How to Predict Power Generation Declines in Solar Power Generation Simulations

In solar power generation simulations, it is important not only to check how much can be generated annually but also to anticipate under what conditions generation will decline. Even if pre-installation forecasts expect sufficient generation, actual output can drop due to shading, temperature, soiling, snow, equipment degradation, equipment outages, changes in electricity usage patterns, and so on. This article explains, from a practical perspective for practitioners who search for "solar power generation simulation," how to check and predict generation declines.

Table of contents

• The purpose of predicting generation declines

• Classifying causes of generation declines

• Reading seasonal variations from monthly generation

• Anticipating declines from shading and surrounding obstacles

• Anticipating declines from temperature, soiling, and snow

• Reading long-term declines from equipment degradation and outages

• Checking effects on self-consumption and surplus electricity

• Adjusting predictions to reflect pre- and post-construction condition differences

• Keeping benchmarks usable for post-installation performance management

• Summary

The purpose of predicting generation declines

The purpose of predicting generation declines in solar power generation simulations is to reduce the gap between post-installation performance and pre-installation forecasts. Solar power generates when irradiance is available, but site conditions are not always ideal. Even in regions with high irradiance, shading reduces output. Even if installed capacity is large, panel temperature increases can reduce output. Even if there are no problems at installation, soiling, changes in the surrounding environment, and equipment aging can cause long-term generation declines.

Pre-installation simulations may show large annual generation figures. However, unless you check what kinds of losses are assumed in those figures, you cannot judge how much will actually be generated. If you anticipate the causes of generation declines in advance, you can avoid excessive expectations and make more realistic installation decisions.

Predicting generation declines is not simply about making conservative estimates. If you understand which factors will reduce generation, you can more easily design countermeasures. If shading is the cause, you can review layout and mounting surfaces. If soiling is the cause, you can plan cleaning and inspections. If temperature rise is likely to be a problem, you can review ventilation and installation methods. If there is a risk of equipment stoppage, you can consider maintainability and monitoring arrangements.

Predicting generation declines also affects profitability and decisions about electricity bill reduction. If annual generation falls, self-consumption and surplus electricity will change. In projects aimed at self-consumption, the impact on electricity bill savings depends on whether the decline overlaps with facility demand periods. If generation declines occur during periods that originally had surplus, the impact may be limited. On the other hand, if generation falls during periods of high facility demand, the reduction in purchased electricity will be smaller.

When predicting generation declines in solar power generation simulations, it is important to check not only total generation but also the causes of decline, when the decline will occur, at what times of day it will occur, and how it will affect self-consumption. This makes the simulation practical for pre-installation decisions, pre-construction checks, and post-installation performance management.

Classifying causes of generation declines

To predict generation declines, you must first classify the causes. There is not a single reason why solar generation drops. Many factors are involved, such as variations in irradiance conditions, shading, temperature rise, soiling, snow, equipment aging, equipment outages, wiring and conversion losses, changes in construction conditions, and changes in facility electricity usage patterns.

The first distinction to make is between declines due to natural conditions and those due to equipment conditions. Natural conditions include weather, irradiance, seasonal variation, temperature, and snowfall. These cannot be fully controlled, but reflecting them in the simulation brings monthly and annual generation estimates closer to reality.

Equipment-condition-related declines include shading, orientation, tilt, wiring, power conversion, panel surface soiling, equipment aging, and equipment outages. These can often be mitigated to some extent through design and maintenance. Avoiding shaded areas, arranging components for easier inspection, rationalizing wiring routes, and anticipating dirty environments are measures that can reduce the risk of generation declines.

Next, separate short-term declines and long-term declines. Short-term declines include cloudy weather, rain, snow cover, temporary shading, soiling accumulation, and equipment stoppages. Long-term declines include aging, growth of surrounding trees, construction of surrounding buildings, addition of rooftop equipment, and accumulation of dirt from poor maintenance. Even if there are no problems immediately after installation, conditions may change after several years, so a long-term perspective is necessary.

Also distinguish factors that reduce generation itself from those that reduce usable electric energy. Shading and temperature losses directly reduce generation. On the other hand, even if generation is sufficient, self-consumption will not increase if facility demand timing does not match. This is slightly different from generation decline but is important as a reduction in the installation effect. In solar power generation simulations, generation and self-consumption should be examined separately.

By classifying causes, the items to check in the simulation become clear. Shading is checked by monthly and time-of-day generation. Temperature losses are checked by summer generation and loss rates. Soiling and snow are checked via surrounding environment and regional conditions. Aging is checked with long-term simulations. Effects on self-consumption are checked by overlap of generation and consumption timing.

To predict generation declines correctly, do not simply consolidate causes into a single loss rate; interpret when, where, and how much each factor will affect performance.

Reading seasonal variations from monthly generation

Checking monthly generation is indispensable when predicting generation declines. Annual generation is useful for grasping the overall estimate, but it does not show in which months generation falls. Solar generation conditions vary greatly by season because of differences in sunshine hours, solar altitude, irradiance, temperature, weather, snow, and the length and angle of shadows.

By looking at monthly generation, you can identify periods prone to declines. In winter, sunshine hours are shorter and solar altitude is lower, so shadows from surrounding buildings, rooftop equipment, and trees tend to extend. In snowy regions, panels covered with snow can create periods when generation stops. Winter declines should be checked not only for lower irradiance but also for shading and snow effects.

Summer appears to be a period with high irradiance and increased generation, but high panel temperatures can reduce output. Strong irradiance does not necessarily mean maximum generation. If summer generation is estimated to be very high, confirm whether temperature losses have been reflected.

Rainy seasons, typhoon seasons, and periods with many cloudy days also tend to reduce generation. In some regions, specific months may show significant dips in annual generation. If monthly generation in the simulation looks unnaturally stable, regional characteristics may not be sufficiently reflected.

When reading monthly generation, overlay it with the facility's monthly electricity consumption. If the facility demand is high in months with low generation, the effect on electricity bill savings is reduced. For example, if a facility with high winter demand has low winter generation, the annual generation may be sufficient but self-consumption benefits could be smaller than expected. Conversely, a facility with high summer air-conditioning demand and high summer generation will more easily realize self-consumption benefits if generation declines are limited.

Monthly generation also provides clues to causes of decline. If generation drops substantially in winter, check sunshine hours, shading, and snow. If it fails to increase in summer, check high temperatures, soiling, tilt angle, and ventilation. If a particular month is notably low, check regional weather and simulation assumptions.

Looking at annual generation alone makes it easy to overlook when and why declines occur. Checking monthly generation helps grasp seasonal risks and plan countermeasures before installation.

Anticipating declines from shading and surrounding obstacles

A representative cause of generation decline is shading and surrounding obstacles. When a solar panel is shaded, the amount of irradiance it receives decreases and generation falls. Simulations that do not adequately account for shading can overestimate annual generation.

Sources of shading differ between rooftop and ground-mounted projects. For rooftop projects, surrounding buildings, rooftop structures, air-conditioning equipment, piping, exhaust equipment, guardrails, antennas, signs, and upstands around skylights create shading. For ground-mounted projects, trees, utility poles, nearby structures, slopes, terrain elevation differences, and neighboring buildings are shading sources.

Shading changes by time of day and season. In the morning, obstacles to the east produce shade; in the evening, obstacles to the west do. Shadows lengthen in winter when the solar altitude is low. Even if a site has little visible shading in summer, significant shading may occur in winter. Therefore, confirm whether the solar power generation simulation accounts for winter and morning/evening shading.

The impact of shading cannot be judged solely by shaded area. The effect on generation changes depending on the time the shade occurs, panel layout, circuit configuration, and the shaded position. Even short-duration shading can have a large impact if it occurs during the mid-morning to early afternoon when generation is high. If shading occurs during peak facility demand hours, it also affects self-consumption.

A useful method to predict shading impacts in simulations is to compare generation with and without shading. Also, checking generation by mounting surface, monthly generation, and time-of-day generation curves helps identify which surfaces or times are affected by shading. If generation decreases when shading is reflected, that result is closer to reality.

Surrounding obstacles affect not only shading but also soiling and maintainability. Trees nearby increase the risk of fallen leaves and bird issues. Installing panels near rooftop equipment can make inspection and cleaning difficult. For land projects, placing panels near trees or slopes can increase the burden of weeding and maintenance access.

When predicting generation declines, treat shading not merely as a temporary factor but as a condition affecting the entire installation plan. Properly accounting for shading reduces post-installation generation gaps and produces more reliable simulations.

Anticipating declines from temperature, soiling, and snow

When predicting generation declines in simulations, you also need to check the impacts of temperature, soiling, and snow. These factors may be less visible than shading but significantly affect actual generation.

First, temperature-induced declines. Solar panels generate from irradiance, but higher panel temperatures can reduce output. Rooftops in particular tend to become hot, and poor ventilation can increase temperature losses. If summer generation is estimated to be high in a simulation, check whether high-temperature output reductions are included.

Temperature losses are easier to identify in monthly generation. While generation tends to increase in summer with higher irradiance, efficiency can drop due to high temperatures. Spring and autumn often offer better balance between irradiance and temperature, leading to stable generation. Confirm whether losses from ambient temperature are considered, not just irradiance.

Next, declines from soiling. If dust, pollen, fallen leaves, bird droppings, or exhaust-related dirt adheres to panel surfaces, received irradiance decreases and generation falls. Locations with many nearby trees, nearby unpaved areas, dust-prone facilities, or places where birds congregate have a higher risk of soiling.

Soiling relates to maintainability. If the layout allows easy inspection and cleaning, soiling-related declines are easier to suppress. Conversely, if rooftop access paths are absent, management paths are insufficient for ground projects, or obstacles surround panels, cleaning and inspection become difficult. In predicting generation declines, consider soiling propensity together with cleaning accessibility.

Snow-related declines are important in some regions. Snow on panels creates periods with no generation. The impact varies with roof pitch, panel angle, how easily snow slides off, space for piled snow, and ease of snow removal. If winter generation is estimated to be high, confirm whether snow impacts are adequately accounted for.

Temperature, soiling, and snow are factors that vary greatly by site. Handling them with general loss rates can produce inaccurate results. In simulations, reflect local roof conditions, surrounding environment, regional characteristics, and maintenance plans to realistically anticipate generation declines.

Reading long-term declines from equipment degradation and outages

When predicting generation declines, do not overlook long-term equipment degradation and outages. Solar power systems are installed for long-term use, and judging solely by first-year generation can misrepresent long-term operation.

Equipment degradation means that generation performance and equipment condition change over time. Panels, wiring, connectors, and power conversion equipment all change condition over long-term operation. Confirm whether the simulation’s generation figures are first-year forecasts or projections that include long-term changes.

Simulations that ignore aging can appear optimistic for long-term generation. For investment decisions, breakeven analysis, and annual financials, it is important to forecast generation changes over multiple years, not only the first year. Check what assumptions are made about how generation will change year by year.

Outages causing declines are also realistic factors. Regular inspections, equipment abnormalities, replacements, communication faults, inspections of connection equipment, surrounding construction, and power outages can cause periods of no generation. It is difficult to predict everything accurately in advance, but assuming ideal continuous operation tends to increase gaps with actual post-installation performance.

Outage risk is also related to maintainability and monitoring. Whether inverters and connection equipment are installed in locations easy to access, whether inspection routes are secured, and whether the configuration allows easy verification when abnormalities occur can affect outage duration. Even at the simulation stage, confirm whether the layout facilitates maintenance.

Long-term generation declines are also affected by changes in the surrounding environment. If trees grow and shading increases, nearby buildings are erected, rooftop equipment is added, or the surrounding environment becomes dirtier, generation may fall compared to initial simulations.

To read long-term declines, consider not only first-year generation but also aging, equipment stoppages, maintainability, and changes in the surrounding environment. Solar power generation simulations can be used to check not only installation-time generation but also long-term operational outlooks.

Checking effects on self-consumption and surplus electricity

When predicting generation declines, you need to check the effects not only on generation itself but also on self-consumption and surplus electricity. The benefit of installing solar power depends greatly on how much of the generated electricity can be used within the facility. When generation falls, it is important to separate whether the reduction affects self-consumption or merely reduces surplus.

Self-consumption is the portion of generated electricity used within the facility. For projects aimed at reducing electricity bills, a drop in self-consumption directly reduces the installation benefit. For example, if shading or temperature losses reduce generation during daytime hours with high demand, the reduction in purchased electricity may be small.

On the other hand, if generation declines occur during times when surplus was abundant, the impact on installation benefits can be relatively small. If generation decreases during times when the facility could not use all the generated power, only surplus declines and self-consumption may be little affected. Thus, the impact of generation decline depends on when generation decreases and the facility load pattern.

This check requires overlaying time-of-day generation and demand. Knowing only the percentage drop in annual generation is insufficient to judge practical impacts. Check whether generation drops in the morning, around midday, or in the evening, and in which seasons. If facility demand exists in those time windows, you can judge the impact on self-consumption.

Changes in surplus electricity are also important. If surplus is reduced by generation decline, the installed capacity may have been oversized. However, if self-consumption is also reduced, generation decline negatively affects installation benefits. Separating and comparing self-consumption and surplus electricity clarifies the practical meaning of generation declines.

When combining batteries, generation declines also affect battery charging. If daytime surplus decreases, the power stored in batteries decreases, reducing the amount available for discharge in the evening or night. In simulations with batteries, check charging, discharging, and state-of-charge trajectories after generation declines.

Predicting generation declines is insufficient if you only look at how much generation decreases. By checking self-consumption, surplus electricity, and battery use, you can correctly judge the impact on installation benefits.

Adjusting predictions to reflect pre- and post-construction condition differences

To accurately predict generation declines, it is important to adjust simulations to reflect differences between pre- and post-construction conditions. Initial proposals are sometimes based on drawings and rough information. However, as site surveys and detailed design proceed, the installable area, number of panels, orientation, tilt, shading, wiring, and equipment layout may change.

In rooftop projects, site surveys reveal rooftop equipment, inspection paths, waterproofing clearance, drains, guardrails, and roof structure positions. As a result, the installable area may be smaller than in initial simulations. If the number of panels decreases, annual generation changes accordingly. Avoiding shaded areas may reduce total capacity but can improve generation per capacity and long-term operability.

In ground projects, site surveys clarify site boundaries, elevation differences, trees, slopes, drainage, maintenance access, and connection candidate points. Areas treated as flat in early stages may actually have slopes or steps that require rethinking layout and row spacing. If these are not reflected, generation decline predictions will diverge from reality.

In the final pre-construction layout, compare initial simulation assumptions with final conditions. Check whether installed capacity changed, shading assessments changed, generation losses changed, and how self-consumption and surplus electricity changed. If generation decreases, documenting the reasons reduces gaps with post-installation performance.

Also, keep the final simulation baseline so it can be compared with post-installation performance. Record not only annual generation but monthly generation, time-of-day generation, generation by mounting surface, self-consumption, surplus electricity, and loss-rate assumptions so that causes of post-installation declines are easy to verify.

Adjusting predictions to reflect pre- and post-construction condition differences is not about conservatively lowering generation. It is about matching reality so you can grasp post-installation generation. Do not cling to initial proposal figures; re-simulate using final conditions after site survey or pre-construction.

Keeping benchmarks usable for post-installation performance management

Predicting generation declines is not only for pre-installation decisions. It is also important to retain the simulation as a benchmark for checking post-installation performance and identifying causes of declines. Solar generation fluctuates with weather, so actual performance will not exactly match simulations. Without a baseline, it is hard to tell whether generation is normal or declining.

For post-installation management, retain monthly generation assumptions in addition to annual generation. With monthly expected values, you can check whether any particular month shows a large drop. If winter is lower than expected, consider shading or snow; if summer is lower, consider temperature or soiling; if the rainy season is lower, consider weather impacts.

If time-of-day generation is available, it is easier to narrow down causes. Low generation only in the morning indicates eastern shading; early evening declines indicate western shading; unusual midday dips may suggest rooftop equipment or equipment issues. If generation by mounting surface is available, you can check whether specific surfaces are affected by soiling, shading, wiring, or equipment anomalies.

Benchmarks for self-consumption and surplus electricity are also important. Even if generation matches expectations, changes in facility operation can alter self-consumption. Conversely, a slight generation decline may not significantly affect self-consumption. To judge post-installation benefits, check both generated and consumed electricity and leftover surplus.

Also retain simulation assumptions. If capacity, mounting surfaces, orientation, tilt, shading evaluation, loss rates, electricity usage data, battery presence, and emergency use policies are lost, it becomes hard to analyze differences with actual performance. With assumptions retained, you can determine whether deviations are due to weather, site condition changes, or equipment condition.

In post-installation management, avoid judging based on short-term differences alone. Generation varies with weather and season. Important is whether a difference persists, whether it is biased to certain months or times of day, or whether it occurs on specific mounting surfaces.

A simulation used to predict generation declines becomes more valuable when used not only as pre-installation material but also as a benchmark for post-installation management. Keeping baseline values helps detect declines early, identify causes, and implement necessary maintenance or improvements.

Summary

To predict generation declines in solar power generation simulations, you need to check comprehensively not only annual generation but also monthly generation, shading, temperature, soiling, snow, equipment degradation, equipment outages, self-consumption, surplus electricity, and differences between pre- and post-construction conditions. Anticipating causes of generation declines in advance reduces gaps with post-installation performance and enables more realistic installation decisions.

First, classify the causes of generation declines. Separate declines due to natural conditions, equipment conditions, short-term declines, long-term declines, and impacts on self-consumption. By checking monthly generation, you can grasp winter shading and snow, summer temperature losses, and seasonal declines due to rainy or cloudy periods.

Shading and surrounding obstacles are representative causes of generation declines. Rooftop equipment, surrounding buildings, trees, utility poles, and terrain elevation differences affect generation by season and time of day. By confirming generation differences with and without shading, monthly generation, and time-of-day generation curves, you can anticipate shading impacts in advance.

Temperature, soiling, and snow-related declines are also important. High temperatures can reduce panel output, and soiling or snow can block irradiance. Because the magnitude of these impacts varies by region and surrounding environment, reflect local conditions rather than relying only on generic loss rates.

Do not overlook long-term declines from equipment degradation and outages. Consider multi-year generation trends, maintainability, outage risks, and changes in the surrounding environment. Also verify how generation declines affect self-consumption and surplus electricity, since the impact on installation benefits differs between cases where only surplus is reduced and cases where self-consumption also falls.

Adjust predictions to reflect pre- and post-construction condition differences. Site surveys and detailed design can change installable area, orientation, tilt, shading, wiring, and equipment layout. Re-simulate based on the final layout and keep benchmarks usable for post-installation management so you can more easily identify causes of generation declines.

Finally, the foundation for accurately predicting generation declines is accurate site information. If you can precisely grasp the candidate installation area, rooftop equipment, obstacles, trees, site boundaries, orientation, tilt, inspection access, and connection candidate points, the assumptions for solar power generation simulations become clear and prediction accuracy improves.

If you want to accurately record installation candidate areas, rooftop equipment, obstacles, trees, site boundaries, orientation, tilt, inspection access, and so on on-site and improve the precision of generation-decline predictions in solar power generation simulations, using LRTK, an iPhone-mounted high-precision GNSS positioning device, is effective. By acquiring high-precision location information on-site, you can more easily organize shading and obstacles, installable areas, wiring routes, and maintenance access, facilitating consistent work from pre-construction checks through post-installation maintenance. To correctly predict generation declines in solar power generation simulations, establish a system to accurately understand the site, not just desk-based generation figures.