Five methods to organize PVSyst simulation results to make them easier to explain

Table of contents

• Why PVSyst simulation results become hard to explain

• Method 1: First define in one sentence what the comparison is about

• Method 2: Make annual energy and PR the protagonists and use monthly trends as support

• Method 3: Explain differences by dividing causes into shading, temperature, electrical, and operations

• Method 4: Present assumptions and uncertainties together

• Method 5: Prepare rephrasings for each type of audience

• How to connect PVSyst result explanations to practical outcomes

Why PVSyst simulation results become hard to explain

PVSyst returns a very large amount of information, which makes it useful in practice but also prone to producing results that are hard to explain. Annual energy, PR, monthly generation, the loss tree, shading impacts, temperature losses, PCS output limits, various loss coefficients—many meaningful numbers and graphs appear on the screen. Even if the engineer understands how these relate, internal stakeholders or clients can easily be overwhelmed by the volume of information and not know what is important. As a result, despite careful simulation work, presentations can end up showing only numbers and failing to convey the conclusion.

A common practical mistake is showing the results as-is. The PVSyst interface and reports are technically well-organized, but that organization is intended for reading calculations, not necessarily for supporting decision-making. For example, if you want to show that annual energy is high but you immediately present monthly graphs, loss items, and configuration screens all at once, the audience will be unsure where to look. Conversely, if you extract only the annual energy number, the audience may not understand which assumptions produced that number, making the material less credible. Ease of explanation is determined not by the amount of information but by how information is arranged.

PVSyst results are also highly dependent on assumptions. A slight difference in how meteorological data are handled, azimuth, tilt, shading assumptions, loss coefficients, or assumptions about availability losses can greatly change the impression of the results. However, in presentations the final annual energy and PR often appear as independent figures. What the audience really wants is not just the numbers themselves but the reasoning that led to those numbers, and this is often not conveyed sufficiently. Making PVSyst results easy to explain therefore also means translating complex assumptions into a form that the audience can judge.

Another often-overlooked point is that different audiences want different things. Engineers may want the breakdown of losses, sales staff may want to know the proposal’s strengths, and decision-makers often want to know whether the option is ultimately advantageous, where uncertainties lie, and what should be checked next. In other words, making PVSyst results easier to explain requires not only organizing the content but also clarifying who you are explaining to. The following sections introduce five concrete organizing methods for that purpose.

Method 1: First define in one sentence what the comparison is about

The first method for making PVSyst results easier to explain is to define in one sentence what the comparison is about. In practice, simulations are sometimes run with multiple comparison points still in play. You want to look at azimuth, check shading, compare PCS options, and consider different loss coefficients—when topics multiply, the meaning of the results can become blurred. If you start explaining in that state, listeners will not know what difference you are showing.

For example: “This comparison is to check the difference in azimuth,” “This comparison is to see the impact of row spacing on shading,” or “This comparison is to confirm the effect of different PCS configurations.” Defining one main subject per comparison makes subsequent explanations surprisingly easier. Whether done before or after preparing comparison scenarios, it is important to briefly state what you want to judge from the result. The more multifunctional PVSyst is, the more effective it is to narrow the purpose of the comparison up front.

Having that one-sentence purpose also determines which numbers to show. If you want to demonstrate differences in shading conditions, not only the annual energy difference but winter monthly differences and shading loss differences become important. If you want to show PCS differences, PR and how output limits appear in addition to annual values are useful supporting information. Conversely, if the comparison objective is vague, every number may seem relevant and you may be tempted to show everything, which makes explanations difficult. What you show naturally narrows once you decide what you are comparing.

In practice, it is very effective to write “what this comparison is intended to check” above each comparison header in your materials, even if only for your own use. Even a single sentence stabilizes the flow of explanation and clarifies the order in which to present PVSyst numbers. Results that are hard to explain are often not due to poor numbers but to unclear comparison objectives. Defining the comparison subject in one sentence is the starting point for making explanations easier.

Method 2: Make annual energy and PR the protagonists and use monthly trends as support

The second method is to make annual energy and PR the protagonists, and use monthly trends as supporting information. PVSyst provides many results, and you may want to explain them all with equal weight, but practical materials communicate better when you separate main and supporting points. Annual energy shows the final quantity, and PR gives a rough indication of the loss structure underlying that generation. Placing these two as your axis helps the audience grasp the conclusion first.

However, annual energy and PR alone do not explain why differences occur. Use monthly trends as support. For example, if annual energy shows only a slight difference but there is a large difference in winter months, shading or tilt-angle differences are suspected. If the difference appears mainly in summer, temperature losses or how the PCS handles output may be the primary cause. Monthly generation is stronger as a supporting tool to explain the meaning behind annual differences rather than as the main focus. Using PVSyst’s monthly charts to reveal what lies behind the yearly totals makes the message clearer.

Also, PR is easier to explain when shown in combination with annual energy rather than as a standalone competitive metric. If annual energy is high but PR is low, that suggests weight in some part of the loss structure. Conversely, if PR is high but annual energy does not increase as expected, check meteorological assumptions, azimuth, and tilt relationships. Just presenting annual energy and PR side by side allows viewers to infer a lot.

In practice, a useful flow is to first show annual energy, then add PR, and then summarize the monthly chart characteristics in one sentence as the reason for the difference. For example: “Overall A option has the advantage in annual totals, but the large winter difference indicates that shading conditions are the main cause.” Rather than showing all PVSyst outputs at once, placing annual energy and PR as protagonists and using monthly trends as support streamlines the explanation.

Method 3: Explain differences by dividing causes into shading, temperature, electrical, and operations

The third method is to explain differences by dividing causes into four categories: shading, temperature, electrical, and operations. PVSyst results involve many overlapping losses, so attempting to explain causes as a single factor tends to be vague. In practice you will trace causes using the loss tree, PR, and monthly generation, and having classification boxes in your mind makes organization much easier.

Shading includes self-shading between rows, building shading, slope shading, tree shading, etc. Temperature includes not only ambient temperature conditions but also ventilation, array density, and interactions with surrounding structures. Electrical includes mismatch, wiring, string configuration, how the PCS handles input, and output limits. Operations includes availability losses, assumptions about cleaning, downtime assumptions, and maintainability. With these four categories, you can convert PVSyst result differences into very practical language.

For example, when explaining a case with a large winter-only difference, saying “it’s due to shading conditions” is shallow, but saying “the main cause is inter-row self-shading” makes it easier to understand. When differences appear in summer, saying just “temperature differences” is weaker than saying “the difference is driven by ventilation conditions caused by denser array spacing.” The PVSyst loss tree shows detailed items, but reorganizing those items into these four boxes makes the explanation much more effective.

In practice, decide the primary classification for each comparison—e.g., “this difference is mainly due to shading” or “this difference is mainly due to electrical conditions.” Adding one or two secondary contributing factors makes PVSyst results much more comprehensible. When you struggle to explain numbers, resist being led by numbers alone and think about which of shading, temperature, electrical, or operations the difference fits into; this creates a clear skeleton for your story.

Method 4: Present assumptions and uncertainties together

The fourth method is to always present assumptions and uncertainties together. PVSyst results can look like finished answers, but in practice it is normal for assumptions such as the installation site, meteorological data, shading conditions, loss coefficients, and availability losses to retain some uncertainty. When preparing materials, there is a temptation to show only tidy numbers and hide uncertainties. This tendency is a major cause of explanations being difficult later.

For example, if shading conditions are provisional before on-site confirmation, azimuth may be slightly adjusted by grading conditions, or availability losses may change as maintenance arrangements become more detailed—these items directly affect PVSyst results. If you hide such uncertainties and show only the annual energy figure, the credibility of the initial materials can erode when conditions change. Conversely, if you clarify which assumptions are settled and which remain to be confirmed, the audience can view the numbers more flexibly and the explanation becomes stronger.

Presenting assumptions together also turns the material from a mere report into decision-making input. A number like “annual energy is X kWh” alone makes it hard for the audience to judge its significance. But if you say “this number assumes the site’s meteorological data, azimuth, tilt, shading conditions, and main losses,” the audience can better judge how much to trust the figure. In practice, numbers with clear assumptions are more useful than perfect numbers with unclear bases.

Therefore, when explaining PVSyst results, briefly summarize key assumptions and which parts still have uncertainty. You do not need to list every detail, but at least indicate what is confirmed and what is provisional and what needs future verification. Materials that are easy to explain are not those that assert certainty, but those that present assumptions and uncertainties in an organized way. Adopting this perspective makes PVSyst outputs far more practical.

Method 5: Prepare rephrasings for each type of audience

The last method is to prepare rephrasings for each type of audience. One big reason PVSyst results are hard to explain is that language natural to engineers does not always convey the meaning to others. Terms such as Near Shading, PR, mismatch, availability loss, DC/AC ratio, and loss tree may be understood among practitioners but can be difficult for sales staff, approvers, clients, or financial institution representatives. It is necessary to convert not only the numbers but the language into terms that make it easy for the audience to judge.

For example, instead of presenting PR bare, add an explanation like “an index of operational efficiency including losses.” Near Shading can be phrased as “the impact of shadows from nearby equipment or buildings,” and availability loss can be “the anticipated operating rate including stoppages and inspections.” Such translations bring PVSyst outputs closer to the audience. In practice, it is more valuable to explain complex matters in simpler terms—without sacrificing accuracy—than to leave them in technical language.

Different audiences also care about different points. Engineers may focus on loss breakdowns and monthly differences, while decision-makers often want to know whether to adopt the option, what the risks are, and what to check next. The same PVSyst results therefore need to be presented and explained in an order tailored to the audience. When explaining is difficult, it is often not because the content is too hard but because the audience-facing translation is insufficient.

Thus, when explaining PVSyst results, first be conscious of who you are addressing and prepare to rephrase into words that help that audience make judgments. You do not need to remove all technical terms, but provide meanings and show how they relate to the conclusions. Making PVSyst simulation results easy to explain is not about neatly arranging numbers but about converting them into language the audience can understand and use.

How to connect PVSyst result explanations to practical outcomes

The five organizing methods described above share a common theme: do not treat PVSyst results as mere output screens. Define the comparison subject, make annual energy and PR the protagonists, organize differences into the shading/temperature/electrical/operations boxes, present assumptions and uncertainties together, and rephrase for the audience. When you can follow this flow, PVSyst simulation results become not just calculations but practical materials that support decision-making.

For practitioners, the important thing is not to show the highest energy number. What is truly valuable is the ability to explain the assumptions behind that number, where improvements are possible, and what to check next. How well you can interpret numbers and communicate them—rather than just how well you can produce numbers—determines how effectively you can use PVSyst. Ease of explanation reflects the depth of understanding of the design itself.

To strengthen explanations further, do not stop at desk simulations. If site boundaries, slopes, buildings, trees, walkways, existing conditions, and maintenance routes are ambiguous, no matter how well you organize results, the assumptions themselves remain weak. To connect PVSyst results to practical outcomes, repeatedly iterate between site understanding and simulation to verify the context behind the numbers.

In that sense, when you want to obtain position confirmation or coordinates more reliably on site, it is natural to consider using iPhone-mounted GNSS high-precision positioning devices such as LRTK. If on-site position information and site conditions are easier to organize, explanations of azimuth, layout, shading conditions, and walkway conditions produced in PVSyst become clearer. If you can increase desk-based comparison accuracy with PVSyst and support site understanding accuracy with LRTK, explaining simulation results becomes not just document preparation but a field-rooted proposal capability. Organizing PVSyst results for easier explanation is not a technique for presenting numbers; it is an improvement of practical capability that connects desk work and fieldwork and enables the audience to make informed judgments.