【How to Import Meteorological Data into PVSyst｜6 Steps for Beginners】

Table of Contents

• Why importing meteorological data into PVSyst is important

• Step 1: Organize location information for candidate sites

• Step 2: Decide which type of meteorological data to use

• Step 3: Create a site in PVSyst and link it to the meteorological conditions

• Step 4: Check the format before importing external meteorological data

• Step 5: Check the validity of the imported meteorological data

• Step 6: Apply to the simulation conditions and review the results

• Common pitfalls beginners encounter when importing meteorological data

• The importance of on-site verification and positioning to enhance reliability in practical work

• Summary: Being mindful of meteorological data accuracy makes PVSyst usage more reliable

Why importing weather data into PVSyst is important

When calculating the energy yield of a photovoltaic system in PVSyst, meteorological data serve as the starting point for the simulation. Energy yield is not determined solely by the installed PV capacity. It is decided by multiple overlapping conditions: solar irradiance, ambient temperature, wind speed, the effects of precipitation and snowfall, solar access conditions due to topography, shading from surrounding obstacles, and the system’s tilt angle and azimuth. Among these, solar irradiance and temperature form the basis of annual energy production, so choosing the wrong meteorological data can substantially change the overall predicted value.

For example, even when assuming the same power generation system, the annual output differs between regions with high solar irradiance and those with low solar irradiance. Also, in regions with high temperatures, it is necessary to account for the output reduction caused by temperature rise of the photovoltaic modules. In PVSyst, such conditions are read in as meteorological data, and the generation is calculated based on monthly or hourly values. Therefore, importing meteorological data is not merely an initial setup but an important process that influences design accuracy.

A common mistake beginners make is choosing meteorological data solely because it is close to the candidate site, without sufficiently checking environmental differences such as elevation differences, coastal areas, mountainous areas, urban areas, and snow-prone regions. Even if the meteorological data point is near the candidate site, if the terrain or elevation differs significantly, actual solar radiation and temperature conditions may change. Especially in mountainous areas, valleys, coastal zones, planned development sites, and slopes, it is important not to judge based only on planar distance but to confirm the conditions including the local environmental factors.

Also, the meteorological data imported into PVSyst affect simulation-result briefing materials, internal review documents, materials for financial institutions, and initial assessment documents for power generation projects. Being able to explain which meteorological data were used, why that location was chosen, and what checks were performed after import will reduce rework in later stages. Meteorological data are not finished once they are read in; they must be handled through the process of verifying their suitability for the design conditions and candidate site conditions.

When learning to use PVSyst, it is more important to understand the relationship between the candidate site, the meteorological data, and the simulation conditions than to try to find perfect data from the outset. If you can sequentially confirm where the system will be installed, which location’s weather data to use, and whether that data is suitable for predicting annual power generation, your overall operation of PVSyst will become more stable.

Step 1: Organize location information for candidate sites

Before importing meteorological data into PVSyst, first organize the location information for the candidate site. Because meteorological data are tied to a specific location, you cannot select appropriate data if the candidate site's latitude, longitude, elevation, and terrain conditions remain ambiguous. Beginners tend to open the PVSyst interface and start searching for meteorological data right away, but it is smoother to整理しておく to organize the on-site conditions beforehand.

The first thing I want to confirm is the latitude and longitude of the candidate site. In solar power generation simulations, solar elevation and azimuth and the annual distribution of solar irradiance are greatly affected by location. If the latitude changes, the sun’s movement changes, and if the longitude or region changes, the meteorological conditions also change. For large development sites or planned power plant sites, it is common to treat a point near the center of the site as a representative point, but when the site is large or there are elevation differences, care is needed in how the representative point is chosen.

Next, check the elevation. Elevation affects temperature and atmospheric conditions, so it is important when validating meteorological data. Even when using nearby weather observation stations, a large elevation difference from the candidate site can result in different temperature and snowfall conditions. In particular, in mountainous areas, plateaus, valleys, and slopes, proximity alone may not be sufficient. When setting a candidate site in PVSyst, be sure to check not only latitude and longitude but also elevation.

In addition, organize the surrounding environment of the candidate site. If there are mountains, forests, buildings, transmission facilities, slopes, or planned development areas nearby, they can affect solar irradiance conditions. Meteorological data itself represents regional solar irradiance and temperature, but actual power generation is also influenced by local shading and terrain. It is important not only to import meteorological data but to consider it in connection with subsequent shadow analysis and layout planning.

When organizing candidate site information, it's useful to compile the project name, site name, representative coordinates, elevation, site overview, the coordinate system to be used, and the drawings or survey results referenced. After creating the site in PVSyst, it will be easier to explain why that meteorological data was chosen. When comparing multiple candidate sites, ensuring each site has the same standard set of information makes it easier to compare power generation.

Importing meteorological data into PVSyst begins not just with operations in the software, but with organizing candidate site information. By carrying this out carefully, the subsequent steps of meteorological data selection, import, checking, and verification of simulation results become a consistent, integrated workflow.

Step 2: Decide the type of meteorological data to use

After organizing the location information for candidate sites, you next decide which type of meteorological data to use. In PVSyst there are several approaches: obtaining data from representative meteorological databases, loading meteorological files prepared externally, or formatting and importing on-site observational data. Which one you choose depends on the project's purpose, the stage of assessment, the required level of accuracy, and the degree of accountability.

In the initial assessment stage, it is common to use general meteorological data for the area around candidate sites to estimate annual energy production. At this stage, because the main objective is to compare multiple candidate sites and layout options, it is important to use data that can be compared under the same conditions. Rather than focusing too much on improving the accuracy of the meteorological data alone, the goal is to understand differences between candidate sites, differences in system size, and the effects of tilt angle and azimuth.

On the other hand, at stages closer to project planning and detailed design, the basis for meteorological data becomes more important. Predicted annual power generation figures may be used for financial projections, equipment design, grid interconnection studies, and internal approval materials. Therefore, it is necessary to select appropriate meteorological data for the candidate site and verify which period of data was used and whether there are any anomalies in the monthly or hourly values.

There are meteorological datasets based on monthly averages and those based on hourly values. Monthly data are easy to handle and suitable for preliminary assessments, but they are limited when it comes to examining hourly power generation fluctuations, shading effects, temperature changes, and equipment constraints in detail. Hourly data contain much more information and are suitable for more detailed simulations, but they require checks on file formats and the consistency of fields, handling of missing values, and verification of units.

What beginners should be careful about is not to judge the quality based solely on the name of the meteorological data. Even data for the same region may differ in how they were created, their time period, resolution, correction methods, and the types of solar irradiance provided. Before importing into PVSyst, check which values correspond to global (total) irradiation, whether direct and diffuse components are included, whether the values are for a horizontal plane or a tilted plane, and whether temperature and wind speed are included.

Also, when using on-site observation data, if the observation period is short it may not be representative of the long-term average. Short-term observations are useful for understanding local characteristics, but unless you check whether the year had more or less solar radiation than a normal year, there can be a bias in long-term power generation forecasts. In practice, long-term regional data and on-site observation data are sometimes considered together.

When using PVSyst, it is important to first clarify the project stage and decide whether it is for a preliminary assessment, detailed design, or for explanatory materials. Then, by choosing the type of meteorological data to use, decisions in later stages will be less likely to vary.

Step 3: Create a site in PVSyst and link it to the meteorological conditions

Once you've decided which meteorological data to use, create a site in PVSyst and link it to the meteorological data. In PVSyst, when creating a project you set the installation location and assign the meteorological data corresponding to that site. For beginners, site setup and meteorological data configuration may appear to be separate tasks, but in reality they are closely connected.

First, in the project's site settings, enter the candidate site's latitude, longitude, and elevation. If the coordinates entered here are incorrect, they will affect solar position calculations and the selection of meteorological data. In particular, be careful about the sign of latitude and longitude, differences between degrees-minutes-seconds and decimal degrees, and confusing east vs. west and north vs. south. Even for projects within Japan, an incorrect coordinate input format can cause the location to be treated as a completely different place.

When you create a site, select meteorological data close to that site or specify a weather file to import from an external source. In PVSyst, by linking the meteorological data with the site information, you can use that data in subsequent simulations. What is important here is to check not only the distance between the candidate site and the meteorological data location, but also the elevation difference and regional characteristics.

For example, if the candidate site is in a mountainous area and a nearby meteorological data point is in a plain, even if the distance is short, temperature, snowfall, and cloud formation tendencies may differ. Conversely, even if the distance is somewhat greater, a location with similar topography, elevation, and climate zone may be more appropriate. When linking a site in PVSyst, do not mechanically select the nearest data available on the screen; instead, judge by comparing it with the candidate site conditions.

Also, the way you name locations is important in practice. If there are similar candidate sites or multiple proposals, you may not be able to tell which meteorological data was used when you look back later. Including information such as the project name, candidate site name, the type of meteorological data, and the creation date or review stage in the location name makes management easier. In particular, when comparing multiple meteorological datasets, standardizing naming rules helps prevent rework.

The task of associating meteorological conditions in PVSyst is not something that ends after a single setup; it is often reviewed several times until the design conditions are finalized. If the candidate site's coordinates change, if the elevation changes due to site development plans, or if you decide to use different meteorological data, be sure to recheck the location settings and meteorological data settings.

Beginners tend to treat creating a site and importing meteorological data as separate tasks, but in practice it's more natural to handle them within the same workflow. Correctly defining the candidate site and linking appropriate meteorological conditions to that site is the foundation for obtaining reliable energy yield calculations in PVSyst.

Step 4: Verify the format before loading external weather data

When importing meteorological data prepared externally into PVSyst, check the file format and fields in advance. In PVSyst, when reading meteorological data you need to ensure that fields such as date (year, month, day), time, irradiance, temperature, and wind speed are correctly recognized. Simply preparing the file is not sufficient; if units, column order, missing values, or the handling of time are not appropriate, unnatural values can be mixed into the data after import.

The first thing to confirm is the time resolution of the data. Whether it is hourly, daily, or monthly data will change how it is handled in PVSyst. For hourly data, a one-year dataset will typically have many rows, with irradiance and temperature listed for each time stamp. You should also check whether the date and time are recorded in separate fields or combined in the same column, and whether the time indicates the start or the end of the interval.

Next, check the types of solar irradiance. Meteorological data include several types, such as global horizontal irradiance, direct irradiance, diffuse irradiance, and tilted-surface irradiance. When importing into PVSyst, you must correctly specify which column corresponds to which value. Treating horizontal values as tilted-surface values or confusing the direct and diffuse components will make the simulation results unrealistic.

Confirming units is also important. Solar radiation is often expressed as an amount of energy per unit time, temperature is in degrees Celsius, and wind speed is generally treated as a value per second. However, units and aggregation methods may differ depending on the data provider or how the data were created. If you need to convert units to match PVSyst input fields, verify that the converted values fall within realistic ranges.

Be careful of missing and anomalous values. External meteorological data may contain missing values due to instrument outages, communication failures, data interpolation, aggregation errors, and so on. Missing values may be left blank, indicated by specific symbols, or filled with extremely large or small values. Before importing into PVSyst, verify how missing values are represented and consider correcting or excluding them as needed.

Also, pay attention to the time reference. Depending on whether the meteorological data are recorded in standard time, in a reference such as Coordinated Universal Time (UTC), or reflect a scheme like daylight saving time, the temporal distribution of solar irradiance may be shifted. If the times are shifted, you can end up with the unnatural situation where daytime solar irradiance is assigned to the morning or evening, which will also affect the time distribution of power generation.

Before importing external weather data, it is reassuring to open the file in a spreadsheet program and check the column names, units, number of rows, period, missing values, timestamps, and the maximum and minimum of solar irradiance. You may notice anomalies after importing into PVSyst, but checking in advance will speed up identifying the cause.

For beginners, checking the format of weather files may seem like a tedious task. However, PVSyst's simulation results are influenced by the quality of the input data. By making it a habit to inspect the contents of the data before importing it, you can more easily prevent problems later such as energy output being too high or too low, or abnormal monthly trends.

Step 5: Check the validity of the imported meteorological data

After importing meteorological data into PVSyst, do not immediately proceed to simulation; first verify the validity of the imported data. If you skip this check, you may calculate annual energy production based on incorrect data and later spend a lot of time tracing the cause. Beginners tend to be reassured by a simple "import successful" message, but in practical work post-import checks are essential.

First, check whether the annual solar radiation is not significantly out of line with the regional characteristics of the candidate site. If the solar radiation is extremely high or low, there may be problems with the radiation units, the specified column, a time offset, or the handling of missing values. Use monthly graphs or listings to verify whether the summer and winter trends look natural, whether the effects of the rainy season or snow season are reflected, and whether the data contradicts the area's climatic character.

Next, check the ambient temperature data. In solar power generation, higher ambient temperatures raise module temperatures and reduce output. Therefore, if the temperature data are anomalous, they will also affect energy output and loss assessments. If the temperature is constant throughout the year, shows extremely high values in winter, or remains abnormally low in summer, this may indicate swapped data columns or a unit error.

If wind speed data are included, also check the range of the values. Wind speed can affect estimates of module temperature. If all values are close to zero or extremely large values occur frequently, there may be a problem with the data ingestion settings. However, because wind speed data can have more missing values and variability than solar irradiance or air temperature, decide how to handle them based on the objectives of the project.

It's also important to look at monthly trends. In solar power generation simulations, you should check not only the annual energy production but also the monthly generation. If there is a problem importing weather data, you may get results where generation is extremely high or low in specific months. Checking the monthly solar radiation and temperature in the imported weather data and comparing them with the simulation results can help detect problems early.

Furthermore, we will reconfirm the relationship between the candidate site and the meteorological data location. Even if the data are correctly loaded in PVSyst, if they are not appropriate for the candidate site, their practical validity cannot be considered sufficient. We check distance, elevation difference, terrain, whether the site is coastal or inland, susceptibility to mountain effects, presence or absence of snowfall, and, if necessary, perform comparisons with other meteorological data.

When comparing multiple meteorological datasets, it is important not to judge solely by differences in annual power generation. Consider the monthly trends in solar irradiance, temperature conditions, the spatial relationship to the candidate site, how the data were generated, and the intended use.

Rather than choosing the dataset that shows higher power generation, it is important to choose the dataset that more accurately reflects the actual conditions of the candidate site.

Checking the validity of imported meteorological data is not an advanced task to be performed only after becoming familiar with operating PVSyst. Rather, it is a basic task that should always be carried out from the beginner stage. Once you can confirm that the meteorological data have been imported correctly, your understanding of the simulation results deepens and it becomes easier to explain the factors behind increases or decreases in power generation.

Step 6: Apply to the simulation conditions and verify the results

After importing and validating the meteorological data, they are applied to the PVSyst simulation settings and the power generation calculation is performed. At this stage, not only the meteorological data but also the installed capacity, module tilt angle, azimuth angle, array configuration, PCS capacity, loss conditions, shading effects, and so on are configured. The meteorological data are combined with these conditions and reflected in the final power generation.

When you run a simulation, first check the annual energy production. However, judging based solely on the annual production is insufficient. Review monthly generation, irradiance, temperature losses, shading losses, conversion losses, and so on, and verify that the trends in the weather data are consistent with the results. Checking whether months with high irradiance correspond to higher generation, whether temperature losses increase in summer, and whether generation drops in winter or during rainy periods will deepen your understanding of the results.

If the power generation is higher than expected, check whether the irradiance in the meteorological data is overestimated, whether shading settings are insufficient, or whether loss conditions are set too low. Conversely, if the power generation is lower than expected, check whether the irradiance in the meteorological data is underestimated, whether weather conditions different from the candidate site are being used, or whether equipment settings or loss conditions have been set excessively.

The important thing in using PVSyst is not to view simulation results as mere numbers, but to interpret them in relation to the input conditions. If you change the meteorological data, the energy yield changes; if you change the tilt or azimuth, the energy yield also changes. Even if the meteorological data are imported correctly, incorrect settings for other conditions will cause the results to deviate. Conversely, even if the system conditions are correct, if the meteorological data are inappropriate the results become hard to trust.

In practice, comparative simulations using different meteorological data are sometimes performed. For example, by comparing a case using standard meteorological data, a case using data from another location near the candidate site, and a case based on on-site observations, you can grasp how differences in weather conditions affect power generation. This makes it easier to explain the range and risks of power generation forecasts.

After checking the simulation results, record the name of the meteorological data used, the location, the period, the main conditions, and what you verified. This is so that if design changes or re-evaluations occur later, you can trace under which conditions the results were calculated. Rather than relying solely on the PVSyst project file, leaving the rationale for the meteorological data in internal review notes and design documents also makes reviews and handovers easier.

The task of importing meteorological data into PVSyst only becomes meaningful in practice once you proceed to run the simulation. By treating the steps of importing, verifying, applying, and evaluating results as an integrated workflow, operating PVSyst functions not merely as data entry but as quality control for power generation forecasting.

Common Pitfalls Beginners Encounter When Importing Meteorological Data

When importing meteorological data into PVSyst, there are several points where beginners tend to stumble. The most common is assuming that choosing data close to the candidate site is sufficient. Indeed, distance is important, but meteorological conditions vary depending on topography, elevation, differences between land and sea, the influence of mountains, and the effects of urbanization. You need to check whether the data matches the actual environment of the candidate site rather than judging by distance alone.

Another common mistake is mixing up the types of solar irradiance. Meteorological data include irradiance on the horizontal plane, the direct component, the diffuse component, and irradiance on tilted surfaces. When importing into PVSyst, mistaking which column represents which value can significantly change the energy yield. Especially when using external files, always check the column names and units, and avoid assigning unclear fields based solely on guesswork.

Time shifts are another important consideration. With time-series data, if the time reference is shifted, the peak in solar irradiance may end up occurring at a different time instead of during the actual midday. Even if looking only at annual generation shows little obvious discrepancy, this can affect the evaluation of time-based generation distribution, shading impacts, and equipment constraints. After importing the data, it is important to confirm that solar irradiance is higher during daytime.

The handling of missing values is another aspect that is easily overlooked. If blank cells or special values are imported as-is, solar radiation can be zero for certain periods or anomalous values may appear. PVSyst may issue warnings, but it will not automatically detect every problem. Check the data ranges before and after import, and verify that there are no unnatural points in monthly or annual trends.

Also, there are cases where, even after updating the meteorological data, old data remain in the simulation conditions. When handling multiple sites or meteorological datasets in PVSyst, you may end up running calculations while still referencing previous data instead of the intended dataset. Before running a simulation, always check which meteorological data are selected for the current project.

Furthermore, when creating multiple proposals it is important not to confuse differences in meteorological data with differences in equipment conditions. For example, if you want to compare different layout proposals but use different meteorological data for each one, you will not know whether the differences in power generation are caused by the layout or by the weather conditions. Clarify the purpose of the comparison, and decide whether to fix the meteorological data or deliberately vary it before you begin work.

When importing meteorological data into PVSyst, what matters more than the procedure itself is the habit of checking. By verifying, in order, the coordinates, elevation, data types, units, time stamps, missing values, monthly trends, and consistency with simulation results, you can more easily prevent major mistakes.

The Importance of On-Site Verification and Positioning for Enhancing Reliability in Practical Operations

Correctly importing meteorological data into PVSyst is important, but in practice there are many cases where relying only on the software’s data is insufficient. At candidate sites for photovoltaic power plants, factors such as topography, surrounding obstacles, extent of land development, existing roads, slopes, trees, and existing structures affect energy yield and design conditions. Even if the meteorological data are appropriate, if on‑site conditions are not adequately understood, discrepancies can occur between the simulation results and the actual construction plans.

Especially when examining shading effects, it is necessary to accurately grasp the surrounding mountains, buildings, trees, and ground elevation after development. PVSyst allows you to set shading conditions, but if the underlying local topography and the positions of objects are ambiguous, the evaluation of shading losses will also be ambiguous. It is important to verify on site not only the representative coordinates of candidate sites but also the site boundaries, planned equipment placement locations, positions of obstacles, and differences in elevation.

What is useful here is the concept of positioning for acquiring high-precision location information on site. LRTK can be used as a high-precision GNSS positioning device that can be attached to an iPhone, allowing you to obtain coordinates on site and use them to organize location information related to candidate sites and equipment layouts. Before and after handling meteorological data and power generation simulations in PVSyst, accurately recording on-site representative points, site boundaries, objects that cause shading, elevation verification points, and so on makes it easier to organize the basis for design conditions.

In evaluating solar power generation, it is important to connect desk-based meteorological data with actual on-site conditions. In addition to setting candidate sites in PVSyst, importing meteorological data, and calculating energy yield, accurately knowing the site's coordinates and elevations makes it easier to improve the accuracy of shading analysis, layout planning, and pre-construction checks. Verification through on-site positioning is especially effective for projects with large sites, undulating terrain, or where the topography changes before and after land development.

PVSyst is a powerful tool for power generation simulation, but the accuracy of the input conditions is also influenced by the quality of on-site verification. In addition to correctly handling meteorological data, accurately identifying the candidate site's location and topography makes it easier to carry out assessments that are more practical and applicable to real-world work.

Summary: Being mindful of the accuracy of meteorological data leads to more consistent use of PVSyst

The process of importing meteorological data into PVSyst is not simply the act of loading a file. It involves a series of tasks: organizing the location information of candidate sites, deciding which type of meteorological data to use, creating the site in PVSyst, verifying the format of external data, checking validity after import, and incorporating it into the simulation results. Understanding this workflow makes it less likely that beginners will get confused when using PVSyst.

What is particularly important is the awareness that meteorological data form the foundation of power generation forecasts. If the assumptions about solar irradiance and temperature are off, no matter how precisely you set the equipment conditions, the reliability of the results will not improve. It is important not only to choose data close to the candidate site, but also to check elevation differences, terrain, regional characteristics, data period, units, missing values, and the handling of timestamps.

At the beginner stage, it becomes easier to use in practice if you first memorize six steps as a template. Organize candidate sites, select meteorological data, link them to the locations, verify the format, check after importing, and read the simulation results. By repeating this flow, operating PVSyst can be understood not as mere screen input but as the task of managing the quality of power generation forecasts.

Also, to make PVSyst results more representative of real-world conditions, it is essential to verify on-site conditions in addition to meteorological data. Accurately understanding the site's location, elevation, surrounding obstructions, sources of shading, and the extent of site development will clarify the connection between meteorological data and design conditions. By using an iPhone-mounted GNSS high-precision positioning device like LRTK, you can more easily carry out candidate site verification, layout planning, and organization of shading factors based on high-precision location information acquired on site.

Importing meteorological data into PVSyst is the entry point for solar power generation simulations and a crucial process that underpins the reliability of the results. By combining the correct selection and import of meteorological data with an understanding of local site conditions, the credibility of generation forecasts is enhanced, making consistent decision-making easier from planning and design through pre-construction verification.