7 Key Points to Avoid Mistakes When Selecting Meteorological Data in the PVSyst Manual

Table of Contents

• The importance of checking meteorological data in the PVSyst manual

• Prerequisites to check first when selecting meteorological data

• Understand the difference between representative-year data and measured data

• Important points to check in solar radiation data

• Impact of Air Temperature and Wind Speed on Power Generation

• Confirm the distance to the installation site and the terrain conditions.

• Data formats and precautions when loading

• The approach of making decisions by comparing multiple data points

• Common mistakes in selecting meteorological data

• Recording methods to make analysis results easier to explain

• Summary

The importance of checking meteorological data in the PVSyst manual

The purpose of learning meteorological data selection from the PVSyst manual is not simply to memorize how to operate the software. In photovoltaic power generation simulations, the quality of the meteorological data you input has a major impact on the analysis results. No matter how carefully you set system capacity, azimuth, tilt, and loss conditions, if the underlying assumptions about solar irradiance and temperature are far from reality, the expected power generation will also be significantly off.

Many people who use PVSyst do so to estimate power generation, compare design proposals, assess project viability, or prepare evidence they can present to clients or internally. Among these factors, meteorological data are a crucial starting point for a project. The manual covers topics such as data import and conversion and the handling of meteo files, but in practice you must make judgments about which data to choose and whether that data will satisfy your accountability.

What you should pay particular attention to is that there are different types of meteorological data. Satellite-derived data, data based on ground observations, representative-year data created from long-term averages, and measured on-site data collected over a certain period each have different characteristics. Data that are easy to work with in one project may lack credibility in another. In other words, it is important not only to check procedures in the PVSyst manual but also to understand the nature of the data and use them appropriately.

To avoid mistakes when selecting meteorological data, rather than trying to find perfect data from the start, it is important to choose data suited to the purpose of the analysis, understand its limitations, and be prepared to compare and correct it as necessary. When site conditions change—rooftop installations, ground-mounted installations, mountainous areas, coastal areas, snowy regions, high-temperature regions—the points that need to be checked also change. The aspects that are easy to overlook if you only follow PVSyst’s interface are precisely the ones that make a big difference in practice.

This article organizes seven items that you should pay particular attention to, from a practical perspective, when reviewing the selection of meteorological data in the PVSyst manual. From points where beginners are likely to stumble to aspects that become important for design studies and the preparation of explanatory materials, we explain them in order so they can be understood.

Prerequisites to Check First When Selecting Meteorological Data

When selecting meteorological data, the first thing to confirm is "what is the simulation for?" Even for the same PVSyst analysis, the required accuracy and depth of explanation differ for preliminary studies, basic design, detailed design, presentations to financial institutions, customer proposals, and verification of existing installations. For preliminary studies, you may use general typical-year data to get a rough estimate first. On the other hand, for evaluations related to investment decisions or contract terms, you need to verify the data source, period, correction methods, and consistency with the site.

When reading the PVSyst manual, it becomes easier to understand if you pay attention not only to the steps for loading data but also to the conditions you should check before loading. Specifically: the site's latitude and longitude, elevation, time zone, the type of irradiance data, whether temperature data are available, how wind speed data are handled, whether there are missing values, and the data period. If you start the analysis while these are unclear, it will be difficult later to explain the validity of the results.

For example, even with the same place name, solar irradiation conditions and temperatures differ between the north and south sides of a mountain, the coast and inland areas, and urban and suburban areas. Even if you can set the site in PVSyst, you cannot say the meteorological data reflects on-site conditions unless you confirm what actual area the weather data represents. Especially in hilly terrain or regions prone to cloud formation, differences can occur even when using nearby data.

Also, it is important to know whether the annual meteorological data used in the simulation represent long-term averages or are measured values for a specific year. If a particular year has higher solar radiation than the long-term average, the estimated energy yield may be higher. Conversely, using data from a year with unfavorable weather can lead to lower estimated generation. Because the figures produced by PVSyst depend on the assumptions behind the input data, it is important to make those assumptions explicit.

Failures in selecting meteorological data are often caused more by insufficient verification of assumptions than by operational errors. Before proceeding through the manual, organizing the analysis objectives, site conditions, and the required level of explanatory detail can reduce rework in later stages.

Understanding the difference between representative-year data and measured data

When handling meteorological data in PVSyst, beginners often get confused about the difference between representative year data and measured data. Representative year data is often used as a dataset reconstructed as an average year from long-term weather records, and is useful when examining long-term projections of power generation. On the other hand, measured data represents the weather conditions actually recorded during a specific period. It is not simply a matter of which is better; you need to choose between them depending on your objective.

The advantage of representative-year data is that it tends to reflect long-term trends. In solar power projects, there are many situations where you want to look not only at single-year generation but also at average generation over a long period. Therefore, data that represent average solar irradiation conditions are convenient for project feasibility assessments and design comparisons. However, because representative-year data do not literally represent any single actual year, differences can arise when simply comparing them with the actual performance in a particular year after operation starts.

The advantage of measured data is that it allows confirmation of the specific conditions observed at the site or in its vicinity. This is especially valuable in areas with complex terrain, regions where cloudiness is highly localized, or locations heavily affected by snowfall or fog. However, caution is needed when the measurement period is short. Measurements over a few months or about one year make it difficult to determine whether that period was typical (close to the long-term average) or an anomalous year. Relying solely on short-term measurements to assess long-term power generation can lead to overestimation or underestimation.

When reviewing the PVSyst manual, it is important not only to check whether the data can be imported but also to understand the nature of that data. When using representative-year data, verify that it aligns with the concept of long-term averages. When using measured data, check the measurement location, measurement period, sensor type, calibration status, handling of missing data, and treatment of outliers.

Also, in practice, representative-year data is sometimes compared with measured data. For example, even when on-site measurements are available, instead of using them as-is, they are compared with long-term data to establish an approach for adjusting to the long-term average. Understanding whether the on-site measured values are higher or lower than the long-term average makes it easier to explain the simulation results.

If you do not understand the difference between representative-year data and measured data, you will not be able to explain, when looking at results from PVSyst, "why the energy yield is high" or "why there is a discrepancy with other data." When selecting meteorological data, it is important to understand the meaning of the data before its format.

Important points to check in solar radiation data

The most important meteorological parameter for photovoltaic generation simulations is solar irradiance. When selecting meteorological data in PVSyst, you must always verify the type and consistency of the irradiance. Solar irradiance can be considered as global irradiance, direct irradiance, diffuse irradiance, etc., and the simulation’s internal processing changes depending on which values are included in the data.

Global horizontal irradiance is treated as an indicator representing the total solar radiation incident on a horizontal plane. Direct irradiance is the component that arrives directly from the sun, while diffuse irradiance is the component that reaches the surface after being scattered in the atmosphere. In PVSyst, these data are used to calculate the irradiance incident on inclined surfaces. Therefore, if the input data are inconsistent, the calculation of irradiance on inclined surfaces will also be affected.

When checking meteorological data, first check whether the annual solar radiation substantially deviates from local expectations. If it is clearly too low for an area with good insolation, or too high for an area that is often cloudy, review the site settings and data selection. However, it is important not to judge based on intuition alone; compare multiple datasets to confirm the trend.

Monthly solar irradiance is also important. Even if the annual value is the same, different seasonal distributions will change the monthly generation results. In particular, in snowy regions winter generation can drop significantly, and in areas strongly affected by the rainy season the solar irradiance in certain months can be low. When reviewing PVSyst's monthly results, you need to check not only the system-side losses but also whether the monthly trends in the meteorological data are reasonable.

When working with solar irradiance data, pay attention to extreme values and missing data. For example, if irradiance is recorded at night, if it is continuously zero during daytime, or if abnormally high values appear only during certain time periods, there may be a problem with data processing. Even if PVSyst can import the data, that does not necessarily mean the data are correct. After importing, it is important to check the graphs and monthly values to see if there are any abnormalities.

Also, when estimating tilted-surface irradiance from horizontal-plane irradiance, you need to view the results in combination with the azimuth and tilt settings. Even with the same meteorological data, irradiance on a tilted surface changes for south‑facing, east‑ or west‑facing, low‑tilt, and high‑tilt orientations. Rather than looking at solar irradiance data alone, checking it together with the design conditions is key to preventing failures.

Effects of Air Temperature and Wind Speed on Power Generation

When selecting meteorological data in PVSyst, many people focus on solar irradiance, but ambient temperature and wind speed also influence energy production. Photovoltaic modules generate more electricity the more sunlight they receive, but their output decreases as cell temperature rises. Therefore, even with the same solar irradiance, generation efficiency can differ between regions with high and low ambient temperatures.

When examining temperature data, it's important to check not only the annual average but also high-temperature conditions during summer. In hot regions and for rooftop installations, module temperature tends to rise, which can lead to larger temperature-related losses. If PVSyst shows large temperature losses, you need to verify not only the system/installation conditions but also whether the air temperature in the meteorological data is reasonable.

On the other hand, in cold climates lower temperatures can be advantageous from a temperature perspective. However, if there is snowfall or reduced winter solar radiation, you cannot judge electricity generation solely by the temperature advantage. It is important to consider temperature, solar radiation, snowfall, installation angle, and shading conditions comprehensively.

Wind speed data is another item that is easy to overlook. When there is wind, the modules are cooled, which tends to lower cell temperature. Therefore, wind speed can affect PVSyst’s temperature model. However, wind speed data is easily influenced by measurement height and the surrounding environment, and the wind speed included in meteorological data does not necessarily accurately represent the airflow around the actual modules.

For example, modules on a ground-mounted installation in a well-ventilated location and modules on a rooftop surrounded by walls and equipment will have different cooling conditions even when using the same regional wind speed data. When consulting the PVSyst manual, you should not take the wind speed included in meteorological data at face value; instead, be aware of how it relates to the installation configuration.

Air temperature and wind speed are sometimes not given as much attention as solar irradiance, but their importance becomes clear when looking at loss diagrams in analysis results. When temperature-related losses are large, it is necessary to separate whether the cause lies in the meteorological data, the installation configuration, ventilation conditions, or module characteristics. If you check temperature and wind speed at the stage of selecting meteorological data, it will be easier to explain the results later.

Confirm the distance to the installation site and the terrain conditions

One common pitfall when selecting meteorological data is underestimating the distance to the installation site. Even if you choose data from a nearby location in PVSyst, that location may not sufficiently represent the actual site. This is especially true in regions with complex terrain like Japan, where weather conditions can change with just a short distance.

In flat plains, similar patterns can occur over relatively wide areas, but caution is needed in mountainous areas, basins, coastal zones, riverine areas, and regions with elevation differences. Adjacent areas separated by a mountain may experience different cloud formation. Coastal areas can be affected by fog and sea breezes. Basins may see stronger heat in summer and more intense cold in winter. Such conditions cannot be judged by simple straight-line distance alone.

When checking the distance to the installation site, confirm where the meteorological data observation points or representative points are located. Close distance is important, but it is not sufficient on its own. You also need to consider whether elevation, terrain, climate classification, and the surrounding environment are similar. Especially for mega-solar or large-scale projects, even small differences in power generation can have a major impact on project viability, so the representativeness of the data should be checked carefully.

Even for rooftop installation projects, terrain conditions are not irrelevant. In urban areas there are effects such as the urban heat island, wind shielding by surrounding buildings, and localized shading. Even if meteorological data represents broad-area conditions, temperature and air circulation on the actual roof can differ. When entering system conditions into PVSyst, it is important to be aware of the differences between meteorological data and on-site conditions.

Elevation differences also affect temperature. If the elevation of the meteorological data point and the installation site differ significantly, the temperature conditions may differ. Higher-elevation locations tend to have lower temperatures, but they can also experience increased effects from snowfall and clouds. Selecting nearby data without considering elevation differences can lead to discrepancies in estimated temperature losses and expected winter power generation.

When verifying meteorological data settings in the PVSyst manual, do not rely solely on the data selection screen; make a point of separately checking the installation site's topography and surrounding environment. Whether you can explain the representativeness of the meteorological data is directly tied to the overall reliability of the analysis results.

Data Formats and Precautions When Loading

When handling weather data in PVSyst, attention must be paid to the data format. Weather data may be provided in formats built into the software or loaded as external files. If units, timestamps, the meanings of columns, missing values, or time zones are not set correctly, simulation results can be significantly affected.

First, what I want to confirm is the units. You must check whether the solar irradiance is given as hourly values or cumulative (integrated) values, and in what units it is expressed. Even if the numerical digits are correct, misinterpreting the units can cause the irradiance to be treated as unrealistic within PVSyst. You must also confirm whether the temperature is in Celsius (°C) or another unit, and whether the wind speed is in meters per second (m/s) or another unit.

The next important factor is time. Weather data can differ in whether a timestamp represents the start of a period, the end of a period, or the midpoint. You also need to confirm whether times are in local time or standard time, and how daylight saving time is handled. If timestamps are misaligned, the correspondence between the sun’s position and solar irradiance will be shifted, which can affect calculations of power generation and shadows.

When loading data into PVSyst, carefully verify the column assignments. If columns such as irradiance, air temperature, wind speed, or humidity are assigned incorrectly, the import may succeed but the analysis results will be unrealistic. Especially when using externally processed data, it is important not to rely solely on column names, but to check the ranges of the values and their temporal variations.

Handling missing and anomalous values is also important. Meteorological data can contain missing values due to sensor malfunctions or communication failures. If missing values are treated as zero, solar irradiance may be underestimated. Conversely, if abnormally high values are included, energy production may be overestimated. Before importing into PVSyst, you should verify data quality and decide on correction or exclusion policies as needed.

Also, when converting file formats, pay attention to character encoding, delimiter characters, and decimal-point notation. If comma, tab, and semicolon delimiters are mixed, columns may not be recognized correctly. The handling of decimal points and digit-grouping separators can also vary depending on locale settings. If loading fails despite following the manual, it is often not the software but the file format that is the cause.

Checking data formats is a mundane task, but mistakes made here affect the entire analysis. When verifying import procedures in the PVSyst manual, don’t stop at whether the data loaded; review the monthly values and graphs after import to confirm that the data are being reflected as intended.

Approach to Making Decisions by Comparing Multiple Data Sets

To avoid making mistakes when selecting meteorological data, it is important not to make a decision based on a single dataset. When multiple meteorological datasets are available for use in PVSyst, comparing each dataset’s annual solar irradiation, monthly solar irradiation, temperature, data period, and site conditions makes it easier to verify the validity of the selection.

When comparing multiple datasets, annual power generation can differ. Rather than simply asking "which one is correct," it is important to check why the difference arises. By examining whether the annual value of solar radiation differs, whether the monthly distribution in summer or winter differs, whether temperature conditions differ, or whether the site or elevation differs, you can understand the characteristics of the data.

For example, even if the annual solar irradiation is almost the same, differences in winter irradiation can affect the monthly power generation trends in snowy regions and mountainous areas. If there are differences in summer irradiation or temperature, temperature-related losses during high-temperature periods may differ. Reviewing PVSyst’s monthly results and loss diagrams to confirm the differences caused by meteorological data can help inform design decisions.

The purpose of comparison is not to select a convenient power generation figure. Rather, it is to understand how much variation arises from different data sources and to be able to explain the reasons for the data ultimately adopted. When presenting to customers, internal stakeholders, or financial institutions, showing "why this data was chosen" and "the comparison results with other data" increases the credibility of the analysis.

Creating multiple cases in PVSyst and comparing them by changing only the meteorological data is also effective. In this approach, keep the system capacity, azimuth, tilt, and loss conditions the same, and observe only the impact of the meteorological data. If you change conditions simultaneously, it becomes difficult to determine where the differences in power generation originate.

Also, when reviewing comparison results, we check not only annual electricity generation but also the performance ratio, monthly electricity generation, and trends in temperature losses and irradiation losses. Even if differences appear small in annual values, large differences in specific months can affect operational plans and financial projections. When selecting meteorological data, it is important to pay attention not only to averages but also to seasonal variability.

By cultivating the habit of comparing multiple data sets, you will be able to make reasoned judgments about PVSyst results instead of simply accepting them. This is a very important mindset for progressing from a beginner to a practitioner.

Common Pitfalls in Selecting Meteorological Data

One common mistake when working with meteorological data in PVSyst is adopting the initially displayed data as-is. When the software presents candidate datasets, they can give the impression of being automatically optimal. However, unless you actually check the location, period, and the characteristics of the data, you cannot say they are appropriate for your analysis purpose.

The next most common mistake is judging based solely on annual solar irradiance. Annual irradiance is important, but without looking at the monthly distribution and temperature conditions you cannot correctly understand seasonal variations in power generation. Confirming monthly trends is indispensable, especially for projects where winter generation is important or where summer peak output is prioritized.

It is common to overlook the difference between the installation site and the meteorological data location. Even if you choose data labeled with a nearby city name, if the actual installation is in a mountainous area or on the coast, solar radiation and temperature conditions may differ. Just because a place name is within the same region does not necessarily mean it is highly representative.

When loading external data, time offsets and unit errors are common. Mistakes such as mixing up the solar irradiance column, loading the temperature column as other data, using the wrong time zone, or treating missing values as zero can greatly alter analysis results. If you proceed without checking graphs and monthly values after loading, it becomes difficult to identify the cause later.

Also, when power generation comes out higher than expected, it is risky to adopt it simply as a favorable result. The higher the generation, the more necessary it is to check whether the meteorological data are overly optimistic, whether solar irradiance deviates from regional averages, and whether loss assumptions are insufficient. Conversely, when generation is lower than expected, the data may be overly conservative.

Failures in selecting meteorological data can be difficult to detect from the numbers alone after analysis. Even if PVSyst’s report shows clean results, if the assumptions behind the input data are inappropriate, their reliability as a basis for decision-making is diminished. That is why it is important to record what checks were made at the time the meteorological data were selected.

Recording methods to make analysis results easier to explain

When selecting meteorological data in PVSyst, it is important to record which data were used. Even if the analyst thinks they will remember, the rationale behind decisions can become unclear when the work is reviewed later or handed over to another person. This is especially important when comparing multiple cases: you need to clearly indicate which case used which meteorological data.

Items to be recorded include the dataset name, target location, latitude and longitude, elevation, data period, data type, type of solar radiation, annual solar radiation, monthly trends, temperature conditions, and reasons for selection. If external data were processed, it is also advisable to retain the original (pre-processed) data, the details of the processing, missing-data handling, unit conversions, and whether any time corrections were applied.

It can also be useful to be deliberate about case names and notes within a PVSyst project. For example, naming cases so that the meteorological data names and the adopted conditions are clear makes later comparisons easier. When generating reports, separately organizing the assumptions about the meteorological data will also help readers understand the results.

When explaining to clients or internally, it is not enough to simply say "calculated with PVSyst." It is important to be able to explain which meteorological data were selected and the reasons you judged that data to be appropriate for the installation site. If the rationale for data selection is clear, it lends credibility to the power generation figures.

Keeping records is also important for future reviews. When design changes, changes in equipment capacity, module changes, PCS changes, or layout changes occur, recalculating using the same meteorological data makes it easier to compare before and after the changes. Conversely, if you compare without noticing that the meteorological data has changed, you will not be able to tell whether the difference is due to equipment changes or to the meteorological data.

Selecting meteorological data is often regarded as behind-the-scenes work, but it is a crucial step that underpins the reliability of power generation simulations. By not only checking procedures in the PVSyst manual but also making a habit of recording the reasons for your selections, you can produce analysis results that are more usable in practice.

Summary

When verifying the selection of meteorological data in the PVSyst manual, it is important to understand not only the operating procedures but also the meaning of the data and its underlying assumptions. Meteorological data are the foundation of energy yield simulations, and if the selection is incorrect, the reliability of the results will be compromised no matter how carefully the system conditions are configured.

To avoid mistakes when selecting meteorological data, first clarify the purpose of the analysis and understand the differences between representative-year data and measured data. Then check the types of solar radiation, monthly trends, air temperature, wind speed, distance to the measurement site, topographic conditions, elevation differences, data formats, timestamps, units, and missing values. Furthermore, it is important to compare multiple datasets and be able to explain the reasons for the chosen data.

The results from PVSyst are calculated based on the input conditions. In other words, the quality of the results depends on the validity of the input conditions. In particular, meteorological data form the foundation of the entire analysis, so they should be checked carefully first. Whether the estimated energy production comes out higher or lower, first reviewing the assumptions about the meteorological data makes it easier to understand the reasons for the results.

In practical work, it is more important to select data that matches your objectives, understand its limitations, and organize approaches for comparison and correction as needed, rather than trying to choose perfect meteorological data. By treating data selection, loading, verification, comparison, and recording as a single workflow while using the PVSyst manual, the credibility of the simulation results is greatly enhanced.

Careful selection of meteorological data is not merely an input task, but a design decision to improve the quality of power generation forecasts. As the first step to mastering PVSyst, make sure to verify the seven meteorological data items and use them to produce evidence-based simulations.