6 Meteorological Data Settings to Avoid Confusion When Using PVSyst

PVSyst (officially styled PVsyst) is used for simulating the energy yield of solar power plants, and in these simulations not only system capacity and panel layout but also the meteorological data settings have a major impact on the results. If irradiance, temperature, wind speed, albedo, site information, or the way the data period is handled differ from the planned site, even the same plant design can produce different results for annual energy yield, monthly generation profiles, and perceived losses. This article organizes six meteorological data items that practitioners searching for "how to use PVSyst" are likely to be confused by, and explains the ways of thinking they should check before and after entering the data.

Table of Contents

• Why PVSyst weather data settings are important

• Match site information and coordinates correctly

• Check the types and units of solar irradiance data

• Understand temperature data and its impact on power generation

• Treat wind speed data as the assumption for temperature-related losses

• Make albedo and ground surface conditions closer to on-site conditions

• Verify data period and representativeness

• Consistency checks to perform after input

• Common mistakes in weather data settings

• Summary: connecting PVSyst usage with on-site surveys

Why PVSyst's meteorological data settings are important

When using PVSyst to assess the energy production of a solar power plant, most people initially focus on PV capacity, panel azimuth, tilt angle, shading effects, electrical losses, and so on. These are important factors that influence generation, but if the meteorological data loaded as the basis do not match the project site, the explanatory power of the simulation results will be limited.

Weather data form the foundation that indicates the solar irradiance, temperature, and wind environments a power plant will be exposed to. In solar power generation, higher solar irradiance generally increases power output, while an increase in module temperature reduces output. In addition, the way wind speed is handled and the installation conditions can change estimates of module temperature and assessments of temperature-related losses. Albedo, the reflectivity of the ground surface, can also affect results for installations with steep tilts, when considering bifacial modules, in snowy regions, or near bright paved surfaces or gravel-covered areas.

For practitioners who are unsure how to use PVSyst, what matters is not simply loading meteorological data but judging whether that data is appropriate for the planned site. Even if the import appears successful on-screen, if the coordinates are far off, the elevation is significantly different, the type of irradiance has been mistaken, or the monthly trends do not match local experience, such conditions can produce discrepancies in the analysis results that are difficult to explain.

Also, when evaluating solar power plants, it is important to be aware that generation figures can easily take on a life of their own. When annual generation is displayed, that number can appear to be a definitive value, but in reality it is an estimate based on input assumptions. In particular, meteorological data deal with natural conditions and therefore exhibit year-to-year variability and differences depending on how the data were produced. It is important to be able to explain which meteorological data were used, which location was referenced, and which period was treated as representative.

When configuring meteorological data in PVSyst, starting by covering six items—location information, solar irradiance, temperature, wind speed, albedo, and the data period—makes it easier to reduce omissions in your assessment. These appear as separate input fields, but in practice they are interrelated. For example, even if you adopt data with high irradiance, if the temperature and wind speed for the same site are not appropriate, the assessment of temperature losses can become unrealistic. If the coordinates or elevation are incorrect, the assumptions about irradiance and temperature will also be off. Verifying each item one by one contributes to the overall credibility of the results.

Align location information and coordinates correctly

The first items to check in the meteorological data settings are the site information and coordinates. In PVSyst, meteorological data are handled according to the plant’s location. If the entered latitude, longitude, elevation, time zone, etc. differ from the actual planned site, it can affect the solar position, time-series data, and the handling of solar irradiance conditions. In particular, when using data for similarly named places or nearby locations, it may look fine on the screen but actually be using conditions from a location that is distant from the planned site.

At solar power plant project sites, weather conditions can vary with topography and elevation even within the same municipality. In plains, mountainous areas, coastal zones, basins, and hilly terrain, the likelihood of fog, cloud movement, wind patterns, and temperature trends differ.

For inputs to PVSyst, it is not enough that the latitude and longitude do not differ greatly; you also need to check elevation differences and surrounding terrain to determine whether the meteorological data used is a reasonable substitute for the planned site.

In practice, it is safer to compile the planned site's coordinates from surveying results, map information, and on-site survey materials, and then reconcile them with the location settings in PVSyst. If you choose a location based solely on the address, the representative point may end up near the municipal office or the center of an urban area, which may not match the conditions for power plant plans in mountainous or suburban areas. When the power plant site is large, it is important not only to consider the center of the site but also to review the extent of earthworks and the array layout area when deciding which position to use as the representative coordinate.

Coordinate mix-ups can be caused by the order of latitude and longitude, east vs. west longitude, north vs. south latitude, the number of decimal places, and conversion between degrees–minutes–seconds and decimal notation. For projects within Japan, north latitude and east longitude are normally used, but if the input format is incorrect it may be processed as an unintended location. Even if the site is shown on the PVSyst screen, it is important to verify that the displayed position actually matches the planned site.

Time zones are another detail that’s easy to overlook. When handling time-stamped solar irradiance data, a mismatch in the time reference can shift the timing of generation peaks and break the consistency of impact assessments. Using monthly data can make time effects less apparent, but you need to be especially careful when importing time-resolved data or external datasets. Confirm whether the time reference is local time or standard time, and for overseas data whether it includes corrections such as daylight saving time, and make sure this matches the assumptions you’ll use when importing into PVSyst.

Site information is one of those items that, once set, tends to carry forward as the study progresses. However, even a small discrepancy in the initial site setup will affect subsequent descriptions of solar irradiation, temperature, shading, and power generation. When you are not yet familiar with using PVSyst, make it a habit to first verify the planned site's coordinates and elevation against another source and cross-check them with the input values before selecting meteorological data.

Confirm the types and units of solar radiation data

Among the meteorological data settings, solar irradiance is an important parameter that directly affects power generation. Because photovoltaic power generation converts solar irradiance into electricity, if the input irradiance changes, the annual energy production results will also change. However, even when referring to irradiance in a single word, there are multiple concepts such as global horizontal irradiance, diffuse horizontal irradiance, the direct component, and irradiance on tilted surfaces. When handling meteorological data in PVSyst, it is necessary to confirm which type of irradiance the data being loaded represents.

In practice, a commonly used approach is to convert the global irradiance incident on a horizontal plane to the plane of installation. What is important here is to understand which input values PVSyst assumes when performing its calculations. If values that have already been converted to a tilted plane are treated as horizontal irradiance, they can end up being converted twice, producing unrealistic results. Conversely, when using data that lack required components, it becomes difficult to explain the results unless you understand the assumptions behind the software’s internal processing and estimations.

Units for solar irradiance are also important. Whether the data are monthly cumulative values, instantaneous or cumulative values for each time of day, or daily averages affects how you should interpret the figures. If you import data with mismatched units, you may end up with extremely high or low power generation estimates. PVSyst may sometimes issue warnings or flag abnormal values, but it does not automatically detect everything. Before importing, you should check the annual totals and the relative magnitudes by month and confirm they do not significantly contradict the region’s typical seasonal trends.

In Japan, some solar power plants are located in regions that typically receive more solar irradiation in summer, but due to the rainy season, typhoons, snowfall, and winter cloudiness, there are regions where the seasonal variation is not straightforward. For example, in some areas solar irradiation may not increase as much as expected in summer despite high temperatures, while in winter the sun’s altitude is low but the clear-sky rate is relatively high. When using PVSyst, it is important to look not only at the annual total but also at the monthly peaks and troughs of solar irradiation. Even if the annual figures appear reasonable, an unnatural monthly distribution will change how seasonal generation and the capacity factor are perceived.

Solar irradiance data can be based on observations, estimates, multi-year averages, or datasets that closely match a particular year's actual conditions. Rather than one being always correct, it's important that the data matches the purpose of the study. In initial assessments, representative weather conditions are often used, while for detailed design and explanatory materials it is necessary to clarify the data's source and how it was prepared. When presenting PVSyst results internally or externally, organizing the type of solar irradiance data used, how it was created, the period covered, and the distance to the project site will make later explanations easier.

What you should avoid when configuring solar irradiance is selecting high-irradiance data solely to make the projected energy production look better. This may produce attractive short-term results, but if large discrepancies appear during actual operation, planning-stage accountability will be questioned. PVSyst is a tool for calculating energy production and does not automatically guarantee the validity of input data. Precisely because irradiance is the basis of energy production, it is important to choose conditions that are realistic for the project site.

Understanding Temperature Data and Its Impact on Power Generation

In PVSyst's meteorological data settings, not only solar irradiance but also ambient temperature data are important. In solar power generation, higher irradiance increases energy production, while higher module temperatures reduce power output. Therefore, even with the same irradiance, temperature-related losses will differ between high-temperature and low-temperature regions. When using PVSyst, you should not treat temperature as a mere secondary parameter; you need to verify it as an input condition that affects energy production.

When looking at temperature data, it is important not to judge based only on the annual average. Reductions in solar PV output are affected especially during periods when solar irradiance is strong and module temperature tends to rise. Even with the same annual average temperature, actual temperature-related losses can vary depending on tendencies toward high temperatures in summer, diurnal temperature differences, temperature differences due to elevation, and the influence of sea breezes or mountain winds. If monthly or hourly temperature data are available, examining how much the periods of high irradiance overlap with periods of high temperature will deepen your understanding of the results.

When the elevation of the planned site differs from that of the meteorological data’s representative location, attention should be paid to temperature discrepancies. In general, temperature tends to decrease with increasing elevation, but this is not determined simply and depends on topography and regional conditions. If the representative location is an urban area and the planned power plant site is in a mountainous area, temperature and wind conditions may differ. To determine whether the temperature data imported into PVSyst adequately represent the planned site, it is necessary to check not only the coordinates but also the elevation and surrounding terrain.

Be mindful of anomalies in temperature data. When viewed as monthly averages, check whether there are months that are unusually high or low, whether the annual seasonal cycle looks natural, and whether the relationship with solar radiation seems reasonable. When handling time-of-day data, missing values, interpolations, or timing offsets may be present. Even if the data format is well formatted, if temperatures are stuck at a constant value, diurnal variation is too small, or seasonal variation is weak, you should recheck whether the data are suitable for power generation calculations.

In PVSyst, the estimation of module temperature involves ambient temperature, incident irradiance, installation conditions, and the settings of the thermal loss model. In models or settings that use wind speed, wind speed also influences the temperature estimate. Therefore, even if ambient temperature is entered correctly, if it is not consistent with wind speed and installation conditions, the interpretation of temperature losses may appear inconsistent. Differences such as ground-mounted, roof-mounted, or rear-ventilated installations change how heat is dissipated from the module. Ambient temperature data should be considered together with the plant’s surrounding environment and installation type.

In practice, it is useful to check how much temperature loss appears in the power generation simulation results and to verify that the temperature data settings are consistent with their description. If temperature losses are too small in warm regions, too large in cold regions, or the monthly loss trends do not match the seasonal changes in irradiance or temperature, these are reasons to review the input conditions. When using PVSyst, it is important not just to input the temperature but to confirm how it is reflected in the calculation results.

Treat wind speed data as an assumption for temperature loss

Wind speed data tends to be overlooked compared with solar irradiance and air temperature. However, in solar power plant energy output simulations, the thermal loss model and settings used affect how module cooling by wind is treated. In locations with good airflow, module temperatures may be less likely to rise, while in locations with weak wind where heat tends to accumulate, temperature-related losses may be greater.

When handling wind speed in PVSyst, the first thing to check is at which location, at what height, and for what period the wind speed data represent. Wind speed is easily affected by topography and surrounding obstacles, and even within the same region values vary depending on observation site conditions. Whether the plant is located on open land, surrounded by trees or buildings, in a valley, or along the coast will change the ventilation conditions around the modules. Whether it is acceptable to directly apply the wind speed from a representative site to the project site must be decided while considering local conditions.

Also, wind speed varies with the measurement height. If the wind speed in the meteorological data was measured at a height close to standard meteorological observation conditions, it may differ from the wind speed actually present around the modules. It is not always necessary to apply fine corrections in PVSyst inputs, but at a minimum it is important to be aware of whether the wind speed data you are using is causing the plant’s thermal environment to be over- or under-estimated.

When wind speed data are insufficient, it may be appropriate to consider using monthly values, default values, or settings that do not rely heavily on wind speed. In that case, it is important not to overstate the accuracy of the results. Providing detailed explanations of temperature losses while the assumptions about wind speed are weak undermines the justification. In internal documents and materials for clients, clearly state the source of the wind speed data and how it was treated, and perform sensitivity checks as needed. For example, examining how annual power generation and temperature losses change when thermal loss or wind speed conditions are varied makes it easier to explain the scope of the wind speed settings' impact.

The relationship between wind speed and installation conditions is also important. For ground-mounted installations where air can flow easily behind the array versus installations placed close to a roof surface, module temperature rises differently even at the same meteorological wind speed. Likewise, ventilation assumptions differ between a development site plan with open surroundings and a plan with windbreak forests or slopes around it. When using PVSyst, it is important not to judge based only on the wind speed in the meteorological data, but to check temperature losses in conjunction with the layout plan, racking conditions, and surrounding topography.

Wind speed settings may not produce as large a difference in energy yield as solar irradiance, but they provide important supplementary information when explaining temperature-related losses. When you review the results of an energy production simulation and find that temperature losses are larger or smaller than expected, check not only the ambient temperature but also the wind speed and the thermal loss model settings. By looking at solar irradiance, ambient temperature, wind speed, and installation conditions together, you can interpret PVSyst results more practically.

Align albedo and surface conditions with on-site conditions

Albedo is a parameter that indicates how much sunlight the ground surface and surrounding surfaces reflect. In PVSyst meteorological data settings and power production assessments, it is relevant when considering the impact of reflected light from the ground on the modules. In particular, albedo settings can affect results for installations with large tilt angles, evaluations of bifacial modules, snowy regions, bright ground surfaces, and sites with extensive pavement or gravel.

For typical ground-mounted, single-sided installations, the impact of albedo can be smaller compared with solar irradiance and shading effects. However, just because it is small does not mean it can be unconditionally ignored. The reflection conditions change depending on whether the actual ground surface is grass, soil, gravel, pavement, close to water, or covered with snow. Even if you proceed in PVSyst with the default values, it is desirable to confirm that those values do not greatly contradict the ground surface at the planned site.

In regions with snowfall, surface reflectance can increase during winter. However, it is dangerous to judge that increased reflection from snow alone will make power generation more favorable. While snow increases reflectance, it also leads to other issues such as accumulation on module surfaces, accessibility, maintenance, mounting height, shading, and system downtime. When setting albedo in PVSyst, you should consider not only the positive effects of increased reflection but also the snow-region-specific losses and operational conditions separately.

In grassy areas and in bare ground immediately after land development, surface conditions can change over time. Even if soil is exposed right after construction, once operations begin weed-control treatments and vegetation management can alter the surface reflectance. In regions where the ground surface changes seasonally, it may be worth considering monthly albedo settings. It is not always necessary to set these in fine detail, but if an impact on power generation is expected, it is important to confirm whether a single annual setting is sufficient.

One thing to watch when setting albedo is entering a high value that does not match actual site conditions in order to make the estimated energy yield look higher. Increasing the reflectance can, under some conditions, lead to higher predicted generation. However, if you cannot justify that value on site, the credibility of the assessment will be reduced. In particular, for bifacial modules or designs that rely on reflected light, you need to verify the ground surface material, array spacing, racking height, and how light reaches the module rear surface together.

When using PVSyst, it is important to treat albedo not as a mere numerical input but as an item that reflects the ground surface conditions at the site. By linking it to site photographs, development plans, extent of paving, weed-control plans, presence or absence of snow, and operation and maintenance policies, it becomes easier to explain why a particular value was chosen. Checking albedo as part of the meteorological data settings improves the consistency between the power generation simulation and the actual site conditions.

Confirm the data period and representativeness

Of the six meteorological data settings, the data period and representativeness are the ones most often overlooked. Whether the weather data loaded into PVSyst represents long-term average conditions, the actual conditions of a specific year, or a representative year derived from processing multiple years of data will change the meaning of the results. When reviewing simulated power generation figures, you need to understand what period of weather is being assumed.

In feasibility studies for solar power plants, there are many situations where you want to use conditions close to a typical year. On the other hand, when verifying the actual performance of an operating plant, you may use data that reflect the actual weather conditions for a specific period. If the objectives differ, the appropriate meteorological data also change. Treating preliminary planning, detailed design, presentation materials, and post-operation performance comparisons in the same way makes the relationship between expected and actual values difficult to understand.

When checking representativeness, it is important to look not only at annual solar radiation but also at monthly variability. Conditions such as an unusually sunny year, a shorter rainy season, major impacts from typhoons or prolonged rain, or heavy winter snowfall will be reflected in single-year data. Treating these as if they were long-term averages can bias the expected power generation. Conversely, when using long-term averages or representative-year data, it is necessary to explain that they will not perfectly match the actual performance of any specific year.

An important practice when using PVSyst is to record the characteristics of the meteorological data after loading it. Organizing details such as the period the data is based on, whether the values are monthly or hourly, whether missing data were interpolated, where the representative location is, and how far it is from the project site will be helpful when reviewing results later. If the input conditions are recorded, it will be easier to trace why the generation came out as it did even if the person in charge changes.

Data period and representativeness are important even when explaining things to external parties. When submitting power generation simulation results, recipients check not only the annual generation figures but also the underlying assumptions. If you cannot explain the representativeness of the meteorological data, the reliability of the results is likely to be questioned. Confirming representativeness is indispensable, especially for large-scale projects or projects assuming long-term operation, because variability in weather conditions can affect project viability.

In practice, it can be useful to compare multiple meteorological datasets as needed. Rather than judging based on a single dataset, compare data from nearby locations or different periods to check whether there are significant differences in annual solar irradiance or monthly trends. If differences exist, do not simply decide which is correct; instead, determine which dataset is easiest to justify in light of the site’s topography, elevation, climate, and intended use. Because PVSyst allows you to vary input conditions for analysis, performing sensitivity checks can help with practical decision-making when representativeness is uncertain.

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After importing meteorological data into PVSyst, do not assume the setup is complete; always verify consistency. Even if the input values are formally correct, they may not match the project site or design conditions. The consistency check should confirm that location information, solar irradiance, temperature, wind speed, albedo, and the data period do not contradict one another.

First, check the annual solar radiation and the trend of monthly solar radiation. Verify that the month-to-month increases and decreases do not greatly contradict the regional climate and that there are no extreme values. Next, assess whether the seasonal variation in temperature is natural. Check whether the relationship between periods of high solar radiation and high temperatures is reasonable, whether winter temperatures are not too high for local conditions, and whether there is any inconsistency when considering elevation differences.

Wind speed should be checked together with the results for temperature loss. If the wind speed is set high but the temperature loss is large, or if the wind is weak yet the temperature loss is extremely small, the installation conditions and the assumptions of the heat loss model should be reviewed. Of course, results are determined by multiple conditions, so you cannot judge by wind speed alone. However, checking the relationship between wind speed, air temperature, and installation type makes it easier to notice unnatural inputs.

Albedo, even when its impact on results is relatively small, should be checked for consistency with on-site conditions. Verify whether the configured values match the actual ground surface—grass, gravel, pavement, snow, etc. In analyses of bifacial configurations or designs that emphasize reflection conditions, albedo can have a larger effect, so ground surface conditions need to be checked more carefully.

Also, on PVSyst’s results screen, check not only the annual energy production but also the monthly energy production, the breakdown of losses, temperature losses, solar irradiance losses, and the balance among influencing factors. Even if the annual production is close to the expected value, if the loss breakdown looks unnatural, the input conditions may be incorrect. For example, if solar irradiance is high but production does not increase, check whether the cause lies in temperature losses, shading, orientation, tilt, electrical losses, or similar factors. It is important to look at the connection between meteorological data and the system’s equipment conditions, not just the weather data.

Recording the input conditions is also indispensable for consistency checks. If you keep a record of the meteorological data name, acquisition date, period, representative site, coordinates, elevation, key settings, items you changed, and the reasons for adopting them, you can later recreate the analysis. The more familiar you become with using PVSyst, the faster you will be at navigating the interface, but if your records are vague it can cause problems during internal reviews or when explaining things to clients. Working not only on the power generation figures but also on managing the underlying assumptions improves the quality of practical work.

Common Mistakes in Weather Data Configuration

One common mistake when setting up meteorological data in PVSyst is simply accepting the first dataset you can load. The absence of errors does not mean the data are appropriate for the project site. Problems such as distant coordinates, different elevation, the wrong type of solar radiation, or a data period that does not match the intended purpose can be difficult to detect at the time of import.

Another common mistake is placing too much emphasis on solar irradiance alone. Because irradiance is important for solar power generation, there can be a temptation to choose data with high irradiance. However, if such data are adopted without checking consistency with temperature, wind speed, terrain conditions, and the data period, the projected power output may become overly optimistic. In power generation simulations, it is important to select conditions that can be justified on site, rather than stacking up favorable factors.

Mixing up units and data formats is also something to watch for. Confusing monthly values, daily averages, hourly values, and cumulative values can lead to drastically different results. Even if the number of digits appears to match, the units may be different. Before importing into PVSyst, simply checking annual totals and monthly trends to see whether there are any extreme values can prevent many mistakes.

Also, treating meteorological data and design conditions as separate issues can lead to failure. For example, even with the same meteorological data, the resulting power generation will vary depending on tilt angle, azimuth, array spacing, shading, racking height, and installation method. Even if the meteorological data alone are set correctly, if the design conditions do not match the site, it becomes difficult to explain the results. Conversely, even if the design conditions are entered correctly, if the meteorological data are off, the assumptions behind the power generation are undermined.

When multiple people in a company use PVSyst, differences in configuration rules between individuals can become a problem. If one person uses representative-year data while another uses single-year data from a nearby location, it becomes difficult to compare projects. Standardizing within the company the criteria for selecting meteorological data, the checks to perform after data entry, and the method for recording results makes it easier to reduce variation in analysis quality.

To avoid getting lost when using PVSyst, it is important to have decision criteria, not just procedural steps. If you only memorize which screen to enter what on, it becomes difficult to handle exceptional cases. On the other hand, if you understand the meaning of the six items—location, solar radiation, air temperature, wind speed, albedo, and data period—you will be less likely to lose sight of the points you need to check even if the data used or project conditions change.

Summary: Connecting PVSyst Use with On-site Surveys

In PVSyst's meteorological data settings, it is important to carefully verify six items: site information, solar irradiance, temperature, wind speed, albedo, and the data period. These are the assumptions for the power generation simulation and determine how the natural conditions of the project site are reproduced. Looking only at the on-screen input work may seem difficult, but in practical workflow it is a process of correctly identifying the project site, confirming the assumptions for irradiance and temperature, supplementing wind and ground surface conditions, and being able to explain the representativeness of the data.

What practitioners should be especially aware of is not to treat PVSyst’s results as the definitive answer. Simulation results are estimates based on the input conditions. If the meteorological data are taken from a location far from the project site, the period does not match the intended purpose, or the ground surface conditions differ from the actual site, the predicted energy output will be affected. For that reason, it is necessary to check the input conditions before reviewing the results and to inspect the loss breakdown and monthly trends after reviewing them.

When standardizing the use of PVSyst within your company, it is effective to decide not only the procedures for setting meteorological data but also how to keep verification records. If you organize the meteorological data used, representative site, coordinates, elevation, period, solar irradiance, temperature, wind speed, albedo, and reasons for selection, it will be easier to review the analysis results later. It will also make it easier to explain why calculations were performed under those conditions in customer presentations and internal approval processes.

Also, meteorological data settings cannot be completed by desk work alone. Local topography, surrounding obstacles, ground surface, snow cover, wind flow, post-construction management policies, and other factors can be difficult to judge from documents alone. By linking on-site surveys, measurements, and design information with PVSyst input conditions, the credibility of the simulation results is increased. If you want to improve the accuracy of power generation assessments, it is important to advance software operations and confirmation of on-site conditions together.

When you're unsure about the meteorological data settings in PVSyst, first go back and check these six items. Is the location correct, is the type and unit of solar irradiance correct, is the temperature close to local conditions, does the wind speed contradict the explanation of temperature loss, is the albedo appropriate for the ground surface, and does the data period match the purpose of your analysis? Simply reviewing them in this order makes it easier to reduce input mistakes and insufficient explanations.

In planning a solar power plant, it is important to consider not only power generation simulations but also to connect understanding of on-site conditions, organizing design assumptions, post-construction verification, and operational records. If you want to link the PVSyst study results to decisions closer to the field, organize on-site surveys, surveying results, site development plans, and operations and maintenance information under the same assumptions to bridge desk-based studies and field practice. By carefully checking the six items of meteorological data, you can more easily improve the planning accuracy and explanatory power of a solar power plant.