PVsyst Japanese Translation Guide: Precise Shadow Analysis in PVSyst Using LRTK Point Cloud Data

The world-standard software PVsyst is often used for designing and simulating photovoltaic systems. However, PVsyst is basically provided in English, and many engineers seek information in Japanese. This article explains an overview of PVsyst and how to use it in Japanese, and then provides concrete steps for importing point cloud data acquired with the latest surveying technology LRTK into PVsyst to perform precise shadow analysis. By accurately evaluating the impact of on-site shading, you can improve the accuracy of generation forecasts and support optimized PV system designs.

What is PVsyst? Overview of PV simulation software

PVsyst is software that can simulate in detail the energy production and losses of photovoltaic systems. Developed in Switzerland, it has been used worldwide and is known as a reliable tool for design verification of large-scale projects. By inputting meteorological data (annual irradiance and temperature), the specifications of the solar panels and inverters used, and the layout, it can calculate annual energy production, breakdowns of losses, and performance indicators. A particularly strong feature is that it can account for the impact of surrounding environmental shading. For example, if there are mountains or high grounds around the site, these can be reflected as a distant obstruction in the horizon profile (the elevation angle of the horizon for each azimuth), and if there are trees or buildings near 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 will be.

Using PVsyst in Japanese

PVsyst currently allows selecting Japanese as the display language for the software. If you choose Japanese from the Preferences menu, the basic interface will be localized to Japanese. However, some parts are automatically translated, and certain terms or sentences can be awkward or hard to understand. In such cases, you can temporarily switch to English display by pressing `F9`, which is useful for comparing with the original. Note that the official documentation and help are still provided only in English. To master PVsyst, it is also important to make use of information sources that explain it in Japanese. Simulation reports contain technical terms and numbers, so understand the meaning of the main indicators. For example, the PR value (Performance Ratio) is a performance indicator that shows how much the actual energy produced compares to the energy that would be expected under ideal conditions. A value close to 100% means few losses and high system efficiency. The Losses section shows output reductions by category, such as panel temperature characteristics, wiring losses, inverter conversion losses, and shading losses. Grasping Japanese translations and the content of these terms will enable you to correctly interpret PVsyst output. If you feel uncertain, using Japanese explanatory materials or Japanese report creation services from specialized providers is another option.

Basic knowledge of shadow analysis

Because shading on solar panels significantly reduces generation, shadow analysis is very important in design. Shadows are mainly of two types: distant obstructions causing horizon shadows (distant shading) and nearby objects causing proximity shading. Distant shading occurs when mountains or tall buildings block sunlight when the sun is at a low elevation, and is evaluated over the year by the relationship between solar altitude and azimuth. Proximity shading is caused by nearby trees, utility poles, or adjacent panel racks inside or near the site, and typically results in parts of panels being partially shaded at certain times of day. In PVsyst, distant shading is entered as the site-specific horizon profile giving the obstruction elevation angles for each azimuth, while proximity shading is modeled by placing objects in the 3D scene, allowing the impact on energy production to be calculated individually.

However, performing such detailed shadow analysis requires detailed data about on-site obstructions. Traditionally, obtaining these data was not easy. For example, to understand distant horizon angles, one had to measure the horizon height on-site with a compass and inclinometer or take all-sky photographs with a fisheye lens and analyze them. To know the heights and positions of nearby trees and structures, survey instruments were used to measure each item individually, or drones were used to capture aerial images and generate 3D models. These methods are time-consuming and require specialized equipment and skills. Estimating from satellite images or existing topographic maps is possible, but these have limits in resolution and freshness and may differ from the actual site. If the input data are inaccurate, no matter how detailed PVsyst’s calculations are, simulation results will be erroneous and the optimized design may fail to deliver the expected generation.

The key to improving shadow analysis accuracy is how simply you can obtain precise on-site survey data. If you can accurately grasp terrain variations and the heights and positions of surrounding obstructions in centimeter units and reflect that in simulations, you can quantitatively estimate shading impacts. Enter LRTK—the new surveying solution using smartphones. The next chapter explains how to efficiently acquire on-site data with LRTK and use it for PVsyst shadow analysis.

What is LRTK: High-precision surveying with a smartphone

LRTK (pronounced “L-R-T-K”) is a small RTK-GNSS positioning device that attaches to a smartphone (mainly iPhone/iPad). RTK (real-time kinematic) is a technique that corrects GNSS positioning errors in real time, reducing position errors that are typically several meters (several ft) with ordinary GPS to a few centimeters (a few in) using RTK. By attaching an LRTK device to an iPhone and launching the dedicated app, anyone can easily use high-precision RTK positioning. The device is compact and pocketable, with the antenna and battery integrated, so surveys can be performed while walking the site without cumbersome wiring or large tripods.

Operation is simple: take the smartphone (with LRTK attached) to the point you want to measure and press a button in the app. The latitude, longitude, and elevation of that point are recorded instantly with cm level accuracy (half-inch accuracy). Conversion to Japan’s plane rectangular coordinate system and geoid height corrections are done automatically, so the obtained coordinates can be used directly in design drawings or CAD. Each survey point can be tagged with date/time and notes—naming points like “Planned ○○ PV site SW corner” helps organize data later.

A major strength of LRTK is that it can operate even in areas without mobile reception, such as mountainous regions. Typical RTK surveying requires mobile communication to receive correction information from a base station, but LRTK supports correction signals derived from Japan’s quasi-zenith satellite system (such as CLAS), enabling high-precision positioning without an internet connection. Since PV construction candidate sites are often in mountainous or suburban areas, the ability to survey without relying on network connectivity is highly reassuring.

Positioning data collected with LRTK can be uploaded to the cloud and shared immediately. Collected coordinate points are plotted on a map, allowing remote offices to monitor progress in real time. Distances and elevation differences between points are automatically calculated on-site, eliminating the need for handwritten field notes. With one smartphone per person, multiple people can split up and survey a wide area quickly. Precision surveying that once required specialist survey companies or heavy equipment is becoming much more accessible thanks to LRTK.

Acquiring point cloud data with the LiDAR scanner

LRTK’s true value is not limited to positioning “points.” By combining the LiDAR scanner included in recent iPhone models with RTK positioning, it is possible to capture the site environment as three-dimensional point cloud data. It’s as simple as walking around the site holding an iPhone—the terrain and structures in front of you are scanned into point clouds (3D scan). Since high-precision self-positioning from LRTK is performed in the background while LiDAR scanning, the acquired point cloud is tied from the outset to global coordinates (latitude, longitude, altitude). There is no need for later georeferencing to give coordinates to the point cloud; the data can be immediately overlaid on maps or CAD planning data.

Traditionally, acquiring 3D point cloud data required setting up an expensive terrestrial laser scanner or flying a drone for photogrammetry processing afterward. With drone surveys, achieving high accuracy often required placing many ground targets in advance and correcting the entire point cloud with those reference points after flight, which is cumbersome. Aerial surveys also cannot capture points not directly visible from above, such as under trees or in building shadows. In contrast, iPhone scanning with LRTK allows people to enter under obstacles and into narrow spaces for measurement, so there is less data loss, and real-time correction yields point clouds with less distortion.

Acquired point cloud data can be uploaded to the cloud and viewed and shared in a web browser. Even without dedicated software, you can measure distances between any two points, areas, and volumes, and display terrain cross-sections with a single click. For example, comparing earthwork volumes (cut and fill) before and after site grading across a large site is easy. Point clouds are digital assets that record current conditions as they are, so later overlaying them with design models makes it possible to proceed with tasks that once relied on estimates using solid data.

Above all, point clouds capture surrounding obstructions such as trees and buildings around the site in full. Using this data provides the material needed for precise shadow analysis in PVsyst. For example, scanning the surrounding forest from the site boundary lets you determine the heights and positions of trees in each azimuth with errors of a few centimeters (a few in). By analyzing that data to determine obstruction angles relative to solar altitude (i.e., how high the sun is blocked at each azimuth) and reflecting that in PVsyst’s horizon profile and proximity object inputs, you can estimate seasonal and time-of-day shading losses with high accuracy. In practice, 3D terrain models of the site derived from LRTK terrain and point cloud data, combined with panel layout models and imported into PVsyst, have been used to simulate when and how much shadow each panel receives as animations—an advanced study. Simulations that faithfully reproduce real environments make post-construction plant performance more certain and are expected to significantly enhance the reliability of investment decisions and design.

Steps to use LRTK point cloud data in PVsyst shadow analysis

Below is a step-by-step outline of how point cloud data and positioning information acquired with LRTK can be incorporated into PVsyst simulations.

• On-site data acquisition: First, conduct surveying at the planned PV site using LRTK. Walk over areas where panels will be installed and measure ground elevations and boundary points with your smartphone. At the same time, perform point cloud scans directed at surrounding obstructions (trees and buildings) to capture the surrounding environment. The key is to scan all obstructions that could affect the panels. Walk around the site perimeter and, if necessary, scan multiple times to obtain 3D data with sufficient coverage for later analysis.

• Point cloud processing and analysis: Next, extract the information needed for shadow analysis from the acquired point cloud data. Display the point cloud on the LRTK cloud platform and measure tree and structure heights and positional relationships. For example, determine the tallest tree heights and the distances from the panels to each obstruction. If you want to derive the horizon profile caused by distant mountain ranges, compute the obstruction elevation angles on the horizon from the point cloud. If necessary, 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 preparations are complete, set up the simulation project in PVsyst. First register the plant location (latitude and longitude), meteorological data, panel specifications, layout, and other basic information. Then reflect the shading elements you extracted earlier into PVsyst. For distant mountains and high grounds, enter the horizon elevation angles for each azimuth in PVsyst’s Horizon editing screen. If you are handling many points, you can consolidate them into a CSV file and import it. For nearby trees and structures, add obstruction objects in PVsyst’s 3D scene editor. PVsyst allows placement of basic shapes like boxes and cylinders, so for trees you can place cylinders of equivalent dimensions for those with known positions and heights to model proximity shading. By placing objects according to dimensions obtained from point clouds, you can reproduce shadow scenarios close to the actual site. If you have terrain elevation data from the point cloud, use it to create ground objects 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 DAE format for import into PVsyst.

• Shadow analysis and result verification: After entering obstructions, run the shadow analysis in PVsyst. First use the “Shading Analysis” tool to calculate annual shading losses and check monthly shading impacts. You can also visualize shadows at specific dates and times in the 3D scene and play animations to simulate the movement of shadows over a day. Because the calculations are based on data from point clouds, the computed shading impacts should reflect site conditions. Analyze the simulation results and check for times or locations with significant shading-related generation losses. Based on the results, consider countermeasures such as reviewing panel layout or trimming/removing obstructions. Finally, run annual energy simulations under the confirmed conditions to obtain generation forecasts that properly account for shading impacts.

Use case: Identifying shading risk in advance with LRTK and PVsyst

Here is a practical example combining LRTK field data acquisition and PVsyst analysis. For the plan of a medium-sized PV plant, a forest south of the site raised concerns about its impact on generation. Quantifying that impact was difficult with conventional methods, but by using LRTK on-site to scan the surrounding forest from the site boundary, the heights and positions of hundreds of trees were digitized. Analyzing the height distribution of trees by direction from the point cloud and reflecting that into PVsyst’s horizon profile and proximity obstruction inputs allowed simulation of shading throughout the year. The results showed that in the late afternoon around the winter solstice, parts of the site could suffer up to about 5% generation loss. Because this risk was identified in the planning stage, problematic tall trees were selectively thinned and the panel layout was slightly adjusted, reducing the expected loss to about 2%. By combining detailed environmental data from LRTK with PVsyst shadow analysis, you can quantitatively evaluate shading risks that were previously overlooked and incorporate countermeasures into an optimal design.

Summary of benefits of using LRTK

From the processes and cases described above, here are the main benefits of adopting LRTK.

• Streamlined surveying work: Because surveying is done with a smartphone and a small device, there is no need to hire specialists or bring in large equipment. One person can cover a wide site quickly, greatly reducing the effort and time required for field surveys.

• Cost reduction: You can reduce costs for expensive surveying instruments, drones, and outsourcing. Since surveying can be completed with simple equipment, labor and equipment costs are lowered, contributing to overall project cost savings.

• High-precision data acquisition: Combining RTK centimeter-level positioning and LiDAR enables acquisition of very high-precision on-site data. This increases the reliability of shading analysis and generation forecasts and allows optimizing plans without excessively conservative safety margins.

• Complete understanding of site conditions: Point clouds include everything from subtle terrain undulations to precise tree positions and heights. Because current conditions are digitized, changes that existing documents cannot capture (tree growth, new constructions, etc.) can be reflected, avoiding omissions during design.

• Improved simulation accuracy: Entering high-precision data obtained with LRTK into PVsyst brings 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: Based on detailed shading analysis, layout improvements and obstruction countermeasures can be implemented to reduce shading losses in advance. This uncovers potential issues in the planning stage and leads to optimal designs, which directly improves the accuracy of investment decisions.

• Shared use of digital data: Survey results and point clouds can be shared immediately via the cloud, enabling real-time team collaboration. The acquired point clouds and created 3D models are flexible for uses beyond simulation, such as visualization of the final image and construction management.

Conclusion: Improve design accuracy and efficiency with LRTK quick surveying

The combination of PVsyst simulation and on-site data acquisition with LRTK has made precise shadow analysis, which was previously difficult, much more accessible. Although PVsyst is primarily English-based, the barrier can be reduced by using Japanese guides and translation functions. Above all, the high-precision point cloud data obtained by LRTK quick surveying directly improve simulation accuracy. The surveying and shadow analysis that once required specialists can now be performed by anyone with a smartphone. Actively adopt LRTK surveying technology and reflect real on-site information in PVsyst simulations. Simulations based on high-quality data will increase the reliability of PV system design and greatly contribute to project success.