Solar Irradiance: GHI, DNI, DHI Guide for Designers

Every kilowatt-hour your solar system produces starts with one number: site irradiance. Get it right, and yield predictions land within 2-3% of measured output. Get it wrong by 10%, and a 500 kWp commercial rooftop misestimates 50,000-75,000 kWh per year.

Irradiance remains one of the most misunderstood inputs in solar design. Designers pull a single GHI number from a free database, skip the decomposition step, and ignore the difference between horizontal irradiance and what actually hits a tilted array. Each shortcut introduces error that compounds through the entire energy yield calculation.

This guide covers every layer that matters: GHI, DNI, and DHI physics, transposition models, major data source comparisons, and how irradiance uncertainty flows through to P50/P90 bankability analysis.

What you will learn:

• The four types of solar irradiance (GHI, DNI, DHI, POA) and how they relate mathematically
• How to calculate irradiance on a tilted surface using transposition models
• A worked example converting GHI data for a 30-degree array in Munich
• Side-by-side comparison of PVGIS, Meteonorm, SolarAnywhere, Solargis, and NASA POWER
• Reference irradiance data for 20 European cities with expected yields
• How irradiance uncertainty affects P50/P90 analysis and project bankability
• How solar design software processes irradiance data from input to energy yield

Chapter 1: What Is Solar Irradiance and Why It Matters for System Design

Solar irradiance is the power of sunlight per unit area, measured in watts per square meter (W/m²). Solar irradiation is irradiance accumulated over time, measured in kWh/m² over a day, month, or year. Design software needs both: irradiance for instantaneous power calculations and irradiation for energy yield estimates.

Outside Earth’s atmosphere, solar irradiance is roughly constant at 1,361 W/m² (the solar constant). After atmospheric absorption and scattering, peak ground-level irradiance rarely exceeds 1,000 W/m² under clear skies. This 1,000 W/m² figure is the basis for Standard Test Conditions (STC), the reference point for all solar panel ratings.

Irradiance vs. Insolation

Solar insolation is essentially irradiation by another name — the total solar energy received per unit area over a specific period. When someone says “Munich gets 1,200 kWh/m² per year,” they are stating the annual GHI insolation. When a panel datasheet says “1,000 W/m²,” that is irradiance at a single moment.

The conversion is simple: a location receiving an average of 4.0 kWh/m²/day of insolation is equivalent to 4.0 peak sun hours — meaning 4 hours of sunshine at the full 1,000 W/m² reference intensity.

How Irradiance Determines kWh/kWp

Specific yield (kWh/kWp) is directly proportional to the irradiance reaching the panels. A system in Madrid receiving 1,660 kWh/m²/year of GHI produces roughly 60-70% more energy per kWp than the same system in Stockholm at 980 kWh/m²/year.

The relationship is not perfectly linear. Temperature losses, spectral effects, inverter efficiency, and cable losses all modify the final yield. Still, irradiance is the single largest variable, accounting for 3-5% of total uncertainty in a typical energy yield analysis.

The 10% Rule

A 10% irradiance error produces approximately a 10% yield error. For a 100 kWp system in Central Europe at 1,000 kWh/kWp, that is 10,000 kWh per year, worth EUR 1,200-1,500 annually.

Over 25 years, that compounds to EUR 30,000-37,500 of misestimated revenue from a single system. Scale across a portfolio and the numbers become very significant for lenders and investors.

Understanding solar angles is a prerequisite for working with irradiance data, because the angle at which sunlight strikes a surface determines how much energy that surface receives. The zenith angle, tilt angle, and azimuth all feed directly into the irradiance equations covered in the next chapters.

Chapter 2: The Four Types of Solar Irradiance

Solar radiation reaching the ground splits into distinct components. Each one behaves differently, and confusing them leads to design errors. Here is what they are and how they connect.

GHI — Global Horizontal Irradiance

GHI is the total solar radiation received on a horizontal surface. It is the number most commonly reported in solar databases and the starting point for nearly all PV yield calculations. GHI combines two components: the direct beam from the sun and the diffuse light scattered by the atmosphere.

Annual GHI values across Europe range from about 900 kWh/m² in northern Scotland and Scandinavia to over 1,900 kWh/m² in southern Spain and Sicily.

DNI — Direct Normal Irradiance

DNI measures only the direct beam component of sunlight, perpendicular to the sun’s rays. It is the primary metric for tracking systems and concentrating solar power (CSP). For fixed-tilt PV, DNI still matters because it determines the direct component hitting your tilted array.

DNI varies more dramatically than GHI across locations. Hamburg might see 850 kWh/m² annually, while Almeria in southern Spain exceeds 2,200 kWh/m². Clouds disproportionately block the direct beam while allowing diffuse radiation through.

DHI — Diffuse Horizontal Irradiance

DHI is the solar radiation received on a horizontal surface from the entire sky dome, excluding the direct beam. It includes light scattered by clouds, aerosols, and atmospheric molecules. On an overcast day, DHI is essentially all you get — the direct beam is gone.

DHI matters more than most designers appreciate. In northern European cities, diffuse radiation accounts for 50-65% of total annual GHI. Even in sunny Mediterranean locations, diffuse makes up 30-40%. Ignoring or poorly modeling the diffuse component will underpredict yield in northern climates and overpredict it for tracking systems.

POA — Plane of Array Irradiance

POA is the total irradiance reaching your array’s surface, accounting for tilt and orientation. It is GHI translated onto your specific array geometry. POA is always calculated, never directly measured in standard databases (though co-planar pyranometers can measure it on-site).

Converting GHI to POA is the core technical challenge covered in Chapter 3. It requires decomposing GHI into direct and diffuse components, then applying a transposition model to project each onto the tilted surface.

The Core Formula

The fundamental relationship connecting these components is:

GHI = DHI + DNI x cos(theta_z)

Where theta_z is the solar zenith angle (the angle between the sun and the vertical). At solar noon on a clear day with the sun at 30 degrees from vertical, if DNI is 900 W/m² and DHI is 120 W/m², then:

GHI = 120 + 900 x cos(30°) = 120 + 900 x 0.866 = 120 + 779 = 899 W/m²

Component Comparison Table

Chapter 3: How to Calculate Solar Irradiance for Your Site

Getting from a GHI number to the actual irradiance on your tilted array involves three steps: decomposition, transposition, and correction. Each step introduces assumptions and potential error.

Step 1: GHI Decomposition

Most irradiance databases provide GHI as the primary value. Some also include DNI and DHI directly. When they do not, you need to decompose GHI into its direct and diffuse components. This is done using decomposition models.

The most widely used decomposition models include:

Erbs model: Uses the clearness index (Kt = GHI / extraterrestrial irradiance) to estimate the diffuse fraction. Simple and fast, but less accurate under partly cloudy conditions.

BRL model (Boland-Ridley-Lauret): A logistic function model that handles the transition between clear and overcast conditions more smoothly than Erbs. Preferred for hourly data.

DISC model (Direct Insolation Simulation Code): Developed by NREL, this estimates DNI from GHI using quasi-physical relationships. Used in many American energy modeling tools.

For most European design work, if your data source already provides DNI and DHI alongside GHI, use the provided values. They are typically derived from higher-quality decomposition algorithms trained on the source’s specific satellite dataset.

Step 2: Transposition Models

Once you have GHI split into DNI and DHI, you need to project these components onto your tilted array surface. This is where transposition models come in. The total POA irradiance consists of three parts:

POA = POA_beam + POA_diffuse + POA_reflected

The beam component is straightforward — it is a geometric projection of DNI onto your tilted surface using the angle of incidence. The ground-reflected component uses a simple albedo factor (typically 0.2 for grass, 0.6 for snow). The hard part is the diffuse component.

Isotropic Model

The simplest approach assumes diffuse radiation comes equally from all parts of the sky:

POA_diffuse = DHI x (1 + cos(tilt)) / 2

Computationally fast, but consistently underpredicts POA by 5-10% because it ignores circumsolar brightening (sky is brighter near the sun) and horizon brightening.

Hay-Davies Model

An improvement over the isotropic model. Hay-Davies introduces an anisotropy index that partitions diffuse radiation into a circumsolar component (treated like a beam, coming from the sun’s direction) and an isotropic background component. It performs well for annual yield estimates and is computationally efficient.

Perez Model

The industry standard. The Perez 1990 model divides the sky dome into three zones: circumsolar disc, horizon band, and isotropic background. It uses empirically fitted brightness coefficients (F1 for circumsolar, F2 for horizon) that vary with sky clearness and brightness conditions.

The Perez model produces mean bias errors below 1.5% at most locations. It is the default in PVsyst, SAM, and most professional yield tools. Unless you have a specific reason to choose otherwise, use Perez.

Step 3: Worked Example — 30-Degree Array in Munich

Let us walk through a real calculation for a south-facing, 30-degree tilted array in Munich (latitude 48.14°N).

Input data (from PVGIS, annual averages):

• GHI: 1,188 kWh/m²/year
• DNI: 1,120 kWh/m²/year
• DHI: 550 kWh/m²/year
• Optimal tilt: 34 degrees
• Chosen tilt: 30 degrees (constrained by roof pitch)

Beam component on tilted surface:

The direct beam on the tilted surface depends on the angle of incidence, which varies hour by hour throughout the year. Using annual averages, the beam transposition factor for a 30-degree south-facing surface at 48°N latitude is approximately 1.25-1.30. This means the tilted surface receives 25-30% more direct beam radiation than a horizontal surface.

Annual beam on tilted surface: approximately 810 kWh/m²

Diffuse component (Perez model):

The Perez model calculates higher diffuse irradiance on the tilted surface than the isotropic model, because it accounts for the circumsolar brightening effect. For a 30-degree tilt, the Perez diffuse transposition factor is typically 0.92-0.95 (slightly less than horizontal because the tilted surface “sees” less sky).

Annual diffuse on tilted surface: approximately 510 kWh/m²

Ground-reflected component:

POA_reflected = GHI x albedo x (1 - cos(tilt)) / 2 = 1,188 x 0.20 x (1 - cos(30°)) / 2 = 1,188 x 0.20 x 0.067 = approximately 16 kWh/m²

Total POA irradiance:

POA = 810 + 510 + 16 = 1,336 kWh/m²/year

This is 12.5% more than the horizontal GHI of 1,188 kWh/m². For a 100 kWp system with a performance ratio of 0.82, the expected yield would be:

Annual yield = 1,336 x 0.82 = 1,096 kWh/kWp

Which aligns well with typical measured yields for well-designed systems in Munich (1,050-1,150 kWh/kWp).

Temperature Corrections

Irradiance data alone does not give you the full picture. Most crystalline silicon panels lose 0.35-0.45% of rated power per degree Celsius above 25°C (STC reference).

In Munich, peak-season cell temperatures reach 40-55°C, causing 5-10% reduction from STC-rated power. In Athens, cell temperatures of 55-70°C cause 10-16% losses. Athens has 33% higher GHI than Munich, but the temperature penalty closes part of the gap.

Professional solar software applies hourly temperature corrections using Faiman or NOCT models. For manual calculations, apply a derating factor of 0.90-0.95 for Central Europe and 0.85-0.92 for Southern Europe.

Chapter 4: Solar Irradiance Data Sources Compared

The quality of your irradiance data directly determines the quality of your yield prediction. Here are the major data sources compared on the metrics that matter.

PVGIS (Photovoltaic Geographical Information System)

Operated by the European Commission’s Joint Research Centre. PVGIS is free, well-maintained, and the default starting point for most European solar projects.

Current version: PVGIS 5.3, using the SARAH-3 satellite dataset from CM SAF (2005-2023).

Coverage: Europe, Africa, most of Asia, parts of the Americas. Best quality in Europe due to the Meteosat satellite coverage area.

Resolution: Approximately 5 km grid spacing for the SARAH-3 dataset. Hourly time resolution available.

Validation: Extensively validated against European ground stations. Typical GHI bias under 3% for most European locations. Performance degrades in mountainous terrain and at high latitudes (above 58°N).

Use case: Preliminary assessments, residential projects, permit applications, and academic work. Accepted by many European banks for projects under 1 MWp when combined with appropriate uncertainty margins.

Meteonorm

Developed by Meteotest AG in Switzerland. Meteonorm is a paid software product that generates synthetic weather data based on long-term ground station measurements and satellite data.

Current version: Meteonorm 8.1 (released 2023), incorporating data through 2022.

Coverage: Global. Interpolates between 8,000+ ground measurement stations worldwide.

Resolution: Site-specific interpolation. Generates hourly TMY (Typical Meteorological Year) data for any coordinate.

Validation: Uses 30+ years of ground station data as its foundation. Considered the gold standard for PVsyst simulations. GHI accuracy typically within 3-5% when a nearby ground station exists, potentially higher uncertainty in data-sparse regions.

Cost: Approximately EUR 650-900 for a professional license (single user, annual).

Use case: Bankable yield reports, PVsyst simulations, financial due diligence. The most widely accepted data source among European lenders and technical advisors.

SolarAnywhere

Developed by Clean Power Research. Primarily focused on the Americas but expanding coverage.

Coverage: Americas (primary), with global coverage through satellite data. Best quality in the continental United States.

Resolution: 1 km or 10 km grid spacing depending on region. Sub-hourly data available (15-minute intervals).

Validation: Validated against the NREL Measurement and Instrumentation Data Center (MIDC) network in the US.

Cost: Subscription-based, starting around USD 5,000/year for professional access.

Use case: US-focused projects. Frequently used for North American bankable reports, especially for utility-scale projects.

Solargis

A commercial satellite-based irradiance data provider with headquarters in Slovakia.

Coverage: Global (latitude 60°N to 50°S). High-quality coverage wherever geostationary satellite imagery is available.

Resolution: 250 m to 2 km depending on region. 15-minute time resolution available.

Validation: Validated against 300+ ground stations globally. Consistently achieves the lowest deviation metrics in the IEA PVPS worldwide benchmark studies. GHI bias typically under 2-3%.

Cost: Per-site reports (EUR 60-250 each) or subscription access.

Use case: Bankable reports for utility-scale projects, portfolio-level resource assessment. Accepted by major international banks and technical advisors (DNV, Black & Veatch, Fichtner).

NASA POWER

NASA’s Prediction of Worldwide Energy Resources. Free access to satellite-derived solar data.

Coverage: Global, based on the NASA MERRA-2 and CERES satellite datasets.

Resolution: Approximately 50 km (0.5° x 0.625°). This is much coarser than other sources.

Validation: Lower accuracy than PVGIS or Solargis for site-specific work. Better suited for regional-scale resource screening.

Use case: Very early-stage screening of remote locations where other data is unavailable. Not suitable for project-level yield estimates due to coarse resolution.

Comparison Table

Chapter 5: Irradiance Across Europe — Reference Data by City

The table below provides reference irradiance values and expected yields for 20 European cities, based on PVGIS 5.3 (SARAH-3 dataset) and validated against Meteonorm 8 data where available.

All values are long-term annual averages. Optimal tilt is the fixed south-facing angle maximizing annual POA irradiance. Expected kWh/kWp assumes a performance ratio of 0.80-0.85.

European Solar Irradiance Reference Table

Northern vs. Southern Europe Analysis

The table reveals several patterns that affect system design decisions:

GHI spread: Southern European cities receive 50-70% more GHI than northern cities. Madrid’s 1,660 kWh/m² is 69% higher than Stockholm’s 980 kWh/m².

DNI spread is even wider: Madrid’s DNI (1,960 kWh/m²) is more than double London’s (870 kWh/m²). This means tracking systems and optimal tilt design provide larger absolute gains in southern locations.

DHI is surprisingly uniform: The diffuse component varies relatively little across Europe — from about 490 kWh/m² in Stockholm to 620 kWh/m² in Rome. What changes dramatically is the direct/diffuse ratio. In London, diffuse radiation accounts for 55% of GHI. In Madrid, it is only 36%.

Optimal tilt increases with latitude: From 31 degrees in Athens to 41 degrees in Stockholm. The rule of thumb “tilt equals latitude minus 10-15 degrees” holds reasonably well across Europe, but local factors (ground albedo, climate patterns) can shift the optimum by 2-5 degrees.

Yield gap is smaller than GHI gap: Madrid produces about 65% more kWh/kWp than Stockholm, despite 69% more GHI. Temperature losses in southern locations and long Scandinavian summer days partially close the gap.

For a deeper look at how solar energy forecasting connects to these irradiance patterns — particularly for grid integration and trading applications — the forecasting guide covers the operational side of irradiance prediction.

Chapter 6: How Irradiance Data Quality Affects Project Bankability

When a bank lends against a solar project, loan repayment depends on energy production, which depends on irradiance. The quality and uncertainty of your irradiance data directly determine whether a project gets financed, and at what terms.

P50 vs. P90: What They Mean

P50 is the median expected annual energy yield. There is a 50% probability that the system will produce at least this much energy in any given year. P50 is your best estimate of long-term average production.

P90 is the energy yield that the system will exceed in 90% of years. It represents a conservative, downside scenario. Banks typically use P90 (or sometimes P75) for debt sizing because they need confidence that loan payments can be covered even in below-average years.

The relationship between P50 and P90 depends on the total uncertainty of your yield estimate:

P90 = P50 x (1 - 1.282 x combined_uncertainty)

For a project with 6% total combined uncertainty (a well-characterized site with good data):

P90 = P50 x (1 - 1.282 x 0.06) = P50 x 0.923

This means the P90 yield is 7.7% below the P50 yield. For a project with 10% total uncertainty (a less well-characterized site):

P90 = P50 x (1 - 1.282 x 0.10) = P50 x 0.872

Now the P90 yield is 12.8% below P50. That 5 percentage point difference in uncertainty translates to a 5+ percentage point difference in the bankable yield — directly affecting the debt-service coverage ratio and the amount a bank will lend.

Sources of Irradiance Uncertainty

Total yield uncertainty comes from multiple independent sources that combine in quadrature (root-sum-square):

Combined typical uncertainty (root-sum-square): 5-8% for a well-characterized project.

Inter-Annual Variability

Solar irradiance is not the same every year. Volcanic eruptions, aerosol loading, and natural climate variability cause annual GHI to fluctuate around the long-term mean. In Europe, the coefficient of variation (standard deviation divided by mean) for annual GHI is typically:

• Southern Europe (Spain, Italy, Greece): 3-5%
• Central Europe (Germany, France, Austria): 4-6%
• Northern Europe (UK, Scandinavia): 5-7%

Counterintuitively, cloudier locations have higher inter-annual variability because small changes in cloud patterns produce larger relative swings in annual GHI.

For a single year’s prediction, the inter-annual variability is used directly. For multi-year averages (the case relevant to project financing), the variability decreases with the square root of the number of years. A 20-year debt tenor effectively reduces the inter-annual contribution by a factor of sqrt(20) = 4.5, to about 1% for southern Europe.

Satellite vs. Ground Measurement Data

Satellite-derived data (PVGIS SARAH, Solargis, SolarAnywhere) uses geostationary satellite images to estimate surface irradiance. Consistent spatial coverage and long time series (20-30 years), but lower accuracy in complex terrain and limited ability to capture localized phenomena.

Ground station data (pyranometer measurements) provides direct physical measurement at a specific point. Highest accuracy when properly maintained (1-2% uncertainty for Class A stations), but sparse network and point measurements may not represent a site 10 km away.

Best practice for bankable projects: Use satellite data as the baseline and validate against the nearest quality ground station within 50 km. On-site measurements, even for 6-12 months, can bias-correct satellite data and reduce uncertainty from 4-5% to 2-3%.

What Lenders Actually Look For

Based on common requirements from European solar project lenders and technical advisors:

1. Irradiance data source must be named and versioned. “Meteonorm 8.1” or “Solargis v2.4” — not just “satellite data.”
2. At least 10 years of data in the underlying time series. 15-20 years preferred.
3. Two independent data sources cross-referenced. If Meteonorm and PVGIS agree within 3%, confidence is higher.
4. Explicit uncertainty analysis with each component quantified and combined using root-sum-square methodology.
5. P50 and P90 yield estimates clearly stated, with the uncertainty assumptions documented.
6. No reliance on a single year’s data. Even excellent on-site measurement data needs a long-term context.

The generation and financial tool in SurgePV produces yield reports with these bankability requirements built in, including source documentation and uncertainty breakdowns.

Chapter 7: Using Irradiance Data in Solar Design Software

In daily practice, solar designers work inside software tools that automate most of these calculations. What matters is knowing how the software processes irradiance data, where to intervene, and how to validate the output.

From Irradiance to Yield: The Calculation Chain

Modern solar design software follows a consistent calculation chain:

1. Location and irradiance data import

You enter project coordinates. The software retrieves irradiance data from its integrated database (typically PVGIS, Meteonorm, or a proprietary source). You receive GHI, DNI, DHI, and ambient temperature as hourly or monthly values.

2. Array geometry definition

You define the array tilt, azimuth, and any tracking configuration. For rooftop systems, this comes from the 3D roof model. For ground-mount, you set the parameters directly.

3. Shading analysis

Shadow analysis tools calculate the shading impact from nearby objects (buildings, trees, terrain) on the array throughout the year. This modifies the irradiance reaching each part of the array — reducing both the beam and diffuse components based on the obstructing objects’ geometry.

Shading and irradiance interaction is error-prone. A building casting shadow on your array during winter mornings blocks direct beam radiation, but the diffuse component is only partially affected. Good software models these effects separately.

4. Transposition

The software applies a transposition model (usually Perez) to convert horizontal irradiance to POA irradiance on your tilted, oriented, and potentially shaded array surface.

5. Module power calculation

Using the POA irradiance and cell temperature (calculated from ambient temperature, irradiance, and wind speed), the software calculates the DC power output for each time step. This accounts for the module’s temperature coefficient, spectral response, and low-light behavior.

6. System losses

DC cable losses, mismatch losses, inverter efficiency curves, AC cable losses, transformer losses, soiling, and degradation are applied sequentially. Each loss factor reduces the gross yield to arrive at the net yield delivered to the grid or consumption point.

7. Energy yield output

The final result: annual energy yield in kWh and specific yield in kWh/kWp. Typically reported as monthly values and an annual total.

Where Designers Should Intervene

While software automates the calculation chain, experienced designers review and adjust at several points:

Data source selection. Do not blindly accept the default. If the software defaults to NASA POWER for a European site, override it with PVGIS or Meteonorm. The data source is the largest single driver of yield accuracy.

Horizon profile. If your site has significant far-field shading (mountains, tall buildings more than 100 m away), manually enter or import a horizon profile. Many irradiance databases assume an unobstructed horizon, which overstates yield for sites in valleys or dense urban areas.

Albedo values. The default albedo of 0.20 (grass) is reasonable for most European sites. But if your ground-mount project is surrounded by light-colored gravel (albedo 0.30-0.40) or seasonal snow cover (albedo 0.60-0.80), adjust accordingly. For bifacial modules, this directly affects rear-side yield — potentially a 5-15% difference.

Soiling assumptions. Default soiling losses of 2-3% are typical for European locations with regular rainfall. Arid sites, agricultural areas, or locations near construction sites may require 5-10% soiling losses. Sites with regular cleaning schedules can justify lower values.

Validating Your Simulation

After running a simulation, perform these sanity checks:

Compare specific yield to reference values. Use the European reference table in Chapter 5. If your Munich simulation shows 1,300 kWh/kWp, something is likely wrong — that is a Madrid-level result. If it shows 800 kWh/kWp, you may have excessive shading or a data error.

Check the performance ratio. A well-designed European system should have a PR of 0.78-0.86. Below 0.75 suggests excessive losses or a modeling error. Above 0.88 for a standard system suggests insufficient loss accounting.

Verify monthly distribution. European solar production is heavily seasonal — expect 3-5 times more yield in June/July than in December/January for northern locations. If your monthly profile looks flat, check for data errors.

Cross-reference with a second tool. Run the same system in a second simulation tool (or at minimum, with a different irradiance dataset). Results within 3-5% of each other indicate consistency. Larger deviations warrant investigation.

SurgePV handles the full chain from irradiance data import through shadow analysis to final yield output in one integrated workflow. By keeping all steps in a single platform, designers avoid the data transfer errors that commonly occur when shuttling between separate irradiance tools, CAD software, and simulation engines.

Conclusion

Solar irradiance is the foundation of every energy yield prediction. The difference between a rough estimate and a bankable report comes down to data source selection, GHI decomposition, transposition model choice, and uncertainty quantification.

Three actions to take from this guide:

1. Audit your current data source. If you are using NASA POWER or a single unvalidated source for commercial projects, switch to PVGIS (free) or Meteonorm/Solargis (bankable). Cross-reference two sources for every project.

2. Check your transposition model. Make sure your simulation software uses Perez or Hay-Davies, not the isotropic model. The difference is 3-8% of predicted yield — larger than most other loss factors you worry about.

3. Quantify uncertainty explicitly. Do not present a single yield number. Present P50 and P90 with documented uncertainty components. This is not just good engineering practice — it is what lenders require, and it protects your professional reputation when actual production data comes in.

Satellite datasets now span 20-30 years and transposition models achieve sub-2% bias. The limiting factor is no longer data availability but whether designers use what is available correctly.

Frequently Asked Questions

What is a good solar irradiance value for solar panel installation?

A good GHI value for solar panel installation is above 1,200 kWh/m² per year. Southern European cities like Madrid (1,660 kWh/m²), Rome (1,530 kWh/m²), and Athens (1,585 kWh/m²) are strong. Northern European locations with GHI above 1,000 kWh/m² — such as Berlin (1,080 kWh/m²) or Amsterdam (1,050 kWh/m²) — are still viable for well-designed systems with proper tilt optimization. Even Stockholm at 980 kWh/m² supports economically viable solar installations, particularly with today’s low module prices.

What is the difference between GHI and DNI in solar energy?

GHI (Global Horizontal Irradiance) measures total solar radiation on a flat horizontal surface, combining direct sunlight and diffuse sky radiation. DNI (Direct Normal Irradiance) measures only the direct beam component, perpendicular to the sun’s rays. GHI is the standard metric for flat-plate PV design. DNI matters more for tracking systems and concentrating solar power (CSP). The relationship is GHI = DHI + DNI times cos(zenith angle). In practice, GHI is what you pull from databases for fixed-tilt system design, while DNI becomes the primary metric when designing single-axis or dual-axis tracking systems.

How do I find solar irradiance data for my location?

The fastest free method is PVGIS. Enter your coordinates and get GHI, DNI, and DHI values with monthly breakdowns. For bankable reports, use Meteonorm (global coverage, EUR 650-900/year license) or Solargis (per-site reports from EUR 60). The Global Solar Atlas provides a quick visual overview of solar potential worldwide. For the most accurate results, always cross-check at least two independent sources, especially for commercial projects. Professional solar software like SurgePV integrates these databases directly, so you get validated irradiance data as part of the design workflow.

How does cloud cover affect solar irradiance?

Cloud cover reduces DNI (direct beam) sharply but increases DHI (diffuse radiation) as sunlight scatters through clouds. On a fully overcast day, DNI drops to near zero while DHI becomes the dominant irradiance component. Total GHI typically falls by 50-80% under heavy overcast. Thin cirrus clouds may only reduce GHI by 10-20%. Locations like London or Hamburg have high diffuse fractions (55-65% of GHI is diffuse) compared to Mediterranean cities where direct beam provides 60-70% of GHI. Modern PV panels still generate 10-25% of rated output under overcast conditions because they convert diffuse radiation. This is why northern European solar projects work despite the common perception that “it is always cloudy.”

What is the best solar irradiance data source for bankable reports?

For bankable solar reports in Europe, Meteonorm and Solargis are the most widely accepted by lenders and investors. Meteonorm offers 30+ year climate normals with global coverage and is the standard reference in PVsyst simulations. Solargis provides high-resolution satellite data validated against 300+ ground stations, consistently achieving the lowest deviation metrics in IEA benchmark studies. PVGIS is acceptable for preliminary assessments and smaller projects, but most financial institutions require Meteonorm or Solargis for due diligence on projects above 1 MWp. Always use TMY (Typical Meteorological Year) datasets for P50 estimates and full time-series data for P90 analysis.