PVSyst Japanese Translation Guide: Precise Shading Analysis with LRTK Point Cloud Data

PVsyst, the global standard software for designing and simulating photovoltaic systems, is widely used. However, PVsyst is primarily provided in English, and many engineers seek information in Japanese. This article explains the overview of PVsyst and key usage points in Japanese, and introduces concrete steps to import point cloud data acquired with the latest surveying technology, LRTK, into PVsyst for precise shading analysis. By accurately evaluating the effect of on-site shading, you can improve the accuracy of generation forecasts and optimize photovoltaic system designs.

What is PVsyst? Overview of photovoltaic simulation software

PVsyst is software that can simulate in detail the power output and losses of photovoltaic systems. Developed in Switzerland, it has been used worldwide and is known as a reliable tool for validating designs of large-scale projects. By entering meteorological data (annual irradiation and temperature), the specifications of the PV modules and inverters used, and the layout, PVsyst can calculate the annual energy yield, the breakdown of losses, and performance indicators. A particular strength is the ability to consider shading effects from the surrounding environment. For example, if there are mountains or high ground around the site, these can be reflected as a far-field obstruction in the horizon profile (the elevation angle of the horizon by azimuth). If there are trees or buildings close to the site, they can be placed as nearby objects in the 3D scene to simulate shadows on the panels. The more detailed and accurate the input data, the more reliable the simulation results.

Using PVsyst in Japanese

PVsyst currently allows you to select Japanese as the display language for the software. Choosing Japanese from the Preferences menu will localize the main screens. However, some parts are machine-translated, and certain terms or sentences may feel unnatural or be hard to interpret. In such cases, you can temporarily switch back to English by pressing `F9` to compare with the original wording. Note that the official documentation and help are still provided only in English. To master PVsyst, it is also important to use Japanese-language reference materials. Simulation reports contain specialized terms and numerical data, so understand the meanings of key indicators. For example, PR (Performance Ratio) is a performance indicator that shows how the actual energy produced compares to what would be expected under ideal conditions. A value close to 100% means low losses and high system efficiency. The Losses section lists output reductions by category, such as module temperature effects, wiring losses, inverter conversion losses, and shading losses. Grasping these Japanese translations and their meanings will help you correctly interpret PVsyst outputs. If you are unsure, using Japanese explanatory materials from specialized firms or Japanese report creation services is an option.

Basic knowledge of shading analysis

Because shading on PV modules significantly reduces power generation, shading analysis is critically important in design. Shading is mainly of two types: far-field shading from distant obstructions (shading on the horizon) and near-field shading from nearby objects. Far-field shading occurs when mountains or tall buildings block sunlight at low sun angles; it is evaluated over the year based on the relationship between solar altitude and azimuth. Near-field shading occurs when nearby trees, utility poles, or adjacent racking cast shadows on some panels at certain times of day. In PVsyst, far-field shading is input as a site-specific horizon profile, which sets the blocking angle on the horizontal plane for each azimuth; near-field shading is modeled by placing objects in the 3D scene and calculating their individual effects on panels.

However, detailed shading analysis requires precise data about on-site obstructions. Historically, obtaining such data has not been easy. For example, to determine the distant horizon angle, you might need to measure horizon heights on site using a compass and clinometer, or take all-sky photos with a fisheye lens camera and analyze them. To know the height and location of nearby trees and structures, survey instruments were used to measure each feature individually, or drones were flown to capture aerial imagery for 3D modeling. These methods are time-consuming and require specialized equipment and skills. Estimating from satellite images or existing topographic maps is possible, but there are limits in resolution and currency, and discrepancies with the actual site are possible. If input data are inaccurate, then no matter how detailed the PVsyst calculations are, the simulation results can be off, and the optimized design may not yield the expected energy production.

The key to improving shading analysis accuracy is how easily you can obtain precise on-site survey data. If you can accurately capture terrain elevation differences and the heights and positions of surrounding obstructions to centimeter-level accuracy and reflect them in simulations, you can quantitatively estimate shading impacts. Enter LRTK: a new surveying solution using smartphones. The next chapter explains how to efficiently acquire on-site data with LRTK and use it in PVsyst shading analysis.

What is LRTK: high-precision surveying with a smartphone

LRTK (pronounced "L-R-T-K") is a compact RTK-GNSS positioning device that attaches to smartphones (mainly iPhone/iPad). RTK (Real-Time Kinematic) is a technique that corrects GNSS positioning errors in real time, reducing typical GPS errors of several meters down to a few centimeters. By attaching an LRTK device to an iPhone and launching a dedicated app, anyone can conveniently use high-precision RTK positioning. The device is lightweight and compact enough to fit in a pocket, with an integrated antenna and battery, so you can survey on the move without complex wiring or large tripods.

Operation is simple: bring the smartphone (with LRTK attached) to the point you want to measure and press the button in the app. The latitude, longitude, and elevation of that point are recorded instantly with centimeter-level accuracy. Conversion to Japan’s plane rectangular coordinate system and geoid height correction are performed automatically, so the coordinates obtained can be used directly in design drawings or CAD. You can attach date/time and notes to each measurement point—for example, naming a point "Planned Plant Southwest Corner"—making later data organization easier.

Another advantage of LRTK is that it can position even where mobile signals are unavailable, such as in mountainous areas. Standard RTK surveying requires mobile communication to receive correction information from a base station, but LRTK supports correction signals originating from Japan’s quasi-zenith satellite system (such as CLAS), enabling high-precision positioning without an Internet connection. Since many candidate photovoltaic sites are in mountainous or rural areas, the ability to survey regardless of communication conditions is a major benefit.

Positioning data acquired with LRTK can be uploaded to the cloud and shared immediately. Measured coordinate points are plotted on a map so progress can be checked in real time from a remote office. Distances and elevation differences between measurement points are automatically calculated on-site, eliminating the need for handwritten field notes. With a smartphone per person, multiple people can divide the work and quickly survey large areas. Precision surveying that formerly required hiring a specialized surveyor or deploying heavy equipment is becoming much more accessible thanks to LRTK.

Point cloud data acquisition with the LiDAR scanner

LRTK's true value goes beyond point positioning. By combining the LiDAR scanner built into recent iPhone models with RTK positioning, you can capture the environment as 3D point cloud data. Usage is simple: hold the iPhone and walk around the site, and the terrain and structures in front of you will be scanned into a point cloud (3D scan). Because high-precision self-positioning from LRTK runs in the background while LiDAR scans, the resulting point cloud is already tied to global coordinates (latitude, longitude, altitude). No georeferencing of the point cloud is required later, and the data can be immediately overlaid with maps or CAD planning data.

Traditionally, obtaining 3D point clouds required setting up expensive terrestrial laser scanners or performing photogrammetry from drone flights. Even with drones, achieving high accuracy often requires laying numerous ground targets in advance and then correcting the entire point cloud using those reference points, which is labor-intensive. Aerial surveys also cannot capture areas occluded from above, such as under tree canopies or in building shadows. In contrast, iPhone scanning with LRTK allows personnel to enter under obstacles and into tight spaces, resulting in fewer data gaps and less-distorted point clouds through real-time correction.

Acquired point cloud data can be uploaded to the cloud and viewed/shared in a web browser. Even without specialized software, you can click to measure arbitrary distances between two points, areas, volumes, or display terrain cross-sections. For example, comparing earthwork volumes before and after site preparation is straightforward. Point clouds are a digital record of current site conditions, so overlaying them with design models later allows work that once relied on estimates to proceed on the basis of solid data.

Most importantly, point clouds capture surrounding obstructions such as trees and buildings in full. This data can be used as material for precise PVsyst shading analysis. For example, scanning the forest surrounding a site boundary lets you determine the heights and positions of trees by azimuth with centimeter accuracy. By analyzing this data to determine the blocking angles for sun altitude at each azimuth and reflecting those angles in PVsyst's horizon profile and nearby object inputs, you can estimate seasonal and time-of-day shading losses with high precision. In practice, 3D terrain models generated from LRTK-derived topography and point clouds, combined with panel layout models imported into PVsyst, allow advanced studies such as animating when and how much shade each panel receives. Simulations that faithfully reproduce actual conditions can make post-construction plant performance more predictable and enhance the reliability of investment and design decisions.

Procedure to use LRTK point cloud data in PVsyst shading analysis

Below is an overview of the steps to take LRTK-acquired point cloud and positioning data and incorporate them into PVsyst simulations.

• On-site data acquisition: First, survey the planned plant site using LRTK. Walk through the area planned for panels and surrounding terrain, measuring ground heights and boundary points with the smartphone. At the same time, perform point cloud scans aimed at surrounding obstructions (trees and buildings) to capture the local environment. The key is to scan every obstruction that could affect the panels. Walk around the site perimeter and, if necessary, perform multiple scans to ensure sufficient 3D coverage for later analysis.

• Point cloud processing and analysis: Next, extract information needed for shading analysis from the acquired point cloud. Display the point cloud on the LRTK cloud platform and measure the heights and positional relationships of trees and structures—for example, identifying the tallest trees and the distances from panels to each obstruction. If you want a horizon profile from distant mountain ranges, derive the blocking angles on the horizontal plane from the point cloud. You may mesh the point cloud to create a 3D model or import it into CAD to trace obstruction outlines. The important thing at this stage is to list the heights and positions of obstructions that should be input into PVsyst.

• Input into PVsyst: Once prepared, set up the simulation project in PVsyst. Enter the plant location (latitude/longitude), meteorological data, module specifications, layout, and other basic information. Then reflect the extracted shading elements in PVsyst. For distant mountains or high ground, input the horizon elevation angle for each azimuth in PVsyst’s horizon editing screen. When dealing with many points, you can compile them into a CSV file and import them. For nearby trees and structures, add obstruction objects in PVsyst’s 3D scene editor. Since PVsyst can place basic shapes like boxes and cylinders, model trees with cylinders of equivalent dimensions where their position and height are known. Placing objects according to dimensions derived from point clouds reproduces realistic shading scenarios. If you have terrain undulation data from the point cloud, create a ground object based on that so you can consider layouts on slopes realistically. You can also create detailed 3D models in external CAD or BIM software and export them in formats such as DAE for PVsyst import.

• Shading analysis and result verification: After entering obstructions, run the shading analysis in PVsyst. Use the shading analysis tool to calculate annual shading losses and check monthly shading impacts. Visualize the shading on the 3D scene for specific dates and times, or animate a day to simulate the sun’s movement and shadowing. Because the inputs are based on point cloud data, the calculated shading should reflect the actual site conditions. Analyze the simulation results to identify time periods or locations with significant shading-related generation losses. Depending on findings, consider measures such as adjusting panel layout or pruning/removing obstructions. Finally, run the annual energy simulation with confirmed conditions to obtain an energy forecast that properly accounts for shading impacts.

Use case: identifying shading risks in advance with LRTK and PVsyst

Here is a practical example combining LRTK on-site data acquisition and PVsyst analysis. For a mid-sized PV plant plan, a forest to the south of the site raised concerns about its impact on generation. Quantifying that impact was previously difficult, but by using LRTK on-site to scan the forest from the site boundary, the heights and arrangement of hundreds of trees were digitized. The point cloud was analyzed for tree height distribution by azimuth and reflected in PVsyst’s horizon profile and nearby obstruction inputs to simulate shading throughout the year. The results showed that some areas of the site could experience up to about 5% generation loss in late afternoon around the winter solstice. Because this risk was identified at the planning stage, targeted thinning of tall trees and slight layout adjustments were considered for the most impacted areas, resulting in a design that reduced expected losses to about 2%. This example shows that combining detailed environmental data from LRTK with PVsyst shading analysis enables quantitative evaluation of shading risks that were previously easy to overlook, and supports design choices that incorporate mitigation measures.

Summary of benefits of using LRTK

From the processes and case studies above, here are the main benefits of introducing LRTK:

• Survey efficiency: Because surveying is done with a smartphone and a small device, there is no need to hire specialists or bring in large equipment. A single person can cover a large site quickly, significantly reducing the time and effort of field surveys.

• Cost reduction: LRTK can reduce the need for expensive surveying equipment, drones, and outsourcing fees. With lightweight equipment, surveying is completed with less labor and equipment cost, which can lower overall project costs.

• High-precision data acquisition: Combining RTK centimeter-level positioning with LiDAR yields highly accurate on-site data. This improves the reliability of shading analysis and energy forecasts, allowing optimization without overly conservative safety margins.

• Complete understanding of site conditions: The point cloud contains everything from subtle terrain undulations to the positions and heights of small trees. Because it digitizes the current site, changes that would not be captured by existing materials (tree growth, new buildings, etc.) are reflected, reducing omissions at the design stage.

• Improved simulation accuracy: Inputting high-precision data from LRTK into PVsyst makes simulation results closer to actual plant behavior. In particular, estimates of shading losses become more accurate, minimizing discrepancies between forecasted and actual generation.

• Design optimization and risk reduction: Detailed shading analysis allows layout improvements and obstruction countermeasures to reduce shading losses in advance. This identifies potential issues at the planning stage and leads to optimal design proposals, directly improving investment decision accuracy.

• Shared use of digital data: Survey results and point clouds can be shared instantly via the cloud, enabling real-time information sharing within teams. Acquired point clouds and created 3D models are useful not only for simulation but also for visualization of final designs and construction management, offering flexible applications.

Conclusion: Improve design accuracy and efficiency with LRTK quick surveying

Combining PVsyst-based PV simulation and LRTK on-site data acquisition has made precise shading analysis more accessible. Although PVsyst is largely English-based, using Japanese guides and the software's translation functions lowers the barrier. Above all, high-precision point cloud data obtained by simple LRTK surveying directly improves simulation accuracy. Surveying and shading analysis that once required experts can now be done by anyone with a smartphone. Actively utilize LRTK surveying technology and reflect real on-site information in PVsyst simulations. Simulations based on high-quality data will enhance the reliability of photovoltaic system designs and greatly contribute to project success.