How to Choose Meteorological Data for Power Generation Calculations: 5 Points to Note

When calculating solar power generation, it’s easy to focus on installed capacity, number of panels, tilt angle, azimuth, loss rates, and so on, but the foundation of the calculation is meteorological data. No matter how much you refine the calculation formulas, if input conditions such as solar irradiance and temperature deviate from reality, the accuracy of the calculated annual generation, monthly generation, and expected generation will also change. In particular, for business planning, design studies, evaluation of existing systems’ generation, and investigating causes of generation decline, the choice of meteorological data can affect the conclusions.

This article explains, in five points, how to choose and what to watch for in meteorological data used for power generation calculations, aimed at practitioners researching "solar power generation calculation". By checking not only irradiance data but also site-to-site differences, time resolution, representative year, temperature, missing data, and the relationship with the surrounding environment, you can make calculation results easier to use for on-site decision making.

Table of Contents

• Meteorological data are organized as prerequisites for power generation calculations.

• Confirm the type of solar radiation data and its conversion to the installation surface.

• Do not judge based only on nearby observations; consider differences between locations and topographic conditions.

• Understand the difference between time resolution and representative years, and choose data that fits your purpose.

• Also check meteorological factors other than solar radiation, such as air temperature and snowfall.

• Record how missing data and adjustments are handled to improve the explainability of calculation results.

• Summary

Organize meteorological data as prerequisites for power generation calculations

When calculating solar power generation, the first thing to confirm is that meteorological data should be treated not simply as input values but as assumptions for the entire calculation. Generated output is affected by many factors: the solar irradiance reaching the panels, panel temperature, conversion losses, losses in power conditioners and wiring, shading, soiling, snow cover, downtime, and so on. Among these, meteorological data are fundamental information representing natural conditions and are as important to verify as equipment conditions.

Common meteorological data frequently used in power generation calculations include solar irradiance, air temperature, wind speed, snowfall, precipitation, humidity, and so on. The most central factor is solar irradiance, but in practice looking at solar irradiance alone is not always sufficient. For example, with the same solar irradiance, in regions with high ambient temperatures or during summer the panel temperature tends to rise, which acts to reduce generation efficiency. In snowy regions, even if there is winter solar irradiance, actual generation is suppressed if the panel surface is covered with snow. Along coasts and in mountainous areas, fog, clouds, wind, and shadows caused by terrain are also factors to consider.

When selecting meteorological data, it is important to first clarify the purpose of the calculations. Whether it is a preliminary estimate for a new installation plan, a revenue-and-expenditure plan used for investment decisions, investigating why an existing facility’s power generation is low, or setting priorities for maintenance and inspection will affect the required accuracy and the period that needs to be checked. For rough estimates, monthly average solar irradiance may be sufficient, but when investigating the causes of reduced power generation, daily or hourly data may be necessary. When judging equipment abnormalities, monthly averages alone may miss the effects of temporary shading, shutdowns, rain, snowfall, or output curtailment.

Meteorological data can be classified into two types: those that look at long-term trends, like climatological normals, and those that look at the actual performance of a specific year. When preparing an estimate of annual power generation, it is effective to use data close to the long-term average. On the other hand, when judging that an existing installation’s generation is low this year, you must compare it with that year’s actual weather. If a year has less solar irradiation than normal, a decrease in generation does not necessarily indicate a system fault. Conversely, if solar conditions are good but generation is low, there is reason to suspect shading, soiling, equipment malfunction, settings, outages, or measurement errors.

In practice, when selecting meteorological data, accuracy is more stable if you also organize the equipment information for the system being analyzed. This includes location, panel azimuth, tilt angle, installed capacity, panel type, power conditioner (inverter) capacity, whether oversizing is present, directions prone to shading, surrounding buildings and trees, presence of snow, and maintenance history, etc. Even if you make the meteorological data precise, if the equipment conditions are ambiguous the calculated results will be hard to match with actual on-site generation.

When handling meteorological data, it is also important to record which location, which period, which time unit, and which type of data were used. Later, when explaining estimated power generation or evaluating actual performance, if the input conditions are not retained, the validity of the calculation results cannot be verified. This is especially true when the information is used in internal documents, customer presentations, before-and-after construction comparisons, or maintenance proposals: not only the calculated values themselves but the transparency of the assumptions contributes to credibility.

Confirm the types of solar irradiance data and their conversion to the installation surface

Among the meteorological data used for power generation calculations, the most important is solar irradiance. However, even when referring to solar irradiance there are several concepts, such as horizontal-plane irradiance, direct irradiance, diffuse irradiance, and irradiance on inclined surfaces. Since photovoltaic panels are typically installed on roofs or mounting structures at a fixed tilt angle and azimuth, simply using horizontal-plane irradiance as-is can differ from the irradiance that actually reaches the panel surface.

Horizontal surface solar irradiance is a concept that represents the solar radiation reaching a surface parallel to the ground. It is readily available as meteorological data and convenient for regional comparisons and rough estimates, but it does not directly reflect the irradiance conditions for solar panels installed on south-facing slopes or east–west-facing roofs. When calculating photovoltaic power generation, it is necessary to consider the irradiance incident on the installation surface according to the panel's azimuth and tilt angle.

When considering the irradiance on the installation surface, the difference between direct and diffuse radiation is also important. Direct radiation is the light that comes directly from the sun and greatly affects power generation on clear days. Diffuse radiation is light scattered in the atmosphere by clouds, water vapor, and the like, and contributes to power generation even on cloudy days. Because solar panels generate electricity from not only direct light but also diffuse light, it is desirable that generation calculations reflect the effects of both.

When examining different tilt angles or when the roof surface is not south-facing, comparing only the solar irradiance on a horizontal surface can lead to incorrect conclusions. For example, a south-facing surface with a moderate tilt tends to receive solar radiation throughout the year, whereas east- or west-facing surfaces have higher shares of generation in the morning and evening. On north-facing surfaces, generation can drop substantially depending on the region and the tilt angle. These differences are difficult to see unless the solar irradiance on the actual installation surface is taken into account.

Also, in power generation calculations, the results you see change depending on whether you use monthly, daily, or hourly solar radiation. Using monthly averages makes it easier to estimate annual totals, but it makes it difficult to represent shaded periods, generation in the morning and evening, daytime peaks, output clipping, and weather variability. If you want to examine in detail the effects of oversizing and the relationship with power conditioner capacity, using hourly solar radiation data allows a more realistic assessment.

However, using more detailed data does not necessarily guarantee greater accuracy. Even when time-resolved data are used, discrepancies with actual performance will occur if local conditions at the installation site—such as localized clouds, mountain shading, building shadows, snow cover, soiling, or equipment outages—are not reflected. The important thing is to match the granularity of the data to the purpose of the calculation. At the estimation stage, it is more effective to clearly organize the assumptions than to rely on excessively detailed data. On the other hand, for performance evaluation and root-cause analysis, data that allow examination of fine temporal variations are useful.

When selecting solar radiation data, you also need to pay attention to the units. Solar radiation can be expressed as the amount of energy received over a certain period, or as instantaneous or average solar irradiance (intensity). In power generation calculations, because the data are combined with system capacity and time, misunderstanding the meaning of the units can lead to large errors. It is important to check that you do not confuse monthly solar radiation, daily cumulative solar radiation, and hourly average irradiance.

Do not make judgments based only on nearby observations; consider site differences and terrain conditions

A commonly overlooked issue when choosing meteorological data is the difference between the observation site and the actual installation location. There is not always a meteorological observation point that exactly matches the location of a solar power plant or rooftop installation. For that reason, calculations often use nearby observation sites or regional data. However, even if the distance is short, solar irradiance and temperature conditions are not necessarily the same.

Especially in mountainous areas, coastal areas, basins, urban areas, along rivers, and regions with differences in elevation, weather conditions can change even over distances of a few kilometers (a few miles) to several tens of kilometers (several tens of miles). On mountain slopes and in valley terrain, morning and evening shadows, fog and cloud formation, and wind patterns may differ from the surrounding areas. Along coasts, sea breezes and the movement of clouds have an influence, and temperature and humidity can differ from inland areas. In urban areas, shadows and reflections from surrounding buildings, waste heat, and locally weak winds can have an impact.

When selecting nearby observation sites for power generation calculations, it's important not to look only at straight-line distance but to verify whether the site has similar terrain and weather. Check elevation, distance from the sea, the presence of mountains, the shape of valleys and basins, and the degree of urbanization in the surrounding area, and choose data that match the installation site's conditions. If there are multiple candidate sites, compare not only the closest site but also sites that are topographically similar and those with stable long-term trends.

When determining whether an existing installation's power generation is low, it is useful not only to check local meteorological data but also to examine trends at multiple points within the same area and at surrounding installations. If the month experienced reduced solar irradiance over a wide area, a drop in generation is likely due to weather factors. Conversely, if surrounding installations are producing at typical levels for the year while only the installation in question is underperforming, you should suspect an equipment-related issue.

However, even when comparing neighboring installations, you must not ignore differences in installation conditions. Orientation, tilt angle, system capacity, overloading ratio, presence or absence of shading, maintenance/inspection status, panel soiling, and measurement methods—if any of these differ, power generation will vary even in the same area. Meteorological data are merely baseline information for confirming natural conditions and need to be evaluated in combination with equipment conditions.

Also, if the representative meteorological site for the weather data is located in a more open area than the installation site, the solar irradiation conditions may appear better than at the actual site. While the observation point may have less surrounding shading, the actual roof or site may have buildings, trees, utility poles, mountain shadows, adjacent structures, and so on. In such cases, actual generation may be lower than the expected generation calculated from the weather data. Rather than immediately concluding that the difference between calculated and actual values is due to equipment failure, it is important to confirm site-specific shading and obstruction conditions.

When considering terrain conditions, we look not only at annual trends but also at seasonal differences. In winter, because the sun's elevation is lower, shadows from obstacles and mountains on the south side tend to be longer. In summer, even with high solar irradiance, output reductions due to high temperatures are more likely, and during the rainy season and typhoon periods the site is more affected by clouds and rain. Even at the same location, the relationship between meteorological data and actual power generation changes with the seasons, so checking on a monthly basis is effective.

Understand the differences between time resolution and representative years and choose data that fits your purpose

Meteorological data come in various time resolutions, such as annual, monthly, daily, and hourly. In power generation calculations, the choice of time resolution affects both the ease of calculation and how the results appear.

If you only want a rough estimate of annual generation, calculations using monthly average solar irradiance can give a general sense. However, if you want to examine generation patterns and the relationship with system capacity in detail, finer time resolution is necessary.

The advantage of monthly data is that it is easy to handle and makes it straightforward to estimate annual power generation. For internal preliminary studies, rough proposals, regional comparisons, and rough evaluations by roof surface, monthly data may be sufficient. On the other hand, monthly data has the weakness of being poor at representing daily weather fluctuations and time-of-day output changes, making it difficult to evaluate shading or output curtailment during specific time periods or capacity constraints of power conditioners.

Using daily data makes the effects of sunny days, cloudy days, rainy days, and snowy days more apparent. In evaluating the power generation of existing installations, it helps distinguish whether days with low generation are due to the weather or to equipment shutdowns or abnormalities. However, even with daily data, time-of-day phenomena such as morning shadows, evening shadows, and daytime peak curtailment can be difficult to capture.

Using hourly data makes it easier to examine how power generation changes with the movement of the sun. East-facing roofs tend to generate more in the morning, while west-facing roofs tend to generate more in the afternoon. South-facing systems tend to have higher output around midday, and effects from over-sizing or power conditioner capacity may appear. If you want to see these time-of-day variations, hourly solar irradiance and temperature data are effective.

The concept of a representative year is also important. In power generation calculations, you may use meteorological data from a specific year or standardized data that approximates the long-term average. Using data from a specific year allows calculations that reflect the actual weather of that year, but if that year was unusually sunny or unusually rainy, it can bias long-term generation estimates. Using data close to the long-term average makes it easier to produce long-term projections, but it may differ from the actual performance in a specific year.

In business planning and the design phase, it is generally more practical to use data that reflect long-term trends. Since weather varies from year to year, placing too much weight on a single year of unusually good or bad weather can lead to overestimating or underestimating expected power generation. Basing evaluations on long-term average trends and, where appropriate, adding conservative assumptions or sensitivity analyses will produce materials that are easier to use for decision-making.

On the other hand, when evaluating the power generation of an existing facility, you need to use data that closely reflect the actual weather during the period in question. For example, even if generation in a given month is lower than the previous year, if the solar irradiance that month was also lower than the previous year, you cannot conclusively attribute the drop to an equipment fault. Conversely, if irradiance is similar or higher yet generation has decreased, you need to investigate equipment-related causes in detail.

When choosing the time resolution and representative year, it is important to decide in advance how the calculation results will be used. Using overly detailed data to explain a rough estimate can make the materials unnecessarily complex. Conversely, relying only on coarse monthly data for root-cause analysis can cause you to miss important anomalies. For power generation calculations, it is more practical to choose a level of granularity that is necessary and sufficient for the purpose, rather than pursuing precision for its own sake.

Check meteorological factors other than solar radiation, such as air temperature and snowfall

In calculating solar power generation, solar irradiance is the central factor, but power output is not determined by irradiance alone. Meteorological factors often overlooked in practice include temperature, wind speed, snowfall, precipitation, humidity, and cloud cover. These factors directly or indirectly affect power output.

First, the important factor is ambient temperature. Solar panels generally tend to produce less power as panel temperature rises. In summer, when ambient air temperatures are high, or on poorly ventilated rooftops, you may not see as much power generation as expected even if solar irradiance is high. When comparing generation by region, it is important to reflect temperature-related losses in the calculation of expected generation for locations that are prone to high-temperature effects, even when irradiance conditions are good.

Panel temperature is not determined by ambient air temperature alone. It is also affected by solar irradiance, wind speed, mounting method, roofing material, racking height, rear-side ventilation, and the surrounding thermal environment. If panels are installed close to the roof and rear-side ventilation is poor, panel temperatures can rise more easily. Conversely, for ground-mounted installations with good airflow, panel temperature increases may be suppressed even at the same ambient temperature. Therefore, ambient temperature data should not be viewed in isolation; temperature losses need to be considered together with installation conditions.

Snow cover is also an important factor. In snowy regions, even if there is solar radiation in winter, if snow remains on the panel surface the system may not generate electricity, or generation may be greatly reduced. Calculations that use only the solar radiation from meteorological data can therefore overestimate winter power generation. Results also vary depending on roof pitch, panel tilt, how easily snow slides off, ambient temperature, ease of snowmelt, and whether snow is cleared.

The effects of precipitation and clouds should not be overlooked. If rainy or overcast conditions persist, solar irradiance falls and power generation decreases. On the other hand, rain can partially wash away dirt from the panel surface. However, rain alone does not always remove all soiling; bird droppings, dust, pollen, fallen leaves, and dust from nearby construction may remain. When investigating the cause of reduced power generation, it is necessary to distinguish whether it is low because there was a lot of rain or because soiling or shading remains.

Wind speed affects panel temperature. When there is wind, panels are more easily cooled, which can help suppress declines in power generation efficiency. Conversely, in poorly ventilated locations, temperatures tend to rise more easily, which can make it harder for power output to increase during the summer. However, wind speed data can differ between the observation point and the actual installation site. Because wind flow is altered by roof height, surrounding buildings, terrain, trees, and other factors, it is necessary to be cautious about treating observed values as if they directly represent on-site conditions.

Also, meteorological factors affect power generation not individually but in combination. On clear days with high solar irradiance, power generation tends to increase, but if the ambient temperature is also high, temperature-related losses become larger. In winter, although lower temperatures are advantageous from a temperature standpoint, power generation falls when the solar elevation is low and daylight hours are short, or when there is snow cover. During the rainy season, solar irradiance is low and humidity and clouds also have an impact. By interpreting calculation results with consideration of seasonal characteristics, you can identify risks that are not apparent from simple annual values alone.

In power generation calculations, you decide how much meteorological data other than solar irradiance to reflect based on the purpose. For rough estimates, these may be summarized as a standard loss rate, but for performance evaluation or accuracy-sensitive calculations, it can be better to consider temperature and snowfall individually. Especially when explaining why generation is low, it is important not to judge by solar irradiance alone but to check a combination of temperature, snow, rain, clouds, wind, soiling, shading, and downtime history.

Record how missing data and corrections are handled to improve the interpretability of calculation results

When handling meteorological data in practice, what's surprisingly important is how you deal with missing data and corrections. Meteorological data are not always complete. Due to instrument malfunctions, communication failures, data processing constraints, insufficient observation periods, and so on, data may be missing for some times or dates. In power generation calculations, the results depend on how those missing data are handled.

Even when missing data are few, the impact varies depending on the evaluation period and the purpose of assessing power generation. For example, if a few hours are missing in an annual calculation, it may not have a large effect. However, if data are missing during the daytime of a specific day being investigated for a drop in generation, it can greatly affect the assessment. In particular, missing data during peak solar irradiance hours requires caution because it is likely to affect daily totals and monthly values.

Methods for filling missing observations include interpolating from preceding and following data, referring to data from nearby sites, using the average value for the same period, or excluding the missing period from the assessment. However, each method has its limitations. On days when clear and cloudy conditions change over short periods, simply interpolating from surrounding values may not reflect the actual situation. Even when using data from nearby sites, localized clouds or differences in terrain may not be captured.

Therefore, when using adjusted values, it is important to record the adjustment method and scope. When you review calculation results later, if you cannot tell which parts are based on actual measurements and which are adjusted values, the reliability of your judgments will decrease. In particular, in materials used for customer explanations or internal approvals, it is safer to explicitly state missing data and adjustments as assumptions rather than hide them.

Also, meteorological data are sometimes already processed. They may have undergone long-term averaging, conversion to a representative year, estimation, spatial interpolation, outlier processing, and so on. While processed data are easier to handle and convenient for calculations, they may not be the original observations themselves. In practice, it is important to check whether the data are observational values, estimated values, averages, or corrected values, and use them accordingly.

Checking for anomalous values is also essential. If there are values such as solar radiation showing large readings at night, remaining near zero for long periods during the daytime, temperatures that deviate greatly from surrounding trends, or monthly totals that are extremely high or low, they need to be checked before input. If anomalous values are included in calculations as-is, estimates of power generation and comparative results will become unrealistic.

To make calculation results easier to explain, it is also important to align the periods of meteorological data and power generation data. If generation results are aggregated monthly while the meteorological data are daily and partially missing, it becomes ambiguous which range is being compared. You should clarify whether the data cover from the beginning to the end of the month, from the facility’s start of operation onward, whether shutdown periods are included, and whether output curtailment or maintenance outages are included.

Furthermore, calculation conditions are not decided once and for all. The items you want to check for the same equipment change during the design phase, after construction, after the start of operations, and after inspections. In the design phase you calculate the expected power generation, after the start of operations you look at the difference from actual results, and after inspections you compare the effects of improvements. Because meteorological conditions differ each time, it is important to record the data used, the period, the correction methods, and the loss conditions so that comparisons can be made using the same standards.

The persuasiveness of power generation calculations is not determined solely by the complexity of the formulas. Rather, clarity about the source, period, units, corrections, and exclusion criteria of the input data increases practical reliability. A major purpose of selecting meteorological data is to ensure that anyone reviewing the calculation results can understand “why the generation amount is what it is” and “under what conditions it would change.”

Summary

When selecting meteorological data for power generation calculations, it is important not simply to obtain solar irradiance and plug it into the formula, but to organize considerations including the calculation purpose, site conditions, time resolution, representative year, temperature and snowfall, and the treatment of missing data and corrections. Because photovoltaic generation is influenced by natural conditions, the choice of meteorological data greatly affects the accuracy and explainability of the calculation results.

First, treat meteorological data as prerequisites for power generation calculations, and clarify which systems, over what period, and for what purpose will be evaluated. Next, verify the types of solar irradiance data and understand the difference between horizontal-plane irradiance and plane-of-array irradiance, the effects of direct and diffuse irradiance, and the meaning of the units. Furthermore, consider not only the distance between the observation point and the site but also elevation, terrain, distance from the sea, and surrounding shading.

For time resolution, monthly data is suitable for rough estimates, daily data for performance assessment, and hourly data is useful if you want to examine generation characteristics by time of day or the effects of overloading. For long-term projections, data that reflect average trends are appropriate, but to investigate a decline in generation during a specific period, you need data that closely match the actual weather for that period.

Also, the effects of ambient temperature, wind speed, snowfall, precipitation, clouds, and soiling cannot be ignored. Even when solar irradiance is high, high temperatures can reduce power generation efficiency, and in winter there can be days when snow prevents power generation. When power output is low, it is important not to draw conclusions based solely on solar irradiance, but to verify the situation from multiple angles by considering equipment conditions and on-site circumstances.

Finally, recording missing data, corrections, outliers, periods of use, and the handling of units makes it easier to explain calculation results later. Power generation calculations are not simply tasks to produce numbers but efforts to prepare the evidence needed for on-site decisions. By carefully selecting meteorological data, you can improve the accuracy of design, proposals, inspections, and decisions on improvements.

To make solar power generation calculations more reflective of on-site conditions, it is essential to understand the actual installation surface, shading, surrounding environment, and equipment condition in addition to meteorological data. By verifying site conditions that desk-based meteorological data cannot reveal and organizing expected generation and factors causing reductions, you can more easily apply the calculation results to design, proposals, inspections, and improvement decisions.