (POA) irradiance and convert it to DC energy based on:
- Module type
- Efficiency
- Temperature behavior
- MPP voltage-current curves
4. Apply System Losses
Energy forecasts account for:
- Inverter efficiency
- Temperature losses
- Soiling losses
- Mismatch losses
- Wiring losses
- Snow, dust, and availability losses
5. Convert DC → AC Output
Inverters translate DC generation to usable AC power.
6. Generate Production Profiles
Forecasting models output:
- Hourly profiles
- Monthly generation
- Annual energy
- Performance ratio (PR)
- Expected degradation
These results feed directly into proposal tools, like the Solar Proposal & Sales Hub.
Types / Variants of Energy Forecasting
1. Short-Term Forecasting
Predicts hourly or daily output—used for grid operations and energy storage management.
2. Long-Term Annual Forecasting
Common in residential and commercial proposals; estimates total kWh over one year.
3. Lifetime Forecasting
Calculates production over 25–30 years, accounting for degradation rates.
4. Weather-Normalized Forecasting
Uses TMY or average climate data to produce stable long-term predictions.
5. High-Resolution Forecasting (C&I + Utility)
Incorporates minute-level irradiance data, terrain modeling, and bifacial gain.
How Energy Forecasting Is Measured
Energy forecasting relies on multiple engineering metrics:
Annual Energy Yield (kWh/year)
Total expected yearly production.
Specific Yield (kWh/kWp)
Normalizes performance relative to system size.
Performance Ratio (PR)
Efficiency of the system after all losses.
POA Irradiance
Effective solar irradiance hitting the panel surface.
Capacity Factor
Percentage of actual output vs theoretical maximum.
These metrics help evaluate system performance, quality, and financial value.
Typical Values / Ranges
Values depend on:
- Latitude
- Weather
- Shading
- Module type
- Tilt & orientation
Practical Guidance for Solar Designers & Installers
1. Use accurate shading analysis
Combine irradiance data with Shadow Analysis to avoid performance surprises.
2. Validate weather and irradiance data
Use TMY or reliable weather databases to avoid inflated projections.
3. Apply conservative system losses
Industry standard assumes 10–18% total system losses.
4. Include degradation in long-term forecasts
Monocrystalline panels degrade ~0.4–0.7% per year.
5. Ensure inverter sizing matches generation
Check the DC/AC ratio — see Inverter Sizing.
6. Use forecasting in customer proposals
Forecasts drive ROI, payback, and financial modeling using tools like:
7. Validate against real-world installs
Comparing forecasted vs actual output helps refine models.
Real-World Examples
1. Residential Solar Forecast
A 6.5 kW rooftop system in a sunny region forecasts 9,800 kWh/year after accounting for shading and system losses.
2. Commercial Forecast (Flat Roof)
A 250 kW system uses TMY data and advanced shading simulation to forecast 345,000 kWh/year with a PR of 82%.
3. Utility-Scale Plant Forecast
A 20 MW solar farm uses terrain-based irradiance modeling and bifacial gain analysis to forecast 34 GWh/year.
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