Six comparison points when selecting meteorological data in PVSyst

Table of contents

• Why choosing meteorological data in PVSyst matters

• Comparison point 1: proximity to the installation site and similarity of terrain conditions

• Comparison point 2: closeness of monthly trends as well as annual averages

• Comparison point 3: consistency not only of solar irradiation but also of temperature and surrounding meteorological conditions

• Comparison point 4: can you compare at the level of granularity required for the design purpose?

• Comparison point 5: ease of aligning assumptions when comparing multiple options

• Comparison point 6: ease of explanation after reviewing calculation results

• A mindset to connect PVSyst meteorological data selection to practical accuracy

Why choosing meteorological data in PVSyst matters

When running an energy yield simulation in PVSyst, many practitioners first focus on equipment configuration, installation orientation, tilt, and loss settings. Those are of course important, but if the fundamental approach to the meteorological data that forms the foundation is off, no matter how carefully you refine the other parameters, the overall reliability of the results will remain unstable. In other words, selecting meteorological data is not just part of the initial setup; it is a crucial decision that determines the very character of the energy yield forecast.

In practice, when a candidate site is not yet finalized or when comparing multiple sites, it is common to use meteorological data from a nearby representative point to carry out preliminary assessments. This approach is realistic, but if the criteria for which data to adopt are vague, the meaning of later comparison results can become unclear. Annual energy yield numbers may take on a life of their own, making it difficult to explain why those numbers were produced and which option is actually advantageous.

Moreover, meteorological data are not just about solar irradiation. In PVSyst, temperature conditions and seasonal trends also affect results, so it is not sufficient to simply choose the premise that appears to have higher irradiation. A location with attractive annual irradiation may, when considering monthly distribution and temperature conditions, be less representative of site reality than another location’s data. Therefore, when selecting meteorological data you need to check consistency with the project as a whole, not just the magnitude of the numbers.

Furthermore, the choice of meteorological data directly affects how easily you can explain simulation results. In internal meetings and comparison discussions, you must be able to explain which meteorological data were used and why they were adopted. If this is vague, no matter how carefully equipment parameters are set, trust in the assumptions themselves will be weakened. If you are using PVSyst in a professional context, meteorological data should be treated not as mere calculation inputs but as the premises underlying design decisions.

Below, the six comparison points that practitioners should pay particular attention to when selecting meteorological data in PVSyst are organized. Each point becomes meaningful when judged in combination with the others. By checking each carefully, not only the accuracy of the energy yield forecast but also the ease of comparison and explanation will change significantly.

Comparison point 1: proximity to the installation site and similarity of terrain conditions

The first thing to confirm is how close the meteorological data’s reference point is to the actual installation site. This seems simple, but in practice it is often handled ambiguously. It is not uncommon to use data from a representative point near the candidate site, but adopting data based only on a sense of proximity can lead to discrepancies with site conditions surfacing later. Since the reference point conditions form the foundation of the entire simulation in PVSyst, the judgment of proximity should be made carefully.

Being close is of course important, but it is not necessarily sufficient. Even within the same region there may be elevation differences, variations in how open the surrounding terrain is, or differences between inland and coastal environments that change the overall impression. Such differences affect not only the expected energy yield but also the interpretation of monthly trends and temperature impacts. Therefore, rather than simply selecting the nearest point’s data, it is important to assess whether that point is a reasonable representative for the site in question.

A common pitfall for practitioners is reusing nearby data from previous projects without reconsideration. Proceeding with the assumption that “it’s the same area, so there shouldn’t be much difference” can make it hard to tell, when creating comparison scenarios, whether differences arise from design conditions or from differences in meteorological reference points. PVSyst results are sensitive to underlying assumptions, so treating the concept of the reference point lightly will make explanation harder than the raw numbers themselves.

As a response to this comparison point, it is useful to document in words why a given meteorological point was selected at the stage of choosing data. If the adoption reasons are clear—closest to the installation site, similar terrain characteristics, reasonable for a preliminary comparison, etc.—it will be easier to re-evaluate later. Choosing meteorological data is less about finding a single correct answer and more about being able to justify why a given premise was reasonable.

Also, being mindful of proximity to the installation site helps identify the range to re-evaluate once site details are clarified. A representative point may be acceptable at the preliminary stage, but as the project moves into more detailed phases, you may need to move to premises closer to actual site conditions. Planning for such staged re-evaluation makes PVSyst simulations more practical.

Comparison point 2: closeness of monthly trends as well as annual averages

When comparing meteorological data, many people first look at annual averages or annual totals. Annual estimates are easy to understand and convenient for comparing projects. However, when conducting practical simulations in PVSyst, judging by annual values alone is insufficient because if monthly trends differ, the character of the results can change significantly even when annual values are similar.

For example, two datasets may have comparable annual totals, but one may be stronger from spring to early summer while another is relatively stable in autumn. Differences in monthly distribution affect how generation behaves and how comparisons are perceived when combined with installation orientation, loss settings, or shading impacts. In PVSyst, monthly generation and how losses appear are important checks, so understanding those trends at the meteorological data stage makes later interpretation easier.

In practice, annual energy yield often becomes the central criterion, so monthly trends are sometimes deferred. However, in internal briefings and project comparisons you may be asked why a particular option performs well in summer or why a winter drop is pronounced. If you selected meteorological data without checking monthly trends, it becomes difficult to separate whether such behavior is due to equipment conditions or to differences in meteorological distribution. Don’t be satisfied with annual figures alone—check for monthly biases as well.

A way to address this comparison point is, when comparing candidate meteorological datasets, to check at least whether the monthly sequences show any extreme inconsistencies. If a particular month looks unnaturally high or low, consider the reason. Of course, regional differences and seasonality naturally cause distribution differences; what matters is whether the distribution is substantially inconsistent with the expected installation site and surrounding impressions for this project.

Being aware of monthly trends also makes reporting easier. If you understand the meteorological reasons for monthly generation patterns from the data selection stage, you can present consistent explanations when viewing PVSyst outputs. When comparing meteorological data, confirm not only the attractiveness of annual averages but also whether the monthly distribution forms a premise that is useful for the project.

Comparison point 3: consistency not only of solar irradiation but also of temperature and surrounding meteorological conditions

When choosing meteorological data in PVSyst, it is natural to focus on the magnitude of solar irradiation. However, what should be compared in practice is not irradiation alone. You must assess whether temperature conditions and surrounding meteorological tendencies are consistent; otherwise, even plausible-looking numbers can lead to simulations that diverge from site reality. Meteorological data are not only the foundation of energy yield but also a premise that affects temperature impacts and how losses appear.

Even between locations with similar-looking irradiation, differences in temperature trends alter how equipment is affected by temperature. Moreover, seasonal temperature behavior and how it interacts with the surrounding environment can change not only the energy yield but also perceptions of monthly stability and losses. In PVSyst, prioritizing irradiation data alone, without strong linkage to other meteorological conditions, can make later interpretation difficult.

Practitioners should be cautious about being overly attracted to datasets that appear to have high annual irradiation. Irradiation is important, but for simulations that reflect project reality, you need consistency that includes temperature conditions and surrounding environment. Especially when comparing multiple options, neglecting the characteristics of surrounding meteorological conditions will make it easy to conflate design differences with environmental differences.

To address this comparison point, after selecting meteorological data, confirm whether the location’s temperature conditions and seasonal tendencies are consistent with the project image. Do not adopt data simply because the irradiation looks favorable; consider whether the combination of irradiation and temperature is natural for the project. This reduces incongruities when connecting to equipment parameters and loss settings, making PVSyst results easier to read.

Checking consistency with temperature and surrounding meteorological conditions is also advantageous for accountability. It becomes easier to organize explanations—for example, why generation is lower than expected or why monthly differences appear—by relating them to meteorological conditions rather than attributing them solely to equipment issues. When selecting meteorological data in PVSyst, avoid jumping to the irradiation figures alone; compare datasets including surrounding meteorological conditions.

Comparison point 4: can you compare at the level of granularity required for the design purpose?

The level of detail at which meteorological data should be handled is also an important perspective in comparisons. PVSyst allows simulations to be built with various assumptions, but you do not need the same level of granularity for every project. What matters is whether the granularity of the meteorological data is appropriate for the current design purpose and stage. Ignoring this can lead to wasting time by over-analyzing or weakening the meaning of comparisons by being too coarse.

For example, when roughly narrowing down candidate sites, it may be sufficient to see the overall trends. Performing overly complex comparisons at this stage can be of limited use if the design conditions themselves are not yet fixed. Conversely, when conditions are reasonably established and you are narrowing differences between options, it can be appropriate to examine meteorological data more carefully. There is no single universally correct meteorological dataset for PVSyst; the important thing is to judge the required level of accuracy according to the project objective.

A common tendency among practitioners is to either try to refine meteorological data down to fine detail even at the initial estimation stage, which wastes time, or to proceed with preliminary assumptions even in the detailed comparison stage, which weakens explanatory power. The point is that how you compare meteorological data should be determined not only by desired accuracy but by whether the granularity matches the current stage. The more you use PVSyst in practice, the more important this discernment becomes.

A useful countermeasure is to clarify in advance what you want to decide with this simulation before selecting meteorological data. If the purpose is candidate site selection, layout comparison, or preparing grounds for internal explanation, that clarity will help determine how deeply you need to compare meteorological data. In PVSyst, it is more efficient to choose a way of using data that matches the purpose than to search endlessly for the best possible data.

Also, organizing the granularity concept makes reporting easier. If you can explain why you adopted a given meteorological dataset and why that level of comparison depth was sufficient, you can justify the approach. PVSyst simulations are not an end in themselves because they are detailed; the key is whether they are useful for project decisions. Therefore, in meteorological data comparisons you should always consider balance with the design objective.

Comparison point 5: ease of aligning assumptions when comparing multiple options

One practically important aspect when selecting meteorological data in PVSyst is how easy it is to align assumptions when comparing multiple options. Issues that might be overlooked for a single option become apparent when comparing alternatives. This is because comparisons demand an ability to explain the causes of differences, not just the numbers. If meteorological data selection varies by option, it becomes difficult to tell whether differences in energy yield are due to design conditions or to differences in meteorological premises.

In practice, it is common to compare options that change only orientation, only tilt, or only layout. The purpose of comparison should be differences in design conditions, not differences in how meteorological data were chosen. Nevertheless, if the criteria for meteorological data subtly differ by option, the basis of comparison collapses. While PVSyst outputs may be numerically valid, their persuasive power as comparison material weakens.

To address this comparison point, first clarify what variables you want to compare. Then, keep all non-comparative premises as consistent as possible. The same applies to meteorological data: if the comparison is for the same installation site, align the meteorological data basis; even when comparing candidate sites, maintain consistent adoption criteria across options. In other words, meteorological data that are easy to compare are not necessarily the most accurate ones but those that do not undermine the axes of comparison.

Holding this perspective also greatly eases reporting and internal explanations. If all options use the same approach to select meteorological data, you can focus differential explanations on design conditions. If meteorological assumptions drift between options, you must first explain those premise differences before discussing the results. In PVSyst practice, this extra explanation often delays decision-making.

Additionally, comparing meteorological data with an eye to how easily assumptions can be aligned speeds up later revisions if conditions change, because it will be clear which premises are common and which are differential. Selecting meteorological data in PVSyst with comparison operations in mind, rather than single-shot optimization, greatly improves practical usability.

Comparison point 6: ease of explanation after reviewing calculation results

The last comparison point is practical but extremely important: whether, after adopting the meteorological data, the calculation results will be easy to explain. In PVSyst it is not difficult to produce numbers. What is required in practice is the ability to explain why those numbers occurred and why that premise was chosen. If this perspective is missing at the meteorological selection stage, the explanatory burden increases dramatically once results are available.

In practice, presenting annual energy yield numbers is rarely the end of the discussion. Questions arise such as why this meteorological reference point was used, why this dataset was chosen over others, and why monthly trends appear as they do. If the adoption reason is vague, it becomes difficult to supplement explanations with equipment or loss conditions. Meteorological data are the entry point to the simulation but also the basis for explanation in professional contexts.

A measure to address this comparison point is to confirm at the meteorological data selection stage whether you can clearly articulate the reasons for adoption. If reasons such as proximity to the site, similarity of terrain conditions, appropriateness for the current project phase, and consistency with comparison scenarios are clear, the consistency of result explanations improves. Conversely, if the reason is merely “it was somewhere nearby” or “we used it last time,” it is hard to lend the resulting numbers credibility. In practical PVSyst use, choosing premises whose rationale you can explain is crucial.

Choosing meteorological data that are easy to explain also speeds up troubleshooting when results seem odd. If the rationale for a premise is organized, it is easier to decide where to look first. PVSyst results become more readable as input conditions are more appropriate, and that appropriateness is deeply linked to explainability. Including explainability as a comparison point is necessary to ensure simulations are not just desk calculations but practical tools.

Furthermore, explainable premises assist team sharing. If another team member can understand why a particular meteorological dataset was adopted, reviews and handovers proceed more smoothly. Selecting meteorological data in PVSyst is not simply selecting the numerically most attractive dataset. The most valuable comparisons in practice are ones that choose premises that remain useful for subsequent checks, explanations, and revisions.

A mindset to connect PVSyst meteorological data selection to practical accuracy

What is common across the six comparison points above is the idea of not treating meteorological data as mere input materials. Only when you consider proximity to the installation site, monthly trends, consistency with temperature conditions, granularity appropriate to the design purpose, ease of aligning assumptions, and explainability does meteorological data selection in PVSyst become a decision that withstands real-world practice. Rather than choosing meteorological data that make annual numbers look good, select datasets that are naturally connected to the design conditions and comparison scenarios and that are easy to explain.

For practitioners, the important thing is not to find perfect meteorological data. Rather, it is more important to clarify why a given premise was adopted for this project and to operate while checking consistency with results. If meteorological data selection is vague, comparisons of equipment conditions, interpretation of losses, and report preparation all become unstable. Conversely, having a consistent approach to meteorological data selection tends to improve both the accuracy and the explanatory power of the simulation.

If you truly want to increase the validity of meteorological premises, it is also necessary not to stop at desktop data comparisons. If understanding of the installation site, site orientation, surrounding terrain, layout constraints, and shading potential is vague, your judgment about which meteorological data are appropriate will be weak. Meteorological datasets handled in PVSyst may be broad regional premises, but how you translate them to the site depends on site understanding. In other words, the accuracy of meteorological data selection is linked to the accuracy of site assessment.

In that sense, if you want to efficiently proceed with on-site position checks and coordinate acquisition, using an iPhone-mounted high-precision GNSS positioning device like LRTK can be effective. If on-site position information and site conditions are easier to organize, it becomes easier to align PVSyst meteorological point judgments with orientation, tilt, and layout conditions. If you can improve desktop simulation accuracy in PVSyst and support site assessment accuracy with LRTK, meteorological data selection becomes not just an assumption setting but a site-rooted design decision. The ability to correctly compare meteorological data not only raises the accuracy of energy yield forecasts but can be said to be the practical capability that connects design and the field.