5 meteorological data settings to check in the PVSyst manual

Table of Contents

• Why configuring meteorological data in PVSyst is important

• Meteorological Data Setting 1: Consistency between Location Information and Coordinates

• Weather Data Settings 2: Types of Solar Radiation Data and How to Read Them

• Weather data setting 3: Temperature data and its impact on power generation

• Meteorological Data Settings 4: Granularity of Monthly and Hourly Data

• Meteorological Data Settings 5: Checking for Missing and Anomalous Values

• Practical workflow to keep in mind when reading the PVSyst manual

• Common mistakes and workarounds in meteorological data configuration

• Precautions when using simulation results in presentation materials

• Summary

Why Meteorological Data Settings Are Important in PVSyst

When performing a solar power generation output simulation using PVSyst, the first thing you should check is the meteorological data settings. The output of a solar power system is not determined solely by panel capacity, azimuth, tilt angle, and inverter specifications. In practice, conditions such as how much solar irradiance the site receives, how high temperatures rise, and how seasonal weather patterns change play a major role in estimating annual energy production.

The purpose of reading the PVSyst manual is not simply to learn how to operate the interface, but to understand how the input conditions are reflected in the calculation results. In particular, meteorological data are the fundamental information representing the project's site conditions. If you proceed with simulations without thoroughly checking this, no matter how carefully you set equipment capacity and layout, the estimated generation potential that underlies your assumptions is likely to be off.

For example, even if the same solar panels are installed at the same angle, annual power generation will differ between regions with high solar irradiance and regions with frequent cloudiness. Also, in areas with high temperatures, conversion efficiency tends to drop due to rises in solar panel temperature. In other words, meteorological data is not mere background information but the very starting point of the simulation.

In PVSyst, you load meteorological data and, while associating it with site information, handle conditions such as solar irradiance, air temperature, and, in some cases, wind speed and humidity. What beginners often struggle with is which data to choose, whether the chosen data are appropriate, and what to check after making the settings. When reading the manual, it is important not just to follow the on-screen explanations but to understand the meaning of each setting item one by one.

In this article, we organize and explain the meteorological data settings you should check in the PVSyst manual into five items that are particularly important in practice. Whether you are using PVSyst for the first time or have performed simulations in the past, reviewing how you handle meteorological data will make it easier to improve your ability to explain analysis results.

Meteorological Data Setting 1: Consistency of Location Information and Coordinates

When configuring meteorological data in PVSyst, the first thing you should check is the consistency between the site information and the coordinates. In photovoltaic simulations, the installation site's latitude, longitude, elevation, and time zone are basic parameters. These pieces of information affect solar altitude and azimuth, the incident angle of solar irradiance, and seasonal generation trends, so they should not be treated lightly as mere address information.

When consulting the PVSyst manual, it is important to view not only the screen for selecting meteorological data but also the project site settings screen. If the location indicated by the meteorological data is far from the actual planned installation site, the meteorological conditions may not reflect the local reality. This is especially true in mountainous areas, coastal areas, basins, and urban areas, where temperature and solar radiation conditions can differ even over short distances.

For example, even within the same prefecture, cloud formation tendencies and temperature trends can differ between coastal and inland locations. In addition, areas at higher elevations tend to have lower temperatures, and the effects of snowfall and fog should be considered. Even when using meteorological data from a nearby location in PVSyst, it is essential to verify whether that location represents the on-site conditions.

Errors in entering coordinates are also a common problem. These can include swapping the order of latitude and longitude, misclassifying east versus west longitudes or north versus south latitudes, or coordinates obtained from an address that point to nearby roads or a different parcel instead of the center of the site. For projects within Japan, major mistakes are often easy to spot on the screen, but for overseas projects or when comparing multiple locations, there is a risk of proceeding with incorrect coordinates.

When using the PVSyst manual, it's helpful to pay attention not only to the workflow for registering a location but also to how you will verify the geographic information after entering data. If you intend to use the simulation in internal reports or when explaining results to customers, being able to show how close the meteorological data point is to the actual installation site will enhance the credibility of the analysis.

Also, when comparing multiple candidate sites, it is important to standardize the sources and acquisition conditions of the meteorological data. If one candidate uses high-precision near-site data while another uses representative data from a distant location, the fairness of the comparison is compromised. This is because it becomes difficult to tell whether differences in power generation between sites are caused by actual meteorological conditions or by differences in data selection.

Consistency between site information and coordinates may seem like a minor detail at the start of a simulation, but if corrected later it can require redoing the analysis. When configuring meteorological data in PVSyst, it is standard practice to confirm the site address, coordinates, elevation, and the reference meteorological data station at the initial stage, and to record the basis for those settings.

Weather Data Settings 2: Types of Solar Radiation Data and How to Read Them

One of the most important meteorological data in PVSyst is solar radiation. Because photovoltaic power generation converts energy from the sun into electricity, how much solar radiation is received directly determines estimates of annual energy production. Even when consulting the PVSyst manual, it is essential to understand the types of solar radiation data and what they mean.

There are several ways to describe solar radiation: global horizontal irradiance, direct irradiance, diffuse irradiance, and irradiance on tilted surfaces. What beginners often confuse is using values shown on a screen without sufficiently checking what they represent. The meaning in calculations differs depending on whether it refers to radiation incident on a horizontal surface, the component arriving directly from the sun, or components scattered by clouds or the atmosphere.

Solar panels are typically installed at a fixed tilt rather than horizontally. Therefore, using meteorological data on the horizontal plane, it is necessary to calculate the solar irradiance incident on the panel surface. In PVSyst, the irradiance received on a tilted surface is estimated based on the input meteorological data and the configured settings. This process involves factors such as azimuth and tilt angles, solar position, and the treatment of the diffuse component.

When reading the PVSyst manual, your understanding will deepen not only by learning how to import solar irradiance data but also by checking how different types of data are converted and used. Rather than simply thinking “it’s fine because I entered irradiance,” it is important to be aware which surface the irradiance value refers to, what averaging period it represents, and whether it is a measured value or an estimated one.

In practice, the selection of meteorological data can change annual power generation by several percentage points. In the solar power business, a difference of a few percent can affect profitability and decisions about equipment design. Therefore, rather than selecting only data that show higher generation, it is necessary to choose data with a sound basis and be able to explain the underlying assumptions.

When looking at solar irradiance data, check not only the annual total but also monthly trends. Even if the annual irradiance is similar, the generation pattern changes depending on whether it is higher in summer or remains stable in winter. For self-consumption solar power systems, compatibility with the demand curve is also important, so it is useful to understand monthly and hourly trends.

Also, if there are months with extremely high or low solar irradiation, you should check for data anomalies or regional characteristics. Determining whether the variation is a reasonable reflection of the actual climate or an unnatural value caused by missing-data imputation or conversion processing will improve the quality of the simulation. Using the graphs and monthly tables in PVSyst and developing a habit of visually checking them makes it easier to spot input errors and unnatural data.

Solar irradiance data are at the heart of PVSyst's generation simulations. When reading the manual, it's important not only to memorize the field names but also to review them with an awareness of which data are used in which calculations.

Weather Data Setting 3: Temperature Data and Its Impact on Power Generation

In solar power generation simulations, attention often focuses on solar irradiance, but temperature data are also very important. Solar panels tend to produce more power with stronger irradiance, while an increase in module temperature tends to reduce their power-conversion efficiency. Therefore, if temperature settings are inappropriate, the estimated power output may be inaccurate.

When checking the meteorological data settings in the PVSyst manual, you should always check how the temperature data is handled. In general, simulations estimate module temperature from the ambient air temperature while taking into account solar irradiation conditions and the effects of wind. Because module temperature affects power generation efficiency, the temperature data also feeds into the loss analysis on the results screen.

In regions where temperatures are high in summer, temperature-related losses can become large even when solar radiation is abundant. Conversely, in regions with lower temperatures, favorable solar radiation conditions can be advantageous in terms of efficiency. However, in cold regions there are other factors such as snowfall and shorter sunshine duration, so you cannot judge performance based on temperature alone. What is important is to understand the power generation characteristics by combining solar radiation and temperature.

When checking temperature data, pay attention not only to the annual average temperature but also to monthly maximum and minimum trends. In particular, summer highs, winter lows, and the diurnal temperature range affect equipment operating conditions and seasonal variations in power generation. It is important to review the monthly temperature trends in PVSyst and confirm that they do not deviate significantly from the local climate experience.

For example, if the site is an urban rooftop project but temperature data from a nearby suburban observation station is used, the results may differ from the actual rooftop environment. Rooftops tend to retain heat more than ground level and are also affected by surrounding buildings and paved surfaces. You should not rely solely on PVSyst’s standard meteorological data; instead, carefully evaluate temperature conditions according to the project’s characteristics.

Also, you should pay attention to the units and formats of temperature data. Importing without confirming the differences between Celsius and Fahrenheit, between monthly averages and hourly values, or whether values have been gap-filled can lead to unnatural analysis results. When reading the PVSyst manual, it is advisable to also check the data file format and the field mapping used at import.

The influence of ambient temperature affects not only power generation but also equipment selection. In high-temperature environments, considerations are required for inverters, cables, racking, and the heat dissipation conditions of the installation space. PVSyst is a tool for power generation simulation, and the temperature losses and seasonal trends obtained from it can also be used as inputs for design reviews.

Temperature data, while less conspicuous than solar irradiance, is an important parameter that affects the validity of simulation results. When using the PVSyst manual to check how to configure it, make sure you understand on which screens temperature is entered and which results it influences.

Weather Data Setting 4: Granularity of Monthly and Hourly Data

The fourth item to check when handling meteorological data in PVSyst is the data granularity. Granularity refers to the time unit in which the data are organized. Whether the data are monthly averages, hourly data, or finer time-series data will affect what can be represented in the simulation.

When you read the PVSyst manual, you’ll find explanations on importing and generating meteorological data, but beginners often assume “data is data once it’s loaded.” However, in practice differences in data granularity affect analysis accuracy and the way results are explained. This is especially true for shading effects, time-of-day analysis of self-consumption, peak output considerations, and inverter clipping assessments — in these cases, how much temporal variation you can handle is important.

Monthly data are convenient for rough estimates of annual power generation and for preliminary assessments, but they have limits in representing intra-day variability and short-term weather changes in detail. For example, in regions where mornings are often cloudy and afternoons tend to be clear, areas prone to afternoon thunderstorms in summer, or sites where mountain shadows frequently appear at dawn and dusk, monthly averages alone may not adequately reflect actual conditions.

On the other hand, using time-series data makes it possible to handle daily changes in solar irradiance and temperature in greater detail. This makes it easier to assess the shape of the generation curve, the degree of alignment with self-consumption, output curtailment when the system is oversized, and the effects of shading. In particular, in recent years there has been an increase in projects where time-of-day generation levels are important—not only for selling power but also for self-consumption, battery integration, and deployment at customer facilities.

When using time-series data in PVSyst, you also need to check the quality of the data. Because time-series data contain a large amount of information, they may include missing or anomalous values. Also, if the handling of time zones or timestamps is off, consistency with the sun position can be disrupted, causing the timing of peak solar irradiance to appear unnatural. While consulting the PVSyst manual, be careful not to overlook points such as the data format, time settings, and whether times are in local time or standard time.

Data granularity should be chosen according to the analysis objective. For rough, early-stage estimates of power generation, representative meteorological data may be sufficient. On the other hand, financial-institution cashflow assessments, proposals to clients, detailed design, and evaluations of battery storage or self-consumption may require finer-grained data. You do not need to use more detailed data than the objective requires, but using data that is too coarse for the purpose will weaken the persuasiveness of the results.

Also, when comparing multiple scenarios, it is important to align the data granularity. If only one option uses hourly data while another uses monthly data, the meaning of the differences becomes hard to interpret. When conducting comparative analyses in PVSyst, it is desirable to match the type, period, granularity, and source of the meteorological data, and then examine differences in equipment conditions and layout.

Data granularity affects not only the precision of a simulation but also how you explain the results. If the results are based on monthly data, it is natural to focus explanations on annual and monthly trends. If the results are based on hourly data, it becomes easier to explain generation curves, peaks, and overlaps with demand. When reading the PVSyst manual, it is important not simply to follow the setup steps but to take the perspective of choosing the data granularity that matches your analysis objectives.

Meteorological Data Configuration 5: Checking for Missing and Anomalous Values

When configuring meteorological data in PVSyst, the last thing you should always check for is missing or anomalous values. No matter how reliable the data you use, file conversion, import, gap-filling, or time-setting offsets can introduce unnatural elements into the dataset. If you skip quality checks of the meteorological data, simulation results may look plausible at first glance but could actually have been computed on incorrect assumptions.

Missing values are cases where data that should be present is absent. For example, this can include the solar irradiation for a particular month being blank, part of the hourly data being zero, or temperature data missing for consecutive periods. In PVSyst, warnings or confirmation screens may appear when importing data, but ultimately the user themselves must verify the validity of the data.

Anomalous values are numbers that are unlikely in reality or that are extreme outliers compared with surrounding data. For example, situations that warrant attention include solar irradiance being high at night, extremely low temperatures recorded during daytime in midsummer, winter solar irradiance being unnaturally high, or identical values appearing throughout the year. Such anomalous values can affect annual power generation and loss analysis.

When reviewing the PVSyst manual, be sure to note not only the procedure for importing meteorological data but also which screens allow you to view the data after import. Check the monthly graphs, time-series displays, statistics, warning messages, etc., and verify there are no values that would be implausible for the region’s climate. You may notice anomalies more easily in graphs than by looking at numerical tables alone.

Particular attention should be paid to the treatment of zero values. Solar radiation being zero is natural at night, but if it remains zero for long periods during the daytime, it may indicate that missing values are being treated as zero. Likewise, if temperature is consecutively zero, it may be the result of missing-value processing rather than actual measurements. A zero value is not necessarily abnormal, but it should be checked against the time of day and season.

Unit mistakes during data conversion are also a common problem. Errors in the units of irradiance, the handling of time integration, or confusing monthly totals with daily averages can cause large discrepancies in energy yield. It is important to compare the original source data before importing into PVSyst with the values shown after import to confirm that the scale and units match. If the energy yield is much higher or lower than expected, you should question not only the system parameters but also the units of the meteorological data.

Checking for missing or anomalous values is part of simulation quality control. As you become more accustomed to operating PVSyst, you may be tempted to skip verification steps in order to produce results more quickly, but you should be particularly cautious when those results are used for client explanations or design decisions. The reliability of the analysis is supported not by flashy report screens but by how thoroughly the input data were checked.

Practical workflow to keep in mind when reading the PVSyst manual

When using the PVSyst manual, rather than memorizing individual setting items separately, it is more efficient to understand them in line with the workflow of real-world practice. For weather data settings, the flow is: first decide the installation site, then select the weather data to use, check the contents of that data, make corrections or comparisons as necessary, and finally verify that it is consistent with the simulation results.

First, clarify the project’s target location. Verify the address, site boundaries, coordinates, and elevation, and make sure they match the location information in PVSyst. At this stage, cross-check against on-site survey materials, drawings, and map information to avoid registering an incorrect location. If you select meteorological data while the location remains ambiguous, the entire subsequent configuration will become unstable.

Next, select the meteorological data to be used. Whether you use data provided in PVSyst or import data from external sources, confirm which location, which period, and which parameters the data include. The important point here is not whether the data are well-known, but whether they are appropriate for the project in question.

After that, check the trends in solar irradiance and temperature. Look at annual values, monthly values, seasonal variations, peak periods, and low periods to determine whether they deviate significantly from the local climate expectations. In practice, it is also effective to compare with past similar projects and with power generation records from the surrounding area. Rather than judging solely by PVSyst results, combining them with on-site knowledge and existing data makes it easier to detect configuration errors.

Furthermore, align the data granularity with the analysis objectives. Depending on whether a rough estimate of annual energy production is sufficient, whether you need to examine monthly financial balances, or evaluate time-of-day self-consumption, the level of detail required in the data will vary. When reading the PVSyst manual, being aware of which settings are appropriate for which objectives will lead to an understanding that goes beyond mere operational procedures.

Finally, verify the relationship between the simulation results and the input data. If the energy production is higher than expected, check not only the panel performance and loss settings but also whether the irradiance data are biased high. If the energy production is low, review not only shading and equipment losses but also the selection of meteorological data and the temperature conditions. Developing the habit of returning from the results to the inputs for verification will greatly improve the quality of PVSyst analyses.

Common Mistakes and How to Avoid Them in Weather Data Configuration

One common mistake when configuring meteorological data in PVSyst is assuming that choosing the nearest data means it is correct. Proximity is important, but it is not always sufficient. Conditions other than distance—such as a mountain in between, different sea breeze influences, large elevation differences, or differing thermal environments between urban and suburban areas—also affect energy yield. To avoid this, check not only distance but also topography, elevation, climate classification, and the surrounding environment.

Another mistake is not recording the source and period of the weather data. Even if you remember at the time you created the simulation, when you review it weeks or months later you may not be able to tell which data you used. Especially when comparing multiple scenarios or handing files off within the company, you should note the name of the weather data used, the period, and the settings.

Units and types of solar irradiance can also be misinterpreted. If you proceed without checking whether the values refer to irradiance on a horizontal plane or on a tilted plane, and without confirming how the direct and diffuse components are being treated, the assumptions behind the energy yield will be unclear. It is important to consult the PVSyst manual and configure the settings only after understanding what each item means.

Time-zone misalignment is another easily overlooked mistake. When working with time-series data, inconsistencies in handling local time, standard time, or daylight saving time can cause the solar irradiance peak time and the sun’s position to become misaligned. As a result, the power generation curve may appear unnatural, and there may be discrepancies in assessing shading effects. After importing, it is useful to check the shape of the irradiance curve to confirm that the peak occurs during daytime and that irradiance is zero at night.

Also, a failure you should avoid is issuing a report without verifying data quality. Because PVSyst reports are neatly organized and easy to read, they can appear to be reliable documents if you only look at the results. However, if there are problems with the input data, the conclusions can be unstable even if the report looks polished. Before submitting simulation materials, it is advisable to establish a check step to reconfirm the meteorological data settings.

A useful countermeasure is to have the same verification procedure each time. If you fix the sequence as site verification, solar irradiance verification, temperature verification, data granularity verification, and missing/abnormal value checks, you can reduce variability between projects. The more familiar someone is with PVSyst operations, the more important it is not to skip the verification steps and to make them a habitual part of quality control.

Points to Note When Using Simulation Results in Presentation Materials

When using simulation results created with PVSyst for customer proposals, internal reviews, materials for financial institutions, design reviews, and similar purposes, explaining the meteorological data settings is extremely important. Even if you present only the generation figures, viewers of the document cannot judge the validity of the results unless they understand the underlying assumptions. In particular, for solar power generation—because it involves estimating future output—it's essential to explain what kind of meteorological data was used.

When preparing documents, it is advisable to succinctly organize the types of meteorological data used, the target locations, the data period, and the main weather conditions. You do not need to include all detailed technical explanations in the main text, but at a minimum you should be able to explain which locations the data are based on, what the trends in solar radiation and temperature are, and whether any special corrections were applied.

Also, the simulation results are not definitive values but estimates based on certain assumptions. Weather conditions vary from year to year. Some years have many clear days, while others are affected by prolonged rain, typhoons, snowfall, or extreme heat. Therefore, when explaining PVSyst results, it is necessary to make clear that they are standard estimates based on the specified meteorological data and do not guarantee single-year performance.

Selecting weather data that conveniently produces excessively high generation figures undermines long-term credibility. Even if attractive numbers can be presented during the proposal stage, a large gap between those figures and actual operational performance will give rise to accountability. PVSyst is a powerful simulation tool, but ensuring the validity of the input conditions is the user's responsibility.

When presenting multiple simulation scenarios, it is essential to standardize the meteorological data conditions. If you want to compare differences in panel angle, capacity, or equipment configuration, the weather conditions must be kept identical. If you also change the meteorological data, you will no longer be able to identify the causes of differences in power generation. In comparison materials, it is important to clearly state what was changed and what was kept fixed.

Furthermore, in presentation materials, verifying monthly generation as well as annual generation increases persuasiveness. While annual values are easy to understand, seasonal variations become harder to see. Showing monthly generation trends makes it easier to convey differences between summer and winter, compatibility with self-consumption, and an image of equipment operation. If you can explain the relationship between the meteorological data settings and monthly generation, the overall credibility of the materials will be enhanced.

Summary

When checking the meteorological data settings in the PVSyst manual, it is important not only to memorize the screen operations but also to understand how the input data affect the power generation simulation. Meteorological data form the foundation that determines the potential of solar power generation, and carefully checking site information, solar irradiance, temperature, data granularity, and missing or anomalous values can enhance the reliability of the analysis results.

Particularly important is the consistency between the actual installation site and the meteorological data location. If the coordinates or elevation are offset, the local climate conditions may not be reflected correctly. Also, with solar radiation data, it is essential to understand the meanings of horizontal-plane irradiance, the direct component, and the diffuse component, and to know which data are used in which calculations.

Temperature data also affects power generation. Even in regions with high solar irradiance, output reductions due to high temperatures can be significant. It is important to check monthly and hourly temperature trends to ensure they align with regional characteristics. Furthermore, by selecting the data granularity according to the analysis purpose, you can more easily address not only estimates of annual generation but also considerations such as self-consumption, battery storage integration, and peak output.

Do not forget to check for missing or anomalous values. Nighttime irradiance, daytime zeros, extreme temperatures, or unit mix-ups can have a major impact on simulation results. Even if PVSyst’s report is nicely generated, if the input data are inappropriate, the reliability of the results will be low.

To use PVSyst in practice, it is important to treat meteorological data not as a task of "entering" but as preconditions that you have checked and can explain. If you cover the five items—location, solar irradiance, temperature, granularity, and quality checks—you will deepen your understanding as you read the PVSyst manual and be able to more clearly demonstrate the basis for power generation simulations.