Seven simulation steps to predict solar power generation in advance

Simulating solar power generation is an important task for forecasting annual output and monthly generation trends before installing equipment. In practice, it is widely used not only when assessing the profitability of a power plant but also for decisions on rooftop equipment installation, improvements to existing equipment, maintenance planning, and identifying the causes of generation declines.

However, simulation results can vary greatly depending on the input conditions. If parameters such as solar irradiance, azimuth, tilt angle, shading, system capacity, temperature-related losses, and wiring or conversion losses are entered ambiguously, the figures may look precise but can lead to forecasts that are difficult to use for on-site decision-making. What matters is not just listing numbers in detail, but clarifying which conditions were assumed and on what basis, and projecting power generation that closely reflects actual operation.

In this article, aimed at practitioners searching for information on "solar power generation simulation", we explain the basic procedures for conducting pre-forecasts in seven steps. We do not rely on specialized analysis software or specific equipment names, but instead focus on the concepts that should be checked in any simulation environment.

Table of Contents

• What to check in a solar power generation simulation

• Step 1: Define the purpose of the simulation and the evaluation criteria

• Step 2: Clarify the installation site and surrounding environmental conditions

• Step 3: Set assumptions for solar irradiance and weather conditions

• Step 4: Input equipment capacity and panel layout conditions

• Step 5: Account for loss conditions such as shading, temperature, and conversion

• Step 6: Check monthly and annual power generation

• Step 7: Verify forecast variability with sensitivity analysis

• Key points for applying forecast results to practical decision-making

• Summary

What to Check in Solar Power Generation Simulations

Simulation of solar power generation is not merely the task of producing the figure "how many kWh will be generated annually." What is important in practice is understanding the assumptions from which the predicted generation is derived, and organizing those figures into a form that can be used for design, investment decisions, construction planning, and operations management.

Solar power generation varies even with the same installed capacity depending on the installation site, orientation, tilt angle, shading, temperature, snowfall, soiling, and equipment configuration. For example, locations that are close to south-facing with little shading tend to have relatively stable generation, whereas east- or west-facing roofs, sites receiving shadows from surrounding buildings, and mountainous or snowy regions can show different annual generation even with the same capacity. Therefore, simulations should not look at installed capacity alone but must check site conditions and loss factors together.

Also, simulations do not guarantee future power generation. Actual solar irradiance varies from year to year, and generation performance also changes due to equipment aging and operating conditions. Therefore, it is important to treat predicted values not as a "fixed answer" but as an "estimate under certain assumptions." Forecasts created under overly optimistic assumptions may lead to large discrepancies with post-installation results, causing difficulties when explaining outcomes internally or to customers.

What practitioners should focus on is not producing the maximum possible power output, but creating figures that can be explained realistically. By organizing input conditions, loss assumptions, and comparison criteria, and, when necessary, separating and checking a standard case and a conservative case, the forecast results become easier to use as a basis for decision-making. From here, we will go through the specific sequential steps to predict power generation in advance.

Step 1: Determine the purpose of the simulation and the evaluation criteria

The first thing to do is to clarify the purpose of the simulation. Even when predicting solar power generation, the required accuracy and the items to be examined change depending on the purpose. Whether you want to determine the feasibility of a new installation, evaluate the profitability of a power generation business, verify a drop in output from existing equipment, or compare design proposals, the necessary input conditions and how you interpret the results will differ.

When considering a new installation, annual power generation, monthly generation, the time periods available for self-consumption, and the relationship with electricity usage are important. If you are evaluating it as a power generation business, you should examine not only annual generation but also equipment utilization rate, loss rate, long-term output degradation, and the risk of downtime due to maintenance. When aiming to improve existing equipment, you need to compare past actual values with simulation values and have the perspective to distinguish whether the cause is insufficient insolation, shading, or problems on the equipment side.

If you start work with unclear objectives, it becomes difficult to judge whether the resulting power generation is high or low. For example, even if a certain amount of annual power generation can be expected, you cannot determine whether that is sufficient for an investment decision, superior when comparing design proposals, or problematic as a maintenance target without evaluation criteria. Before running simulations, it is important to clarify which figures you will examine and what decisions you will make.

When setting evaluation criteria, it is important not to rely solely on annual power generation. Solar power generation has seasonal variability, and output patterns differ between summer and winter. Depending on roof shape and installation orientation, generation can also be biased between morning and afternoon. Therefore, in addition to the annual total, including monthly trends, months when generation tends to drop, and time periods when shadowing has a strong effect among the items to check makes it easier to use for practical decision-making.

Also, when using this in internal documents or materials for clients, you need to be mindful of the responsibility to explain the predicted values. Make sure you can later explain the solar irradiance data entered, equipment capacity, loss rates, degradation rates, shading conditions, and so on, as this makes it easier to prevent misunderstandings among stakeholders. A simulation is both a computational task and the preparation of materials for sharing the assumptions.

Step 2: Organize the conditions of the installation site and surrounding environment

Next, we will organize the conditions of the installation site and the surrounding environment. Solar power generation is affected not only by regional solar irradiance conditions but also by the shape of the site and the roof, nearby buildings, trees, mountains, utility poles, signs, and other factors. Since power generation can vary even with the same system capacity depending on installation-site conditions, organizing the on-site conditions forms the basis of the simulation.

The first thing to confirm is the location information of the planned installation site. Annual solar radiation, temperature, snowfall, and cloudiness trends vary by region. By setting conditions based on as precise a location as possible—not just a rough address—you can make calculations of solar radiation and solar altitude more representative of on-site conditions. In particular, in mountainous areas, coastal areas, snowy regions, and densely built urban areas, conditions can differ even within the same prefecture.

When installing on a roof, organize the roof's orientation, pitch, area, obstacles, roofing material, load conditions, and so on. Because the amount of solar radiation photovoltaic panels receive changes with orientation and tilt angle, it is important to check the direction and angle of each roof surface separately. If installing across multiple roof surfaces, do not treat south-facing, east-facing, and west-facing surfaces as a single group; consider each surface's generation characteristics separately to improve the interpretability of the predictions.

For ground-mounted installations, check the site area, the slope of the terrain, surrounding elevation differences, racking arrangement, row spacing, and so on. If row spacing is too narrow, front-row panels may cast shadows on rear rows. Because the sun’s altitude is lower in winter, shadows that are not problematic in summer can affect performance in winter. To stabilize power generation, it is necessary to consider layout conditions based on the sun’s movement throughout the year.

When checking the surrounding environment, it is important not to overlook sources of shading. Nearby buildings, trees, chimneys, rooftop equipment, lightning protection devices, fences, and mountain ridgelines can affect shading depending on the time of day and the season. In particular, at low sun angles in the morning and evening shadows stretch long, so a visual check conducted only during daytime may not be sufficient for assessment. Where possible, combine on-site inspections, drawings, photographs, and topographic information to clarify the shading conditions.

At this stage, what is important is to separate conditions that can be input from those that are difficult to input. Some shadows can be represented in detail in the simulation, while other factors can only be treated as simplified loss conditions. Rather than trying to reproduce everything perfectly, it is practical to prioritize the conditions that have a major impact on power generation and to ensure the accuracy necessary for decision-making.

Step 3: Set assumptions for solar radiation and weather conditions

Once you've organized the installation conditions, the next step is to set assumptions for solar irradiance and weather conditions. In photovoltaic power generation simulations, solar irradiance is a fundamental input parameter. Because solar panels convert the incoming irradiance into electricity, if the solar irradiance settings are inappropriate, the overall power generation forecast may be off.

When setting solar irradiance, it is important which period of data you use. Using data from only a single year can skew the results depending on whether that year happened to be particularly sunny or particularly cloudy. In practice, it is common to use long-term average meteorological data to estimate standard power generation. However, when performing performance comparisons or analyzing anomalous years, the irradiance conditions for the specific year are sometimes checked separately.

Ambient temperature also affects power generation. Solar panels tend to produce more electricity when solar irradiance is strong, but their output typically decreases as panel temperature rises. Therefore, in summer even with high irradiance, panels can suffer losses due to temperature increases. Conversely, in winter lower ambient temperatures can be advantageous for output, but shorter sunlight hours and a lower solar elevation mean that monthly generation varies depending on the region and installation conditions.

Wind speed, snowfall, snow accumulation, and dirt buildup are also factors that should be considered depending on the region. In snowy areas, power generation decreases when panels are covered by snow for periods of time. In regions with little rainfall where dust and sand easily accumulate, it is necessary to allow for losses due to surface soiling. However, even if these conditions are set in excessive detail, if the underlying basis is vague the reliability of the forecast will not increase. It is important to set reasonable assumptions based on local weather trends and operational plans.

When handling solar irradiance data, attention must also be paid to the difference between irradiance on a horizontal plane and irradiance on an inclined plane. Solar panels are often installed at a fixed tilt angle. Therefore, rather than using irradiance observed on the horizontal plane directly for estimating power generation, it is necessary to convert it to the irradiance received by the panel surface and evaluate it. If the azimuth or tilt angle changes, the amount of irradiance incident on the panel surface will vary even in the same region.

Also, when comparing simulation results, it is important to use the same weather conditions. For example, if different solar radiation data are used for each design option, it becomes difficult to tell whether the differences are due to layout and equipment configuration or to the differences in weather conditions. When comparing multiple options, standardizing solar radiation and weather conditions and limiting the items that are changed will make the results easier to interpret.

Step 4: Enter system capacity and panel layout conditions

After setting the solar irradiance and meteorological conditions, enter the system capacity and panel layout conditions. Here you organize the solar panel capacity, number of panels, installation orientation, tilt angle, the configuration for each mounting surface, and connection conditions. While power generation will in part increase proportionally with system capacity, losses arise from layout and connection conditions, so you should avoid judging solely by capacity.

When setting system capacity, verify not only the panels' rated output but also the actual number of panels that can be installed and any layout constraints. For rooftop installations, roof surface dimensions, clearances from edges, inspection walkways, interference with rooftop equipment, and waterproofing and load conditions are relevant. For ground-mounted installations, site boundaries, service access routes, racking spacing, maintenance access flow, and drainage conditions must be considered. Even if a layout appears feasible on drawings, it may need to be revised when actual construction and maintenance are taken into account.

Orientation and tilt angle influence seasonal fluctuations in output and the time-of-day generation profile. In general, selecting orientations and angles that receive more sunlight makes it easier to secure annual generation, but in practice the design must accommodate roof shapes and site constraints. Rather than relying solely on a near-south-facing arrangement, another approach is to distribute panels east–west to adjust biases in generation timing. If self-consumption is a priority, it is advisable to check not only the annual total but also whether the layout tends to generate during periods of high electricity demand.

If there are multiple installation surfaces, enter the conditions for each separately. If you combine panels with different orientations or tilt angles into a single surface, power generation trends and the effects of shading will be averaged and may deviate from reality. Especially for buildings with complex roofs, checking the generation for each surface makes it easier to identify surfaces with high and low installation effectiveness.

Connection conditions also affect power output. Because solar panels are connected by combining multiple modules, grouping panels that differ in shading or orientation on the same circuit can impact the overall output. In simulations, it may not be possible to reproduce the equipment configuration in greater detail than necessary, but it is important at least not to force together surfaces that receive very different shading or that face different orientations.

In this procedure, it is important to distinguish between the "maximum installable capacity" and the "practically operable capacity." If you prioritize maximum capacity too much, you may end up with layouts that are difficult to inspect, have significant shading, or carry higher construction risks. Power generation simulations should be used not only to increase capacity but also to verify the balance between generation efficiency and operability.

Step 5: Account for loss conditions such as shading, temperature, and conversion

One factor that often causes differences in solar power generation forecasts is the setting of loss conditions. If you calculate using only theoretical irradiance and system capacity, the estimated generation can end up higher than the actual output. In real-world installations, output is reduced by various factors such as shading, temperature rise, wiring, power conversion, dirt on panel surfaces, equipment downtime, and aging.

Shading losses can vary greatly depending on the site. We check shading sources separately, such as shadows from surrounding buildings and trees, localized shading from rooftop equipment, shading between panel rows, and morning/evening shadows caused by terrain. It is not enough to know simply whether shading exists; it is important to know in which season, at what times of day, and over what extent it occurs. If shading only appears in winter mornings and evenings versus occurring around midday when generation is high, the impact on annual energy production will be different.

Temperature-related losses should not be overlooked. Solar panels heat up when exposed to sunlight, and their output tends to decrease as panel temperature rises. If installed close to a roof, poor ventilation can cause panel temperatures to rise more easily. On ground-mounted systems or racks with good ventilation, temperature increases may be suppressed. In simulations, it is necessary to account for temperature conditions according to the installation configuration.

Losses from power conversion should also be included in the generated power. The direct current power produced by solar panels is typically converted into alternating current power suitable for use or grid interconnection. Certain losses occur during this process. In addition, if wiring distances are long or design conditions are not appropriate, voltage drops and losses from the wiring should also be considered. Because detailed setting values vary with equipment design, do not casually reuse standard values; verify that they are appropriate for the planned equipment configuration.

Losses from soiling vary depending on the region, the installation environment, and the cleaning schedule. In locations affected by sand dust, pollen, bird droppings, fallen leaves, or exhaust, soiling on the panel surface can impact power generation. Rain may wash some of it away, but on slopes or in environments where dirt tends to remain, regular inspection and cleaning should be assumed. In simulations, rather than estimating overly detailed figures, it is easier to treat them as realistic losses organized according to the site environment.

Furthermore, equipment aging is also important in long-term forecasts. Solar power generation systems do not retain the same performance they have immediately after installation indefinitely. When forecasting generation over a long period, it is necessary to anticipate a certain annual decline in output. Treating the generation in the first year after installation as the same as that several years or a decade later can lead to incorrect judgments about long-term profitability and maintenance planning.

The key point when setting loss assumptions is not to be overly optimistic. If you assume all losses will be minimal, you may produce forecasts that look good but tend to deviate significantly from actual results. Conversely, being excessively conservative can lead to underestimating the benefits of implementation. Separating and reviewing a standard case, a conservative case, and an improvement case makes the forecast easier to explain.

Step 6: Check Monthly and Annual Power Generation

After setting the input conditions and loss parameters, review the monthly and annual power generation from the simulation results. The important point here is not to stop at just looking at the total annual generation. Because solar power generation is affected by seasons and weather, checking month-by-month variations makes it easier to grasp the characteristics and risks of the system.

Annual generation is an easy-to-understand indicator for broadly assessing implementation effects and profitability. However, even if the annual total is the same, differences in how generation is distributed change its practical value. For systems intended for self-consumption, it is important whether generation can occur during periods or times of day when electricity demand is high. Even for systems aimed at selling power or operating a generation business, understanding seasonal biases in generation makes it easier to forecast revenue and plan inspections.

When reviewing monthly power generation, check the seasonal variation in solar irradiance, output reductions due to temperature, shading effects, and impacts from snow and the rainy season together. For example, even in seasons with high irradiance, losses from rising temperatures mean that summer does not necessarily produce the highest output. Also, in winter the sun’s elevation is lower and shadows are longer, making systems more susceptible to the influence of nearby obstructions. If you see an unnatural dip in monthly generation, it can be a prompt to review the input conditions and shadow settings.

When reviewing annual power generation, consider not only the first year but also long-term declining trends. Given equipment aging, the possibility of component replacement, downtime for maintenance, dirt accumulation, and changes in the surrounding environment, long-term forecasts involve a degree of uncertainty. When using long-term forecasts in internal documents or customer explanations, it is preferable to present not only a single figure but also the underlying assumptions and the expected range of variation.

Also, in the simulation results, it is advisable to check the capacity factor and the breakdown of losses. The capacity factor is used as an indicator of how much electricity is being generated relative to the installed capacity. However, since it varies depending on local solar irradiation conditions, installation orientation, and system configuration, it is important not to evaluate it simply as high or low, but to compare under the same assumptions. Looking at the breakdown of losses also makes it easier to see where there is room for improvement.

When reviewing results, pay attention to the units of the figures. If generated energy, installed capacity, solar irradiance, and loss rates are mixed together, misunderstandings can easily arise among stakeholders. When preparing documentation, clearly separate annual generation, monthly generation, installed capacity, and assumptions, and organize them so they can be compared using the same units. Simply aligning units and conditions significantly improves the explainability of the simulation results.

Step 7: Check the range of prediction variability using sensitivity analysis

In the final stage of the simulation, we perform a sensitivity analysis to check the range of variation in the forecasts. Sensitivity analysis is the process of slightly changing conditions such as solar irradiance, loss rates, shading, system capacity, and degradation rates to see how much the power generation changes. This allows you to understand which conditions strongly affect the results.

There is uncertainty in forecasting solar power generation. Future solar irradiance varies from year to year, and the surrounding environment may change. There are factors that cannot be fully predicted in advance, such as tree growth, construction of new buildings, soiling of equipment, equipment downtime, and differences in maintenance frequency. Therefore, in practice it is important not to treat a single forecast value as absolute, but to check expected generation under different conditions.

In a sensitivity analysis, first select the items likely to have the greatest impact on power generation. Assume site-level variations such as lower-than-standard solar irradiance, slightly increased shading losses, a revision of temperature losses, or increased losses due to soiling and downtime. Because changing all conditions at once makes it difficult to identify causes, it is easier to organize the results by changing one condition at a time at first.

For example, if shading is the factor that causes annual power generation to deviate significantly from the planned value, it is worth considering layout changes or eliminating the causes of shading. If temperature-related losses have a large impact, there is room to review ventilation and installation methods. If losses due to soiling are a concern, inspection and cleaning schedules should be included in the operating conditions. In this way, sensitivity analysis is not merely a verification task but an activity that leads to design and operational improvements.

Also, the results of sensitivity analysis are useful for explaining matters to stakeholders. If only the standard case is shown, it becomes difficult to explain when generation underperforms. Organizing a standard case, a slightly conservative case, and a favorable-conditions case can provide a range for decisions on deployment and financial viability. Especially for projects that involve investment decisions and long-term operation, it is important to understand not only upside in generation but also downside risk.

When performing sensitivity analysis, take care not to assume unrealistic conditions. Setting an extremely low loss rate or solar irradiation conditions that are unlikely in reality makes the results difficult to use as a basis for decision-making. Conversely, assuming overly pessimistic conditions prevents a correct evaluation of the benefits of installing equipment. It is important to vary conditions within a defensible range, taking into account site conditions, past performance, design conditions, and maintenance policies.

Key Points for Applying Prediction Results to Practical Decision-Making

A solar power generation simulation doesn't end with producing results. In practice, what's important is how those results are applied to design decisions, installation decisions, and operational planning. To turn forecast values into information that can be used on site, you need to organize not only the generation figures but also the assumptions, loss factors, risks, and potential for improvement.

First, when documenting simulation results, always retain the input conditions. By recording the installation site, system capacity, azimuth, tilt angle, assumptions for solar irradiance data, loss rates, how shading is treated, degradation rate, and so on, you can verify the results later. If only the generation figures remain without the conditions, they become difficult to reuse for design changes or re-evaluation.

Next, when comparing design proposals, it is important to align the comparison conditions. By keeping solar irradiance and weather conditions the same and then comparing differences in layout, capacity, tilt angle, shading countermeasures, etc., it becomes easier to determine which proposal is most advantageous. Comparisons made under inconsistent conditions may show large apparent differences in power generation that are actually due to differences in the underlying assumptions.

When using this for operational planning, identify the times of year when power generation tends to drop and the times of day that are more susceptible to shading. Monthly power generation forecasts are useful when deciding whether to concentrate inspections in seasons of reduced output, whether to avoid shutdowns during periods of high generation, and how to time cleaning and maintenance. Linking simulation results to maintenance plans leads to concrete actions to sustain power generation.

Simulations are also effective for verifying the performance of existing installations. If actual power generation falls significantly short of the predicted value, it is necessary to disentangle whether the cause was lower solar irradiance, increased shading, equipment malfunction, or the effects of soiling or downtime. However, when comparing with measured results, it is important to take the irradiance conditions during the target period into account. Directly comparing a forecast created under average weather conditions with the actual performance of a specific year can lead to incorrect conclusions.

When explaining power generation forecasts to stakeholders, it is desirable to avoid overly definitive wording. Simulation figures are estimates based on certain conditions and do not guarantee actual power generation. In presentation materials, showing the forecast values, assumptions, and main factors causing variability together makes realistic decision-making easier. Especially for customer-facing explanations, rather than emphasizing only favorable results, communicating variability due to shading, weather fluctuations, and maintenance conditions also helps build trust.

Summary

To forecast solar power generation in advance, it is necessary to comprehensively consider not only the system capacity but also the installation location, solar irradiance, weather conditions, azimuth, tilt angle, shading, temperature, conversion losses, soiling, and aging. Simulations are a convenient tool, but if the input conditions remain ambiguous, the results become difficult to use for practical decision-making.

The basic workflow is to first decide the objective and evaluation criteria, then organize the installation site and surrounding environment and set the solar irradiance and meteorological conditions. On that basis, input the system capacity and panel layout, and estimate loss conditions such as shading, temperature, and conversion. Once you have the results, check not only the annual energy generation but also the monthly generation and the breakdown of losses, and finally use sensitivity analysis to grasp the range of prediction variability. By carrying out this sequence carefully, the generation forecast becomes not just a number but decision-making material that can be used for design and operation.

In practice, it is more important to produce realistic forecasts based on explainable assumptions than to make expected power generation appear high. Checking not only upside deviations in power generation but also downside risks and the potential for improvement through maintenance makes it easier to develop plans that take post-installation operations into account. In particular, because shading, soiling, and inspection status vary from site to site, it is essential to adopt an approach that links simulation results with on-site information when making judgments.

If you want to take solar power generation forecasts beyond the design phase and use them for post-construction verification and operational improvement, it is helpful to continuously record on-site data and establish a system that can compare actual generation with projections. By linking and managing design-stage simulations, construction-time condition records, and operational generation results, you can more easily notice changes in output and make decisions about improvements.