Six tips to avoid missing the mark on energy yield forecasts by choosing meteorological data in PVSyst

When performing energy yield forecasts with PVSyst, attention tends to focus on modules and loss settings, but in reality the choice of meteorological data sets the foundation for the results. If any of irradiance, temperature, wind speed, or time definition deviates from on-site conditions, calculations can be meticulous yet the forecast can still diverge from reality. PVSyst itself organizes that meteorology is a major source of uncertainty in generation simulations and that the files used for simulation are hourly meteorological files. PVSyst +1

In practice, the required accuracy and accountability differ depending on the situation—early-stage estimates for a project, internal approval assumptions, checks before detailed design, or post-construction comparisons of actual performance. Nevertheless, if you always use the same type of meteorological data, forecasts can become overly optimistic or excessively conservative. What matters is not just whether the data can be imported into PVSyst, but whether that data is appropriate for the specific project.

Table of Contents

• Why choosing meteorological data in PVSyst is important

• Tip 1 Use monthly values, time series, and TMY according to purpose

• Tip 2 Choose data from locations not only close but with similar terrain conditions

• Tip 3 Check the length and representativeness of the period, not just single-year values

• Tip 4 Check not only irradiance but also temperature, wind speed, and missing-data conditions

• Tip 5 Always inspect timestamp definitions and time-shift consistency

• Tip 6 Don’t treat one dataset as the truth; use comparisons to capture a forecast range

• Summary

Why choosing meteorological data in PVSyst is important

PVSyst’s energy yield forecast reads horizontal-plane irradiance from the meteorological data you input, converts it to the tilted plane, applies temperature effects, and stacks various losses to produce the final energy output. In other words, the character of the meteorological data entered at the start cascades through the entire subsequent calculation. If irradiance is relatively high, the forecast tends to be higher; if temperature is relatively low, output will appear favorable; if wind speed is overestimated, module temperature will appear lower. Even small apparent differences can accumulate into non-negligible annual discrepancies. PVSyst +1

Also, PVSyst separates geographic site information that holds monthly values and the hourly meteorological files used directly for simulation. There are methods to generate hourly data from monthly values and methods to read originally hourly data. Therefore, choosing meteorological data is not mere data entry but a design decision about which level of granularity and which accuracy to adopt. If this is left vague, revising only loss settings later will not remove the fundamental mismatch. PVSyst +1

Tip 1 Use monthly values, time series, and TMY according to purpose

The first tip is to use the type of meteorological data that matches the project purpose. In PVSyst you can synthesize hourly time series from geographic site information that contains monthly meteorological values, or you can import external hourly data directly. Furthermore, if you have more than 10 years of hourly data, there is a function to generate a TMY as a representative year. In short, PVSyst does not have a single correct option; it provides multiple choices: monthly values, synthesized time series, measured or reanalysis time series, and TMY. PVSyst +2 PVSyst +2

For early rough estimates, a synthesized time series based on monthly values can be effective to grasp the overall picture. However, this is a time series constructed to satisfy monthly averages and is not intended to reproduce hour-by-hour cloud transitions or extreme weather events. In fact, PVSyst’s synthesized time series are adjusted to fit the monthly input values, but their time variability is generated. Therefore, in situations that require strong accountability—such as financing explanations, procurement decisions, guarantee negotiations, or comparisons with existing plants—long-term hourly data or TMY should be prioritized for greater persuasiveness. PVSyst +1

A common mistake is to expect time-series accuracy when only monthly values are available. Monthly values are convenient, but if only monthly global irradiance is available and diffuse components must be estimated separately, additional errors are likely to be introduced. The more carefully you want to perform tilted-plane conversion and impact assessment of irradiance, the more important it is to distinguish which variables are actual measurements and which are estimates. PVSyst

Tip 2 Choose data from locations not only close but with similar terrain conditions

The second tip is not to adopt data solely because the station is geographically close. In practice, there is a tendency to use the nearest observation point or the most easily obtainable gridded data. However, even within the same municipality, coastal and inland sites, basins and plateaus, or foothills and plains can differ greatly in morning/evening irradiance, fog occurrence, diurnal temperature range, and wind patterns. You must judge whether the site represents the project location, including elevation, local terrain, surrounding obstructions, and whether snow accumulates.

In mountainous or highly undulating areas, horizon conditions are especially important. PVSyst explains that site database basic irradiance is usually treated under a free-horizon assumption, whereas measured-based data may already include the horizon influence at the observation point, and synthesis models do not know that horizon shape. Therefore, if the observation point’s horizon conditions differ from those at the actual plant, morning and evening incident light conditions can be mismatched. In mountain projects, choosing meteorological data with a mindset suited for flatland sites can lead to errors not only in annual energy but also in the hourly output shape. PVSyst

In practice, when selecting meteorological data you should check not only map distance but also elevation difference, surrounding terrain, whether the site is in a valley or on a ridge, whether it is affected by sea breezes, and whether winter snow reflection or frequent fog occurs. Choosing data with a climate formation similar to the site rather than simply the closest data point tends to stabilize PVSyst’s energy yield forecasts.

Tip 3 Check the length and representativeness of the period, not just single-year values

The third tip is not to treat single-year data as representative. Solar resources are not constant year to year. PVSyst also notes that annual integrated irradiance at a location varies from year to year and that sufficiently long records should be characterized by averages and variability. Looking only at single-year results risks selecting an unusually sunny year and making an overly bullish forecast, or picking a poor-weather year and becoming excessively conservative. Fixing a forecast without considering long-term averages is dangerous. PVSyst

A useful approach here is the use of TMY generated from multiple years of time series. PVSyst has a function to create a TMY from more than 10 years of hourly data, allowing multiple years’ representative conditions to be combined into a single year. When explaining a project forecast, it is also easier to justify why a particular year was chosen as representative. For detailed design and profitability checks, it is practical to look at TMY or the average of multiple-year simulations rather than concluding from a single-year file. PVSyst +1

Another important point is how the average is taken. Simply averaging hourly or daily data to make a “mean year” is not appropriate. PVSyst materials state that simply averaging hourly or daily data to create a mean year lacks physical meaning and that one should use a statistical method that meets TMY-like conditions. If multiple years of measured data are available, it is prudent to aggregate by month to create average months and then synthesize a time series, or to simulate each year individually and average the results. PVSyst

Tip 4 Check not only irradiance but also temperature, wind speed, and missing-data conditions

The fourth tip is not to judge meteorological data quality solely by irradiance. Meteorological variables handled in PVSyst simulations include global horizontal irradiance, diffuse irradiance, temperature, and wind speed. Even if irradiance matches, if temperature is too high, wind speed too low, or daytime data have many gaps, calculations of module temperature and effective generation will be distorted. Temperature and wind speed treatment matters especially when assessing high-temperature summer output or seasonal loss behavior. PVSyst

Data gaps have a larger impact than you might think. PVSyst’s meteorological data check treats rows missing key daytime irradiance values as invalid, and incomplete meteorological files are not recommended for simulation. Additionally, comparison with a clear-sky model can reveal time shifts, abnormal amplitude on clear days, unnatural daytime irradiance appearing at night, and data holes. Even if annual totals look plausible at a glance, poor internal quality can easily break down monthly or hourly reproduction. PVSyst +1

Be aware that having the necessary variables present and those values being reliable are separate issues. Even if item names are filled, unit errors, lack of sensor correction, or outlier contamination can make data unusable. PVSyst’s custom import includes quality-check mechanisms, so when using your own or externally obtained data, do not be satisfied merely with successful import—treat quality checks as part of the workflow. PVSyst +1

Also, for projects with only monthly values, it is reassuring to confirm that not only global components but also diffuse components are sufficiently available. If only global components exist and diffuse components must be estimated, added uncertainty is likely. Choosing meteorological data is not simply selecting the most accessible file, but judging to what extent you can reasonably provide the physical quantities PVSyst requires.

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Tip 5 Always inspect timestamp definitions and time-shift consistency

The fifth tip is not to take timestamp definitions lightly. PVSyst basically treats standard time as the reference and does not internally use daylight saving time. Therefore, if the meteorological data you import include daylight saving or use a different definition of record time, the same-looking irradiance can be misaligned with the solar position. This misalignment affects not only morning and evening but also incidence angles, shading, tilted-plane conversion, and temperature estimation in a cascading way. PVSyst +1

PVSyst’s quality checks make it easy to detect time shifts or anomalies exceeding about 30 minutes by inspecting monthly time shifts on clear days, the balance of morning/evening distributions, and consistency with a clear-sky model. If the data appear biased toward the morning or afternoon, the time labels may not match PVSyst’s assumptions. Time shifts are subtle, but if uncorrected and used for yield forecasts they tend to remain as unexplained discrepancies when compared with site performance. PVSyst +1

In practice, when you receive meteorological data, first confirm whether timestamps are in standard time, whether they refer to the start or end of the observation interval, and whether daylight saving conversion has been applied; then check whether the shape of clear-day curves in PVSyst’s graphs looks natural. Even if the annual irradiance sum appears reasonable, if the time axis is shifted, error becomes more apparent in detailed analyses. To avoid missing the mark in PVSyst energy forecasts, align not only numerical magnitudes but also the meaning of timestamps.

Tip 6 Don’t treat one dataset as the truth; use comparisons to capture a forecast range

The sixth tip is not to absolutize a single meteorological dataset. PVSyst has functions to compare multiple meteorological files, which can be used to understand interannual variation and dataset biases. PVSyst itself states that meteorology is a major source of uncertainty in simulations and that comparing multiple data helps grasp year-to-year variability and bias between datasets. Rather than deciding on a single file from the start, lining up multiple candidates and considering reasons for differences produces forecasts that are easier to explain. PVSyst +1

PVSyst materials explain that available meteorological data are not a strict unique solution and that differences arise depending on location, period, and creation method. Comparison results often show differences of a few percent to about ten percent relative to the mean, and it is not easy to determine in advance which dataset most closely represents reality. That is why, in practice, it is better to express forecasts as a range—“the forecast using this data is at this level” and “under other conditions it could be this much lower”—which makes later explanations easier. PVSyst

A recommended approach is to run PVSyst with at least two different meteorological datasets at the project’s early stage and compare annual energy, monthly distribution, summer high-temperature periods, and winter low-irradiance periods. Then select a primary dataset based on proximity of terrain conditions, length of period, completeness of variables, and quality-check results. With such a selected dataset, you can more easily explain internally and to the client why you adopted those numbers. The major value of this comparison is that it increases not only the numerical forecast but also confidence in the forecast.

Summary

To avoid missing the mark on energy yield forecasts in PVSyst, it is important to judge whether the meteorological data are appropriate for the project, not merely whether they can be input. Monthly values, synthesized time series, TMY, and measured or reanalysis time series each have strengths and weaknesses. Look beyond distance to the site—check elevation, terrain, and horizon conditions; verify the length and representativeness rather than relying on a single year; and inspect not only irradiance but also temperature, wind speed, missing-data conditions, and timestamp definitions—these form the foundation for forecast accuracy.

Finally, do not place absolute trust in a single dataset; compare multiple candidates and think in ranges. PVSyst is a useful tool, but if the meteorological data entered are not appropriate, results may look clean but be impractical for real-world use. Conversely, if you have a sound rationale for selecting meteorological data, your ability to explain forecasts will greatly improve. Getting energy yield forecasts right is not only about tweaking loss rates in detail but also about conducting careful initial meteorological data selection.

Note that even if meteorological data accuracy is improved, if understanding of on-site terrain, orientation, and distant shading is vague, the overall forecast accuracy is limited. If you want to solidify site coordinates, elevation, and on-site conditions early, leverage tools such as LRTK (iPhone-mounted GNSS high-precision positioning device) to organize site coordinates and terrain conditions before aligning PVSyst assumptions, which will help produce more practice-ready energy yield forecasts.