6 Methods to Calculate Solar Power Generation by Region | Explaining Why Differences Occur

Table of Contents

• Understand the significance of calculating by region at the outset

• Method 1 Calculate using regional coefficients for annual generation per 1 kW

• Method 2 Aggregate using monthly solar irradiation conditions

• Method 3 Adjust orientation and tilt angle including regional characteristics

• Method 4 Anticipate regional differences in snowfall, rainy days, and cloudy days

• Method 5 Reflect output reductions due to temperature conditions

• Method 6 Adjust based on nearby performance records and projects in the same region

• How to consider the reasons why differences arise by region

• Points easily overlooked in region-specific calculations

• A procedure for practitioners to follow without confusion

• Summary

First, grasp the significance of calculating by region

When calculating solar power generation, looking only at system capacity is not sufficient. Even with the same 10kW system, the annual generation will vary depending on the region where it is installed. This is not simply a matter of whether there are many or few sunny days, but rather because multiple factors overlap, such as solar irradiance, latitude, temperature, snowfall, and seasonal weather patterns. In other words, if you want solar generation figures that are usable in practice, you must account for regional differences in your calculations.

When a practitioner searches for "solar power generation calculation", what they want to know is not tidy textbook theoretical values but realistic figures that can be used for decisions on adoption, comparisons of system size, internal explanations, and organizing forecasts of self-consumption. If you apply the same coefficient uniformly nationwide, it can produce values that are too high in some regions and too low in others. That may be acceptable for initial studies, but at the proposal and decision-making stage, incorporating regional differences at least to some extent increases the reliability of the numbers.

Considering regional differences does not necessarily mean performing complex meteorological analyses. At first, simply adjusting the baseline annual generation per kW slightly for each region can have a significant impact. If needed, you can then add monthly solar irradiation conditions, temperatures, and the effects of snowfall in sequence, which will incrementally improve accuracy. The important thing is not to try to produce a perfect calculation from the start, but to clarify at which stage which regional differences should be introduced.

In this article, we break down six representative methods for calculating solar power generation by region. We also summarize why regional differences occur, how to interpret those differences, and what is easily overlooked in practice. Rather than narrowly interpreting "by region" as merely differences between prefectures, understanding it as differences in power generation that include the area's meteorological and geographical conditions will significantly improve calculation accuracy.

Method 1: Calculate using regional coefficients for annual generation per 1 kW

The simplest and easiest method to use is to calculate using a regional coefficient for annual generation per 1 kW. The idea is: Annual generation (kWh) = System capacity (kW) × Standard generation per region (kWh/kW·year). By varying this standard generation by region, the figure becomes one step closer to reality than a uniform nationwide estimate.

For example, in one area you might use roughly 1,100 kWh per kW per year as a guideline, in another area about 1,000 kWh, and in areas with harsher conditions about 950 kWh. For a 10 kW system, the former would be 11,000 kWh per year, the latter 10,000 kWh, and under stricter conditions 9,500 kWh. This shows that even with the same 10 kW, just the initial figure can produce this much of a difference.

The advantage of this method is that it requires only a small number of input values and is very easy to use for initial assessments. As long as the equipment capacity is determined, you can compare multiple candidate sites in a short time simply by changing the regional coefficients. It is extremely effective when you want to share a rough sense of scale within the company or grasp the differences between candidate regions at the initial stage.

However, this method is only an initial approximation. Even within the same region, conditions can differ between coastal and inland areas, highlands and lowlands, and urban and mountainous areas, and it does not adequately reflect installation orientation or shading conditions. Therefore, rather than making a final decision based solely on regional coefficients, it is appropriate to use this as the first step to grasp regional differences.

For practitioners, simply having this method makes comparing system capacities considerably easier. For a 5 kW system, annual yields divide by region into roughly 5,500 kWh, 5,000 kWh, and 4,750 kWh, and for a 20 kW system those differences become even more apparent. In other words, the regional coefficient is the simplest way to visualize regional differences in solar power generation.

Method 2: Stack by monthly solar radiation conditions

If you want to raise the accuracy of region-specific calculations, an accumulation method using monthly insolation conditions is effective. Because it can reflect seasonal differences that a single annual coefficient alone cannot show, the characteristics of a region are represented more naturally. The idea is: Monthly generation (kWh) = system capacity (kW) × that month’s average equivalent generation hours (h) × number of days in the month × correction coefficient. Calculate this for each month and then sum them to obtain the annual generation.

For example, even for the same 10 kW system, if the equivalent generation hours in spring are 4.0 hours, for 30 days, and the correction factor is 0.82, then 10×4.0×30×0.82 = 984 kWh. In winter, if the equivalent generation hours are 2.5 hours, for 31 days, and the correction factor is 0.80, then 10×2.5×31×0.80 = 620 kWh. This shows that even with the same system there can be a considerable difference from month to month.

From a regional perspective, this monthly calculation is particularly important. This is because regional differences are reflected not only in the annual total but also in which seasons those differences appear. In some regions spring and autumn are strong, in others output tends to be higher in summer, or the winter decline is substantial. A single annual coefficient does not show this, but calculating on a monthly basis makes the generation characteristics of each region much clearer.

This method is also useful when considering self-consumption. For example, in regions with high cooling demand, how generation overlaps with summer demand is important, and in facilities with high winter usage you will want to check how much generation drops in winter. Even if the annual total is sufficient, if there is a shortage in the months when it is needed, the assessment of system size and operation may change.

Although more labor-intensive than Method 1, stacking monthly solar radiation conditions allows you to reflect regional differences quite practically. It is especially effective when you want to emphasize seasonal patterns rather than annual totals.

Method 3 Adjust azimuth and installation angle to account for regional characteristics

The third method is to correct the orientation and installation angle by taking regional characteristics into account. Solar power generation varies not only by region but also by how effective particular orientations and tilt angles are in that region. Even when facing due south, the apparent solar altitude and the seasonal pattern of sunlight vary by region, so the effectiveness of orientation and angle is not uniform.

The basic idea is to multiply the value calculated from system capacity and the regional reference generation by an azimuth and tilt correction factor. If the orientation and angle are close to ideal, the factor will be near 1.0; if slightly unfavorable, it is lowered a bit; if more unfavorable, it is reduced further. For example, even a system with an annual reference value of 11,000 kWh would yield 10,450 kWh with an azimuth and tilt correction of 0.95, and 9,900 kWh with 0.90.

The important point here is not to consider orientation and angle uniformly across the country. Sun altitude differs by region and season, so even the same roof pitch or the same orientation can give a different impression of the solar radiation received. In practice, it is more appropriate to consider winter shadow patterns and the shallowness of solar incidence angles together with latitude and regional weather tendencies.

Also, for projects with multiple installation surfaces, separating differences by surface in addition to by region improves accuracy. For a configuration such as 6 kW on the south face, 2 kW on the east face, and 2 kW on the west face, applying adjustments for regional conditions to each surface and then summing them at the end will be closer to reality than evaluating the whole at once. This makes it easier to explain how differences in roof shape affect energy generation even within the same region.

This method is meaningful in that it does not simply end with the generalization that "south-facing is advantageous," but considers which orientations and angles are effective in a given region and to what extent. As an approach to calculating regionally, it serves to connect geographical conditions with installation conditions.

Method 4 Anticipate snowfall, rain, and cloudy weather by region

The fourth method is to estimate regional weather trends such as snowfall, rainy periods, and cloudy conditions. The reason solar power generation varies by region is not simply the annual total solar irradiation. In some areas the influence of the rainy season and cloudy weather is significant, while in other areas winter snowfall has an effect. Calculating regionally without taking these weather trends into account can cause the figures to be too high or too low.

As a concept, you multiply the monthly or annual baseline values by a weather-condition correction factor. For example, in regions where snowfall or prolonged rain have a significant impact, you apply a slightly reduced factor, whereas in regions with relatively stable, sunny days you do not lower the factor much. You can apply a single annual correction, but in practice monthly adjustments — such as for winter only or for the rainy season — make it easier to explain seasonal differences.

The advantage of this method is that it makes it easy to reflect the causes of regional differences numerically. For example, even in nearby areas with the same solar irradiance, one may see a large drop in power generation due to winter snowfall while the other does not. Or, even if there appears to be little difference on an annual basis, the degree of decline during the rainy season may differ. Such differences become easier to represent only when meteorological trends are taken into account.

Also, this method is useful when explaining why differences appear between regions. Rather than simply saying “this region has a lower coefficient,” it lets you show the background—such as “winter snowfall is expected,” “months with rain or cloud cover tend to cluster,” or “there is a large seasonal variation”—which makes internal explanations more persuasive. The main benefit is that it becomes easier to convey the reasons behind the numbers than the numbers themselves.

Of course, examining weather trends in too much detail increases workload. Therefore, in practical work it is better to use a staged approach: be rough in the initial assessment and delve into month-by-month detail at the decision-making stage. This is an effective method when you want to bring regional differences one step closer to reality.

Method 5 Reflecting Output Reduction Due to Ambient Temperature Conditions

The fifth method is to account for power output reductions caused by temperature conditions. Solar power generation is not always advantageous simply because solar irradiance is strong. Since output tends to decrease as temperature rises, in some regions it may be better to anticipate losses from high temperatures even when irradiance conditions are favorable. Ignoring this point can lead to overestimating generation, especially during hot periods.

As a concept, it is easiest to treat output loss due to temperature as a temperature correction coefficient. Apply a slightly lower coefficient during the hot summer period and use a higher one for seasons when temperatures are relatively stable. If processing on an annual, lump-sum basis, you can slightly adjust the overall coefficient to account for the region’s tendency toward higher temperatures, but if you look month by month, strengthening the correction only for the summer months will be closer to reality.

This method is important to avoid misreading regional differences. For example, a region that looks very favorable based solely on solar irradiance may see actual power output slightly suppressed by high summer temperatures. Conversely, in regions where extreme high-temperature conditions are uncommon, solar irradiance is more likely to be directly reflected in power generation. In other words, regional differences need to be considered not only in terms of solar intensity but together with temperature conditions.

Also, in practice, because attention is often paid to power generation during summer when air-conditioning demand is high, this temperature correction is important for explanatory purposes. If you simply assume that generation will be highest in summer because solar radiation is strong, it’s easy to overlook high-temperature losses. Setting figures that are not overly optimistic for summer will actually contribute to more stable explanations later.

Temperature conditions may at first glance appear to be minor adjustments, but they are difficult to ignore when interpreting regional differences for practical use. In particular, for projects that prioritize summer power generation or in regions with severe heat conditions, simply including this value can greatly change the perceived credibility of the forecast.

Method 6 Adjust based on nearby actuals and projects in the same area

The sixth method is to adjust based on nearby performance records or the track record of projects in the same area. This is highly effective for reflecting region-specific conditions that cannot be fully captured by desk-based coefficients or theoretical values alone. In particular, if there are existing facilities in the same region or adjacent area, their results provide a strong point of reference.

The approach is to first perform theoretical, region-specific calculations and then compare those results with nearby actual performance to determine a correction factor. For example, suppose that in the same region the theoretical annual generation for a 10 kW installation was estimated at 10,500 kWh, while the actual result was 9,450 kWh. In this case, the site-specific correction factor is 0.9. Applying this 0.9 as a reference to new projects brings the forecasts closer to predictions that incorporate, to some extent, the region’s specific weather tendencies and operational conditions.

A major advantage of this method is that it can absorb differences that are not apparent on paper. For example, even within the same prefecture, coastal and inland areas can experience different weather conditions, and there are conditions that theoretical values alone find hard to capture—such as the high-heat environments typical of urban areas or the tendency toward cloudy skies in mountainous regions. If there are nearby actual results, those factors will be reflected in the outcomes, making it easier to produce figures that are more practical for real-world use.

Also, using nearby results makes it easier to explain regional differences. Rather than simply saying "we are estimating this area on the low side," it is more convincing to explain, "because nearby results tended to be this much lower than the theoretical value, we are adjusting for that difference." In practice, those who can quantify their experience as numbers, rather than relying solely on theory, are stronger.

Of course, not all projects have nearby precedents. However, for practitioners who have accumulated past projects, this method is very effective. As a final refinement of regional calculations, it is worth remembering as a way to bring theoretical values closer to on-site conditions.

How should we think about the reasons for regional differences?

We've reviewed six methods so far, but it's important to clarify why there are regional differences in the first place. Regional variations in solar power generation cannot be explained simply by whether there are more or fewer sunny days. They are determined by a combination of factors such as solar irradiance, latitude, solar altitude, temperature, patterns of snowfall and rainfall, and the frequency of cloudy days.

First, the most significant factor is the variation in solar radiation conditions throughout the year. Even with the same equipment, regions where solar radiation is more stable and regions that are more affected by clouds and rainfall will show differences in annual power generation. Next, there are differences in latitude and solar altitude. These affect how orientation and tilt perform, influencing seasonal shadow patterns and incident light conditions.

Moreover, temperature cannot be ignored. Strong solar irradiance does not automatically confer an advantage; in regions where high temperatures easily cause output declines, summer gains can be suppressed. Also, in snowy areas, winter generation tends to drop, and in regions heavily affected by the rainy season or persistent cloud cover, generation in certain months may struggle to increase.

In practice, it is important not to attribute such regional differences to a single reason. Rather than dismissing them with "this region has weak solar radiation" alone, organizing factors from multiple perspectives—"annual solar radiation conditions," "seasonal weather," "high-temperature losses," and "winter shading and snowfall"—will increase confidence in the calculations. After all, carrying out calculations by region is essentially the task of deciding how far to translate these multiple differences into numerical values.

Commonly Overlooked Points in Regional Calculations

When calculating by region, a common mistake is to treat everything uniformly based only on prefecture names. Even for the same regional name, conditions can differ between coastal areas, inland areas, plains, highlands, urban areas, and mountainous areas. In other words, if you interpret "by region" solely as an administrative division, you are likely to overlook site-specific differences. It is better to consider conditions that are as close as possible to the actual installation location.

Next, people often assume they've accounted for regional differences and end up ignoring differences in installation conditions. They tend to think it's sufficient once a regional coefficient has been applied, but in reality variations in orientation, tilt, and shading have a significant impact. Regional differences are an important entry point, but only when installation conditions are included do the values become suitable for practical use. It's important not to consider the job complete with regional differences alone.

Also, it is risky to compare only annual values without examining regional differences by month. Even if the total annual amounts look similar, some regions may experience a large drop in winter while others suffer losses due to high temperatures in summer. Those differences are hidden when you look only at annual values. Especially for projects that consider self-consumption or seasonal loads, it is better to take a monthly perspective.

Furthermore, it would be a waste not to make use of measured data and nearby precedents. You can make tidy comparisons using theoretical values, but in practice there are always region-specific quirks. If there are nearby results to reference, using them will yield figures closer to reality than desk-based assumptions. Don’t over-rely on theory—it's important to reflect experience in the numbers.

How to Proceed Without Confusing Operational Staff

To prevent practitioners from becoming confused when performing region-specific calculations, it is important to have a staged process for increasing accuracy. First, use Method 1 to grasp the annual outline from equipment capacity and regional coefficients. Next, incorporate monthly solar radiation and azimuth angle differences with Method 2 and Method 3, and, if necessary, reflect meteorological and temperature conditions with Method 4 and Method 5. Finally, if there are nearby performance records, make adjustments with Method 6. Following this order makes it easy to see how the numbers changed at each stage.

Also, it's important to record not only the numbers but also the assumptions. How you set the regional coefficient, what monthly trends you observed, how much correction you applied for orientation and tilt angle, and how you handled snowfall and high-temperature conditions. Organizing these will prevent confusion when reviewing later. Conversely, if only the numbers remain, you won't be able to trace why that kWh value was reached.

Moreover, the accuracy of acquiring on-site conditions directly affects the accuracy of regional calculations. Even if you properly account for regional differences, if the understanding of obstacle locations, candidate installation positions, and elevation differences is imprecise, shadow and layout corrections will be misaligned. In other words, calculating by region means considering both broad-area meteorological conditions and site-specific positional conditions together. In practice, treating these two together rather than separately reduces calculation errors.

Summary

To calculate solar power generation by region, six practical methods are commonly used: approximating with a regional coefficient of annual generation per 1 kW, stacking estimates based on monthly solar irradiation conditions, correcting for azimuth and installation angle including regional characteristics, accounting for region-specific snowfall, rainy days, and cloudy days, reflecting output reductions due to temperature conditions, and adjusting based on nearby actual results or projects in the same region. None of these is万能 on its own, but by using them stepwise you can represent regional differences quite close to reality.

Regional differences arise not only from solar radiation but from a combination of factors, including latitude, solar elevation, temperature, snowfall, and cloudiness trends. Therefore, in regional calculations it is important not to rely too heavily on a single figure and to clarify the extent to which each factor influences the results. Looking not only at annual values but also at monthly values and installation conditions together greatly increases the credibility of the numbers.

In practice, if you want to further increase accuracy, you need to consider not only desk-based regional variations but also the local positional relationships. If the positions of obstacles, elevation differences, and candidate installation locations remain ambiguous, shading conditions will be misaligned even if regional factors are applied carefully. In other words, to make regional calculations truly usable, it is important to accurately capture both broad-scale meteorological conditions and on-site conditions.

In that regard, for practitioners who want to grasp on-site positional relationships with high accuracy, the iPhone-mounted GNSS high-precision positioning device LRTK is useful. Because it makes it easier to accurately record candidate equipment locations and obstacle positions on site, it facilitates linking to generation calculations that reflect regional differences as well as shadows and layout conditions. Understanding how to calculate solar power generation by region is important, but to make those figures truly usable in practice, having a system in place to accurately capture on-site conditions is a major advantage.