Solar power generation simulation is not just a simple calculation to decide equipment capacity. It is an important review task to predict power generation for projects with different installation conditions—houses, factories, warehouses, stores, idle land, public facilities, etc.—and to connect those predictions to investment decisions, design decisions, power usage planning, and preparation of explanatory materials. However, even if you assume the same roof area or the same equipment capacity, simulation results can vary greatly depending on the accuracy of input conditions and on-site verification. Overestimating generation increases the risk that expected benefits will not materialize, while underestimating it may cause you to miss the introduction effects that could actually be obtained.

This article organizes ten factors that affect accuracy for practitioners who are researching "solar power generation simulation." Rather than simply understanding the formulas, it explains from a practical perspective which input conditions strongly influence results, what to check during on-site surveys, and what to look at when comparing multiple proposals—so that it can be used in actual work.

# Table of contents

• Why the accuracy of solar power generation simulation matters

• Overview of the ten factors that determine accuracy

• Factor 1: Reliability of irradiance data

• Factor 2: Orientation and tilt angle of the installation site

• Factor 3: Shading effects and surrounding environment

• Factor 4: Panel layout and usable installation area

• Factor 5: Equipment capacity and conversion efficiency settings

• Factor 6: Output reduction due to temperature rise

• Factor 7: Electrical system loss settings

• Factor 8: Handling of aging degradation and long-term performance

• Factor 9: Assumptions about self-consumption, feed-in, and load patterns

• Factor 10: Accuracy of on-site surveying and input dimensions

• How to proceed with practical checks to improve accuracy

• Summary

# Why the accuracy of solar power generation simulation matters

The purpose of solar power generation simulation is to predict future generation as realistically as possible. Forecasts of generation affect many decisions: equipment scale, whether to introduce the system, financial planning, power contracts, the need for storage equipment, internal approvals, and customer explanations. Therefore, it is insufficient to simply output “how much will be generated annually.” It is necessary to confirm under which assumptions the results were calculated, whether those assumptions match on-site conditions, and whether the figures account for risks.

In practice, special attention must be paid to the fact that simulation results can easily be taken at face value. When a report lists annual or monthly generation, those numbers can be received as if they were fixed. However, solar power generation is influenced by weather, seasons, installation angle, temperature, shading, equipment losses, and operating conditions, so simulations are only predictions under certain conditions. If the assumptions are crude, the results will be crude as well.

For example, if the roof orientation is entered as more south-facing than it actually is, if the shadow from adjacent buildings is overlooked, or if areas where panels cannot be installed are counted as available area, the generation tends to be overestimated. Conversely, if shading or losses are assumed too conservatively, the benefits of introduction may be undervalued. Improving accuracy is not about producing optimistic numbers or skewing figures conservatively; it is about appropriately reflecting on-site and operational conditions to produce figures useful for decision-making.

In small residential projects, roof shape, orientation, shading, and electricity usage patterns have a greater impact on results. For corporate or industrial projects, site shape, panel layout, incoming power equipment, self-consumption rate, differences in holidays and operating hours, and assumptions about maintenance are important. In other words, the weighting of accuracy factors varies by project. Practitioners must not only look at the calculation results themselves but also discern “which factors are likely to be sources of error in this project.”

# Overview of the ten factors that determine accuracy

The accuracy of solar power generation simulations is determined by the accumulation of multiple factors. Even if irradiance data are accurate, results will be off if orientation or tilt angle are wrong. Even if shading is carefully assessed, annual generation estimates change if panel layout or electrical loss settings do not reflect reality. Therefore, it is not enough to be strict about a single item; it is important to align all conditions related to generation as a whole.

The factors that affect accuracy can be broadly categorized into five groups: natural conditions, installation conditions, equipment conditions, operational conditions, and on-site information. Natural conditions include irradiance, temperature, snowfall, rainfall, and regional characteristics. Installation conditions include orientation, tilt angle, roof shape, topography, surrounding buildings, trees, and racking layout. Equipment conditions relate to panel output, conversion efficiency, power conversion devices, wiring, equipment losses, and temperature characteristics. Operational conditions include self-consumption, feed-in, downtime, maintenance, cleaning, and power usage patterns. And underpinning all of these is the accuracy of on-site surveying and dimensional information.

In practice, it may be difficult to investigate everything perfectly from the outset. In initial studies, simulations use approximate conditions to assess the feasibility of installation. Subsequently, on-site surveys, drawings review, surrounding environment checks, and compilation of power usage data are performed to gradually improve accuracy. What is important is to be clear about which stage the simulation corresponds to. Using early-stage results for final decisions can lead to large discrepancies later.

Also, improving accuracy requires managing the basis for input values as well as generation figures. Recording which location’s weather data were used, whether roof dimensions came from drawings or measurements, which season/time the shading was confirmed for, and whether loss rates are standard values or tailored to site conditions makes later verification easier. Simulations are not created once and finished; they should be updated according to design changes and on-site confirmation.

# Factor 1: Reliability of irradiance data

Irradiance data are the foundation of solar power generation simulations. Because solar power converts energy from the sun into electricity, the amount of irradiance that can be expected is the most fundamental condition affecting annual generation. Simulation results change depending on how you handle regional irradiance, monthly variations, seasonal differences, and weather trends.

One point to note about irradiance data is the representativeness of the location used. For example, even within the same municipality, coastal areas, mountainous areas, basins, and urban areas can have different cloud patterns, temperatures, snowfall, and fog occurrence conditions. Using only broad-area data may fail to adequately reflect site-specific conditions. For initial studies, regional averages may be acceptable, but the closer you get to a final decision, the more desirable it is to use conditions near the installation site.

Also, judging irradiance from a single year’s data can be biased. If a given year is unusually sunny, generation will be high; if it has many rainy or cloudy days, generation will be low. Therefore, you must clarify whether you will base assumptions on long-term averages or consider recent meteorological trends. In practice, the choice of irradiance assumptions differs depending on whether the goal is to produce a standard annual generation estimate or a conservative planning figure.

Furthermore, it is important whether you use horizontal-plane irradiance or treat it as irradiance on a tilted surface. Solar panels are generally installed at angles corresponding to roofs or mounts, not horizontally. Simple horizontal-plane irradiance alone may not adequately represent the irradiance actually received by panel surfaces. Estimating panel-plane irradiance appropriately based on orientation and tilt angle is the first step toward improving simulation accuracy.

# Factor 2: Orientation and tilt angle of the installation site

Panel orientation and tilt angle directly affect generation. In general, the closer the orientation and angle are to conditions that receive sunlight easily, the higher the annual generation tends to be. However, optimal conditions vary by region, roof shape, surrounding environment, and timing of power use. Rather than rigidly assuming “south-facing is best” or “this angle is best,” it is important to evaluate according to actual installation conditions.

Inputting orientation incorrectly is a common cause of large simulation errors. Mistaking the upward direction on a drawing for north, or having ambiguous orientation checks on site, can lead to calculations based on a different direction than reality. Especially for houses with multiple roof planes or buildings with complex shapes, you need to enter orientation for each plane. Summarizing with one average orientation obscures differences in which planes generate more in the morning, at noon, or in the afternoon.

Tilt angle is equally important. Shallow and steep roof slopes produce different seasonal generation trends. Low angles may perform relatively better in summer, while higher angles may receive more winter irradiance. In snowy regions, however, you must also consider snow-shedding, wind effects, and mount safety. In practice, tilt angle should be decided considering not only generation but also constructability and maintenance.

For corporate projects, the approach differs between installing directly on roof planes and installing racking on flat roofs. On flat roofs, increasing angle can improve irradiance conditions, but it may require row spacing to avoid mutual shading. Even if steeper angles increase per-panel generation, a reduced number of panels can mean total generation does not increase. Therefore, orientation and tilt angle should be checked together with the layout plan, not in isolation.

# Factor 3: Shading effects and surrounding environment

Shading is an extremely significant factor affecting the accuracy of solar power generation simulations. Causes of shading vary by site: buildings, trees, utility poles, billboards, rooftop equipment, chimneys, railings, neighboring structures, and mountain ridgelines. Shading may occur only during certain times of day or change greatly with the seasons. In particular, because solar altitude is lower in winter, shading that wasn’t problematic in summer can affect generation.

What makes shading assessment difficult is that a single on-site observation is often insufficient to judge shading impacts over the year. Even if the site visit occurs on a sunny day, if no shadows are present at that time, you may overlook shading risks. Conversely, if shadows are present during the survey, you cannot determine how much they will affect annual generation without considering season and time of day. Simulations should reflect shading occurrence areas and times as realistically as possible.

Pay attention to small shadows on roofs as well. For example, shadows from ventilation equipment, piping, antennas, or parapet upstands may be small in area but can affect panel efficiency if they cover part of a panel. In panel systems where multiple modules operate together, a shadow on part of the array can affect total output. Therefore, it is important not only to look at shadow area but to confirm which panels are shaded, at what times, and to what extent.

Surrounding environments may change in the future. New buildings on adjacent land, growing trees, added rooftop equipment, or changes in site usage can cause shading that did not exist at installation time. While you cannot predict every future change, if there is adjacent land with clear development potential or nearby trees likely to grow, you should note this as a risk when interpreting simulation results.

# Factor 4: Panel layout and usable installation area

In solar power generation simulations it is important to correctly estimate the number of panels or capacity that can be installed. The premise for that is the usable installation area. Even if a roof or site area looks large, not all of it can actually be used for panels. Considering architectural constraints, inspection corridors, fire separation distances, rooftop equipment, load conditions, drainage routes, construction space, and areas to avoid shading, the available area can be smaller than assumed.

A common source of error is treating the drawing’s area as the panel installation area. Especially with old or simplified drawings, actual roof dimensions, upstands, slopes, and obstacle positions may not be accurately reflected. Also, assuming panels can be placed up to the roof edge can cause issues during construction or maintenance. Simulations should assume the area that can actually be installed safely and appropriately.

Panel layout affects generation not only by orientation and angle but also by row spacing. On flat roofs or ground-mounted installations, front rows can shade rear rows. Tighter row spacing increases installed capacity but may increase shading losses. Conversely, wider spacing reduces shading but may reduce installed capacity. The optimal layout should be decided considering not only capacity per unit area but also annual generation, peak output, constructability, and maintainability.

When splitting panel orientations, you can also manage different generation times. An east–west divided layout may have lower peak generation than a single south-facing surface but can better utilize morning and evening generation. For projects prioritizing self-consumption, it is important to consider not only maximum daytime generation but the alignment of generation times with demand. Layout planning is closely related to both generation and how power is used.

# Factor 5: Equipment capacity and conversion efficiency settings

Equipment capacity is a basic condition that greatly affects simulation results. Generally, larger installed capacity yields more annual generation, but simply increasing capacity is not always the right approach. You must set appropriate capacity considering installation area, orientation, tilt angle, shading, connection conditions, converter capacity, and the purpose of power use. If the entered capacity differs from the actual design, generation forecasts will deviate accordingly.

Nominal panel output and conversion efficiency are also important for realistic generation estimates. Catalog outputs are based on fixed test conditions, and actual outdoor environments are affected by irradiance, temperature, dirt, shading, wiring, and equipment losses. Therefore, simulations need to account for operational losses as well as entering panel performance values. Assuming nominal output is always fully realized tends to lead to overestimation.

You should also check the balance between panel capacity and converter capacity. The combination of panel capacity and converter capacity can limit peak output on sunny days. How much of this limitation is acceptable depends on annual generation, equipment utilization rate, cost-effectiveness, grid connection conditions, and the purpose of self-consumption. Even if some output is curtailed at peak times, it may not be a major issue over the year. Conversely, excessive capacity settings can lead to lost generation opportunities.

Moreover, accuracy differs when you input generic equipment specifications versus specifications of the actual equipment to be adopted. In early stages, comparing standard conditions may be acceptable, but as you approach final design, it is important to reflect actual equipment specifications, connection methods, and operating ranges. Equipment capacity and conversion efficiency may look like simple numbers in simulations, but they reflect the overall design approach.

# Factor 6: Output reduction due to temperature rise

Solar panels tend to generate more when irradiance is strong, but their output decreases as panel temperature rises. On sunny summer days, while irradiance is high, panel surface temperature can become high and output may not increase as much as expected. Failing to properly account for temperature-related output reduction can lead to overestimation of generation, especially in regions or roof conditions prone to high temperatures.

Temperature effects also vary with installation method. When panels are installed close to roofing material, there may be little ventilation behind the panels and heat may accumulate. Conversely, if racking creates space behind panels and allows wind to flow, temperature rise can be suppressed. Roof material, roof color, surrounding airflow, building height, and nearby obstacles also affect temperature conditions.

In simulations, it is important to consider not only air temperature data but how panel temperature is estimated. Even with the same ambient temperature, panels heat up more on strong-irradiance, low-wind days and cool more on windy days. Simplified simulations sometimes treat temperature loss as a constant value, but detailed studies consider monthly or hourly temperature effects to achieve more realistic results.

Temperature losses are easily overlooked but accumulate into differences in annual generation. Large corporate roofs can become hot overall, so it is necessary to carefully examine actual generation characteristics. If peak summer generation is important, check temperature-related output reduction along with irradiance.

# Factor 7: Electrical system loss settings

Electricity generated by panels passes through junction boxes, power converters, distribution equipment, and incoming power equipment before it can be used. During this process, wiring resistance, conversion losses, connection losses, and equipment standby losses occur. If these losses are not set appropriately in simulations, there will be discrepancies with the usable or sellable electricity actually available.

Wiring losses depend on wiring length, conductor size, current, voltage, and routing. On large sites or projects where panels are far from incoming power equipment, wiring distances tend to be long and losses may not be negligible. If wiring routes are undecided in initial studies, a standard loss rate may be used, but detailed design should review this according to actual routes and capacities.

Conversion efficiency of power conversion equipment is also important. Converting generated DC to usable AC involves losses. Conversion efficiency can change depending on operating output range and does not always run at maximum efficiency. Considering real operating conditions—low output operation in mornings/evenings or cloudy conditions, and high output operation on sunny days—means a single uniform value may not fully represent performance.

Additionally, you should consider generation opportunity losses due to equipment downtime or maintenance. Scheduled inspections, grid-side work, equipment failures, protective operation, cleaning, and renovation can all create periods when generation is not possible. Standard generation simulations often assume equipment is operating normally, so how much operational downtime risk you account for should be decided based on the scale and importance of the project.

# Factor 8: Handling of aging degradation and long-term performance

Solar power equipment is intended to operate for many years, not just in the first year after installation. Therefore, it is important to check not only first-year generation but long-term generation that takes aging degradation into account. Panel output gradually decreases over time, so even with the same irradiance conditions, generation tends to decline year by year.

If you only look at first-year generation in simulations, you may overestimate long-term returns and self-consumption effects. When deciding on introduction, you need to understand not only whether the first-year number looks good but what generation you can expect in several or many years. For corporate projects—where recovery of capital investment, long-term electricity cost savings, and valuation of environmental benefits are involved—considering aging is essential.

For degradation settings, you may use a standard degradation rate or set it according to the performance warranties and specifications of the chosen equipment. In either case, assuming zero degradation is unrealistic. Additionally, soiling, salt damage, snow, bird damage, environment-driven degradation, and differences in maintenance affect how performance deterioration actually occurs. It is important to consider environmental conditions for coastal areas, industrial zones, agricultural surroundings, and tree-rich locations.

When assessing long-term performance, check not only the annual generation trend but also assumptions about maintenance. Actual generation is affected by how frequently cleaning and inspections are performed, whether there is a system to detect faults early, and whether equipment replacement or repair plans exist. To improve simulation accuracy, reflect operational management realism as well as equipment performance.

# Factor 9: Assumptions about self-consumption, feed-in, and load patterns

In solar power generation simulations, how generated electricity is used is as important as generation itself. In recent years, the self-consumption ratio—using generated electricity within one’s own facility or home—has attracted attention in introduction studies. Even with the same annual generation, the benefits differ between facilities with high daytime demand and those with demand concentrated at night or on holidays.

When considering self-consumption, the accuracy of load patterns is important. Monthly electricity usage alone may not sufficiently capture how much electricity is used during daytime, whether there is demand on holidays, or seasonal variations. Since solar generation mainly occurs during daytime, the degree to which daytime demand aligns with generation affects self-consumption ratio. Factories, warehouses, stores, offices, schools, and welfare facilities are strongly affected by differences in operating hours and holidays.

If you assume feed-in, your view of generation changes as well. Whether you assume all generated power is self-consumed, that surplus is exported, or that output control is possible will change the expected benefits. Simulations can show how much energy can be generated, but that electricity may not all be effectively used. It is necessary to combine generation simulations with power usage planning.

When using storage equipment or control devices, assumptions become more complex. Objectives vary—using surplus daytime electricity after sunset, reducing peak power, preparing for emergencies—so optimal operation differs depending on the purpose. Judging based solely on annual generation may fail to properly evaluate the benefits of storage and control. Viewing generation, usage, timing, and control rules together improves practical simulation accuracy.

# Factor 10: Accuracy of on-site surveying and input dimensions

The last important element that supports simulation accuracy is the accuracy of on-site surveying and input dimensions. Even if you carefully set irradiance and equipment performance, results will deviate from reality if roof dimensions, site shape, orientation, obstacle positions, or elevation differences are incorrect. Simulations calculate based on the entered conditions, so high-quality on-site information is essential for reliable results no matter how advanced the calculations are.

For roof installations, you need to accurately grasp roof plane length, width, slope, eave and ridge positions, upstands, equipment positions, and inspection spaces. For ground-mounted installations, site boundaries, grading shape, elevation differences, existing structures, drainage routes, access routes, fences, vegetation, and relationships with adjacent land are important. If you create layout drawings with these details unclear, panel counts may be reduced or layouts changed during construction.

Survey accuracy also relates to shading assessment. If obstacle heights and positions are not accurate, you cannot correctly estimate shadow extents or times. Rooftop equipment heights, heights of adjacent buildings, positions of trees, and terrain undulations directly affect shading evaluation in simulations. A simple site check may miss fine shading risks.

Also, drawings and the actual site do not always match. Renovations, added equipment, roof repairs, and changes to exterior works can make on-hand drawings differ from the actual state. When performing simulations based solely on old drawings, on-site confirmation to identify differences is indispensable. In practice, combine drawings, on-site photos, survey data, orientation checks, and obstacle information to substantiate input conditions.

# How to proceed with practical checks to improve accuracy

To improve simulation accuracy, it is effective to raise the level of confirmation step by step, rather than diving too deeply into details from the start. In the initial stage, use the installation location, approximate area, orientation, assumed capacity, and standard irradiance to get a rough sense of generation. The purpose at this stage is to confirm feasibility and the overall scale. Showing numbers with too much detail can make as-yet-uncertain conditions appear fixed, so exercise caution.

In the next stage, reflect on-site conditions. Confirm roof or site dimensions, areas where installation is not possible, shading causes, surrounding environment, equipment positions, and power usage patterns, and update simulation conditions. It is important to grasp differences before and after changes. What looked like sufficient generation in a rough estimate may see reduced capacity when shading and installation constraints are included. Conversely, on-site confirmation might reveal more usable area than assumed.

In detailed studies, organize equipment conditions, wiring losses, conversion losses, temperature losses, degradation, and self-consumption conditions. For corporate projects, it is desirable to examine power usage data not only monthly but, as far as possible, daily or hourly. Checking the overlap of generation and demand makes self-consumption rates and surplus power estimates closer to reality. In residential projects, whether occupants are at home during the day, use of hot water or air conditioning, and possible future additions of electrical appliances can all have an impact.

When reporting simulation results, do not present a single number; make the assumptions clear. Organize annual and monthly generation, self-consumption, surplus energy, loss items, shading impacts, and long-term degradation assumptions, and be able to explain under which conditions the numbers were calculated. Especially for internal approvals and customer explanations, unclear bases for numbers make it difficult to explain when conditions change later.

In practice, comparing multiple assumptions—such as optimistic, standard, and conservative cases—is also effective. Viewing only one result makes it easy to overlook errors and risks. By checking multiple cases, you can see how much irradiance variability, shading impact, equipment capacity differences, and self-consumption rate changes affect results. This makes generation simulations more useful as comparative materials for decision-making rather than mere forecasts.

# Summary

The accuracy of solar power generation simulations is not determined by irradiance data alone. Orientation, tilt angle, shading, panel layout, equipment capacity, temperature losses, electrical system losses, aging degradation, self-consumption assumptions, and the accuracy of on-site surveying all accumulate to affect results. Overlooking even one element can change annual generation estimates and expected introduction benefits.

What practitioners should emphasize is not accepting simulation numbers at face value, but confirming from which assumptions those numbers were derived. Required accuracy differs depending on whether the simulation is at an initial estimate stage, after on-site survey, or at detailed design stage. In initial studies, a rough judgment may be sufficient, but when proceeding to introduction decisions, contracts, or construction planning, high-accuracy simulations that reflect on-site data and operational conditions are necessary.

In particular, shading impact, usable installation area, roof and site dimensions, and obstacle positions cannot always be grasped through desk-based study alone. Accurately measuring on-site conditions and digitizing actual shapes and positional relationships—rather than relying only on drawings or photos—substantially increases simulation reliability. To improve generation forecast accuracy, it is essential not only to know how to use calculation tools but also to improve the quality of the on-site data entered.

When you want to more accurately capture roof shapes, site boundaries, obstacle positions, orientation, and installation ranges in solar design or proposals, improving the efficiency of on-site positioning is also an important consideration. LRTK supports on-site acquisition of location information and surveying work as a GNSS high-precision positioning device that can be attached to an iPhone. In situations where you want to accurately organize on-site dimensions and layout conditions that form the basis of solar power generation simulations, leveraging such high-precision positioning environments can reduce discrepancies between desk calculations and on-site realities and lead to generation forecasts that are more useful in practice.