6 Ways to Read PVSyst Solar Irradiance Data｜Meteo Basics

When carrying out solar PV system design and energy-yield forecasting in practice, it's easy to focus only on PVSyst's result screens and reports, but in fact the way you interpret the Meteo data that precedes them has a large impact on the quality of the results. PVSyst's official tutorial also explains that meteorological data are a primary source of simulation uncertainty and that you should use reliable data and perform basic cross-checks. In other words, if you handle Meteo carelessly, no matter how carefully you examine the Loss Diagram or PR afterward, the foundation will remain uncertain.

Especially for practitioners searching for "how to read PVSyst," understanding how to interpret Meteo is often the first hurdle. If the differences between GHI, DHI, and DNI, the difference between monthly averaged data and hourly data, the meanings of temperature and wind speed, and the relationship with horizon conditions remain vague, confidence in the predicted power generation figures will be weak. Because PVSyst calculates the solar irradiance on the receiving surface from meteorological data and then builds up losses on top of that, Meteo is not just an input field but the starting point for the entire simulation.

Also, Meteo data are not a single type. In PVSyst, there are multiple ways to input data, such as synthesis from monthly average data, importing external time-series data, and using a representative year. The official documentation also notes that there is significant variation among the meteorological data sources available, and that it is difficult to determine strictly which one is most appropriate for a given site. That is why, rather than taking the numbers at face value, it is important to first read and understand what kind of data they are.

This article organizes how to read PVSyst irradiance data in practice from six perspectives. First, confirm the types of data and the time resolution; next, clarify the relationships among GHI, DHI, and DNI; then separate raw data from values after tilt-surface conversion; grasp the meaning of air temperature and wind speed; check TMY and multi-year representativeness; and finally summarize the workflow for reading the data in combination with horizon and terrain conditions. By understanding the basics of Meteo, subsequent readings of power generation forecasts, PR, and the Loss Diagram become much more reliable.

Table of Contents

• Key points to grasp before reading Meteo data

• How to read 1｜First check the data types and temporal resolution

• How to read 2｜Clarify the relationships among GHI, DHI, DNI, and BHI

• How to read 3｜Do not confuse raw data with values converted for tilted surfaces

• How to read 4｜Do not dismiss temperature and wind speed as mere "auxiliary values"

• How to read 5｜Confirm whether the dataset is TMY, a single year, or representative of multiple years

• How to read 6｜Interpret the data in combination with horizon and terrain conditions

• Common misunderstandings

• The accuracy of assessing site conditions determines how Meteo data should be used

• Summary

Key points to keep in mind before reading Meteo data

Before reading PVSyst's Meteo data, the first thing to understand is that Meteo is not a "background explanation" of the results but the "foundation" of the results. PVSyst's simulations calculate the incident energy on the receiving surface based on the solar irradiance, temperature, wind speed, and other data contained in the meteorological file, and then stack optical losses, temperature losses, array losses, and system losses. Therefore, if your interpretation of Meteo is ambiguous, no matter how carefully you trace losses later, isolating the root causes will be unreliable.

In PVSyst's Weather Data documentation, the typical variables contained in a standard .MET file are organized with GlobHor, DiffHor, and T_Amb as the core, while additional variables such as BeamNor, BeamHor, WindVel, and Albedo can also be included. In other words, Meteo data is not simply a "table of solar irradiation"; it is a composite dataset used to pass irradiance, temperature, and meteorological conditions to simulations. When handling Meteo data in practice, it is important to grasp this overall picture first.

Furthermore, PVSyst also shows that differences in meteorological data are not small. In the official comparison of data sources, it states that the available meteorological data are not an exact science, that there are large differences between databases, and that it is difficult to determine which is optimal. Therefore, reading Meteo data means being aware not only of the meaning of the variables but also of the provenance and nature of the data.

How to Read 1｜First, check the data type and temporal resolution

The first thing to check is the "type" and "time resolution" of the Meteo data — whether it is on a monthly-average basis, hourly data, or a representative year. According to PVSyst's official documentation, if only monthly average data are available, it first generates synthesized time-step data and then uses that for the simulation. In other words, results calculated from monthly-average data need to be understood as being based on a time series synthesized by a model, not on time behavior that was directly measured.

On the other hand, when using a representative year or time-series data, they can be treated as data that inherently contains temporal information. PVSyst’s description of TMY states that there is a tool to generate a TMY from time-series data spanning more than 10 years, and that if time-series data are not available, there is an alternative method that synthesizes it from monthly averages. What this shows is that even if results appear to be the same "representative-year-like" output, you should interpret them differently depending on how they were generated.

In practice, this check is important so you don't overtrust fine-grained hourly behavior in synthetic data derived from monthly averages. Conversely, if you're using time-series data or TMY, you can be somewhat more confident in monthly or hourly interpretations. The first step in reading Meteo data is to confirm what granularity the data has. If you read only the results without checking this, it's easy to misplace your expectations of accuracy.

Reading 2｜Organizing the relationships among GHI, DHI, DNI, and BHI

Next, what I want to clarify are the relationships among the basic variables of irradiance. In PVSyst's Irradiation models, GHI corresponds to GlobHor, DHI to DiffHor, DNI to BeamNorm, and BHI to BeamHor. GHI is expressed as the sum of DHI and BHI, and BHI is obtained by multiplying DNI by the sine of the solar altitude. In other words, these are separate numbers, but they are interrelated components of irradiance.

The reason this distinction is necessary in practice is that DNI and BHI are easily confused. DNI is the direct irradiance on a surface normal to the sun's rays, while BHI is the amount of that projected onto a horizontal surface. Even though both are "direct components," the reference surface is different, so both the magnitude and the meaning of the numbers change. In PVSyst, BeamNorm and BeamHor are used as different names, so understanding this makes it easier to read the column names and result variables in a Meteo file.

Also, it is risky to look only at GHI and conclude that this represents the site’s irradiance. For shading and Transposition calculations, GHI must be separated into DHI and the direct component. The official documentation also states that, to calculate shading losses, global irradiance needs to be decomposed into diffuse and direct components. In other words, even if you only know the GHI, you need to examine its components to proceed with evaluating the receiving surface and shadows.

What beginners should first remember is that GlobHor, DiffHor, BeamNorm, and BeamHor are not independent variables on the same level, but related components of solar irradiance. Understanding this makes it easier when looking at Meteo data tables to tell which values are raw data and which are intermediate values from conversions, and it also stabilizes how you interpret the subsequent incident-light conditions and losses.

How to Read 3｜Do not confuse raw data with values after conversion for sloped surfaces

When reading meteo data, it is crucial not to confuse raw data with values that have been transposed to a tilted plane. In PVSyst's "Simulation variables: meteo, irradiance and PV array", variables such as GlobHor, DiffHor, Tamb, and Windvel are listed as the raw meteorological variables contained in the meteo file, and then GlobInc, BeamInc, DiffAInc, DiffSInc, and AlbInc appear as the results of the transposition. In other words, the meteorological inputs on the horizontal plane and the incident energy converted to the tilted plane are different things.

If you don't understand this difference, you might, for example, assume that GlobHor is the solar radiation received by the equipment surface as is. In reality, however, GlobInc for the tilted surface is calculated by taking into account azimuth, tilt, horizon, reflections, and other factors. The official documentation also explicitly states that the incident energy entering the collector plane is the result of a transposition. In practice, when assessing incident-light conditions, the value you generally need is this tilted-surface value.

Additionally, a PVSyst .MET file may contain GIPMeas, i.e. measured plane-of-array irradiance. This is a special case, but when using measured POA it is important not to confuse it with the results automatically obtained by transposition from a horizontal-plane weather file. The meaning of subsequent calculations changes depending on the type of raw data.

When working with Meteo in practice, it becomes easier to organize things if you always keep in mind whether a number is an original input or a value converted by PVSyst. If you follow the order of checking the horizontal-plane input first and then looking at the irradiance on the tilted plane, your understanding of the resulting numbers will deepen considerably. This forms the basis for interpreting power generation forecasts, the Loss Diagram, and PR.

How to Read 4｜Don't dismiss temperature and wind speed as "auxiliary values"

When reading Meteo data, focusing only on solar irradiance and overlooking ambient temperature and wind speed is risky. In PVSyst's list of meteorological variables, Tamb is specified as ambient temperature and Windvel as wind speed, and it explains that if Windvel is not present in the file, monthly values or the default value of 3 m/s will be used. In other words, even if wind speed appears to be an optional input, the simulation assumes that some value will always be used.

Temperature and wind speed are important because they affect energy production through module temperature. Even PVSyst’s meteorological data tutorial organizes meteorological data as requiring not only horizontal irradiance but also ambient temperature, and wind speed should be provided if available. Because temperature losses have a major impact on annual and monthly results, treating temperature and wind speed lightly at the Meteo stage will make subsequent prediction accuracy unstable.

In practice, there are cases where summer power generation plateaus despite favorable solar irradiance conditions. In such cases, the first thing to suspect as the cause is the temperature conditions, including air temperature and wind speed. If there are anomalous temperatures or wind speeds in the weather file, the way TempLoss appears will also change. Therefore, when reading Meteo data it is important to check not only the irradiance but also the validity of the temperature conditions.

A common misconception among beginners is treating air temperature and wind speed as merely "auxiliary reference values." However, in practice these are the foundation of thermal losses and greatly influence summer performance evaluations. As a basic principle of Meteo, it is important to have the sense that temperature conditions are as important as solar radiation.

How to read 5 | Check whether it's a TMY, a single year, or representative of multiple years

The next important thing when reading meteo data is to confirm whether the data are TMY, single-year data, or multi-year representative values. In PVSyst's description of TMY, TMY is treated as a method for creating a representative year from time-series data spanning a sufficient number of years, and when time-series data are not available a method of synthesizing from monthly averages is also used. In other words, even if they look like the same "annual forecast," how you interpret them changes if the representativeness of the underlying time series differs.

Also, in PVSyst's comparison of data sources, there are large differences among the available meteorological datasets, and it is considered difficult to estimate which is optimal for a given site and how large the errors are. Meteorological data are far from an exact science, and differences between sources, differences in measurement periods, interpolation methods, and models all affect the results. Therefore, when reading Meteo, it is important not only to look at the numbers themselves but to confirm which period the data represent.

A common misconception in practice is to take the results from single-year data as if they were a long-term average. Conversely, it is also off the mark to expect the behavior of a specific year when using representative-year data. PVSyst’s weather-data tutorial likewise states that the data are a major source of simulation uncertainty and that reliable sources and basic cross-checking are important. In other words, confirming whether the data are TMY, single-year, or multi-year averages is itself an important way to interpret and decide how to use the results.

Once you can adopt this perspective, you are less likely to over-rely on power generation forecasts and PR figures. Reading Meteo is not only about the meanings of meteorological variables, but also about understanding which years the data represent and how they do so. In practice, the level of confidence in your work depends greatly on whether this point is grasped.

How to Read 6｜Interpret in Combination with Horizon and Terrain Conditions

The final point when reading meteo data is not to consider it separately from horizon and terrain conditions. In PVSyst’s monthly-average-based data source description, the effects of far shading at sunrise and sunset in mountainous areas may not be included in the original data, and in such cases a horizon mask must be applied separately for the project area. Furthermore, it is explained that providing the horizon conditions within PVSyst makes it easier to correctly handle diffuse transposition onto sloped surfaces.

This point is extremely important in practice. Even if Meteo data shows sufficient solar irradiance, the actual irradiance received by a system can vary because of the local horizon, surrounding terrain, and distant obstructions. In other words, looking only at Meteo and assuming you understand the site conditions can cause you to overlook terrain effects. This difference is especially evident in mountainous areas, on slopes, or in places with large variations in surrounding topography, and it clearly appears in power generation forecasts.

Also, PVSyst's simulation variables include irradiance corrected for far shading, such as GlobHrz. In other words, the raw values in the Meteo data and the effective irradiance that accounts for horizon conditions are handled at different stages. Understanding this makes it easier to consider the irradiance and horizon conditions when the Meteo data numbers look good but the results don't improve.

As a practical approach, after checking Meteo you should always verify whether corrections for the horizon or surrounding terrain are necessary at that site. Solar irradiance data gives you the sky conditions, but how open the sky actually is on site is a different matter. As a basic rule for Meteo, remember to read the sky figures and terrain conditions together; doing so will make power generation forecasts considerably more stable.

Common Misunderstandings

The most easily misunderstood aspect of PVSyst's Meteo data is assuming that GlobHor directly represents the irradiance on the plane of the installation. In reality, GlobHor is raw data on the horizontal plane, and the irradiance on the installation plane is GlobInc after transposition. Because the meaning differs before and after applying azimuth, tilt, and horizon conditions, confusing these will lead to a significant misinterpretation of the incident irradiance conditions.

Another common mistake is treating data derived from monthly averages as if their temporal behavior were actual observations. PVSyst explains that when only monthly averages are available, it synthesizes time-series data before running simulations. In other words, even if you get results with fine temporal resolution, if their source is synthesized data you should not overtrust behavior at the time-of-day (hourly) level.

Also, it’s dangerous to assume that choosing a single weather data source will make you safe. PVSyst’s comparisons of data sources show differences between databases, and it is difficult to determine which is optimal. In other words, Meteo data should be used with an understanding of its characteristics rather than treated as the “absolute correct” choice. Being aware of this uncertainty lets you handle power generation forecast figures more responsibly.

The accuracy of assessing on-site conditions determines how Meteo is used

However carefully you analyze Meteo data, if your understanding of the on-site conditions is unclear, there are limits to how you can use it. This is because Meteo provides sky-side conditions, and how much solar radiation an installation actually receives is determined by orientation, tilt, obstructions, the horizon, and the layout. PVSyst treats far shading and transposition as separate stages for the same reason: the amount of incident solar radiation cannot be determined from meteorological data alone.

In practice, even small differences in the openness of the horizon, the positional relationship with buildings and trees, and the orientation and spacing of equipment change how Meteo data should be used. In other words, the ability to correctly read Meteo and the ability to accurately assess the site are inseparable. Making use of solar irradiance data means understanding not only the sky conditions but also how the site receives that sky.

In that sense, having a means to determine on-site positional relationships with high precision helps strengthen the practical use of Meteo. If you can more accurately grasp equipment layout, distances to obstructions, azimuths, and horizon clearance, it becomes easier to refine the assumptions that go into PVSyst, and you will gain greater confidence in how you interpret Meteo data. It is important not only to examine meteorological data carefully, but also to consider how to connect it to the site.

What naturally follows here is LRTK, an iPhone-mounted GNSS high-precision positioning device that serves as a means to accurately grasp on-site positional relationships. By making it easier to confirm positions on site, measure distances to obstacles, and improve the reproducibility of equipment placement, it becomes simpler to determine which horizon conditions and layout conditions should be applied to Meteo data. In practical work where you want to use PVSyst’s Meteo tied to actual site conditions rather than just desk-based numbers, measures like LRTK are effective.

Summary

When reading PVSyst irradiance data, first check the data type and time resolution, next clarify the relationships among GHI, DHI, DNI, and BHI, then treat raw data and values after conversion to the tilted surface separately, verify temperature and wind speed as the basis for temperature losses, confirm whether it is TMY or a single year and whether it has multi-year representativeness, and finally read it in combination with horizon and terrain conditions. By mastering these six ways of reading, Meteo becomes much easier to understand as the foundation for power generation forecasting rather than just a simple input table.

What is important is not to view Meteo data as a mere list of numbers, but to interpret what granularity the data has, what representativeness it carries, and what transformations it undergoes before reaching the receiving surface. Once you can do this, you will be more likely to accept the subsequent PR, Loss Diagram, and power generation forecast figures. The basics of Meteo lie in correctly understanding the entry point of the simulation.

And to make that interpretation even more reliable, it is essential to understand the site’s spatial relationships with high precision. If you want to organize the horizon, obstacles, and equipment layout more accurately, it can be useful to consider leveraging LRTK, an iPhone-mounted GNSS high-precision positioning device. By combining the ability to correctly read PVSyst’s Meteo with the ability to accurately assess the site, it becomes easier to arrive at more convincing power generation forecasts and design decisions.