Six steps to calculate solar power generation from solar irradiance data

When calculating solar power generation, the method of estimating annual kWh from installed capacity alone is very convenient. However, if you want figures that are more convincing in practice, it is useful to understand the approach of constructing estimates from solar irradiance data. Public solar irradiance databases provide monthly average data and time-of-day data, and you can also check irradiance by azimuth angle and tilt angle. Such data can be used not only for rough estimates of annual generation but also for month-by-month comparisons and for validating system conditions. ([NEDO][1])

Especially for practitioners searching for "solar power generation calculation", what's important is not merely looking at irradiance data as-is, but how to connect system capacity in kW, the units of irradiance, and the actual generated energy in kWh. The units of irradiance are not the generation itself; they are converted into kWh only through the system and loss conditions. Therefore, when using irradiance data, it is essential to grasp the meaning of the units, how to choose azimuth and tilt, how to expand to monthly and annual values, and the order of corrections. ([気象庁][2])

Also, the method of calculating power generation from solar irradiance data is useful as a simple simulation before undertaking heavy, specialized analyses. Monthly-average tilted-surface solar irradiance data have been widely used for everything from residential photovoltaic systems to various rough estimates of power generation, and using hourly data allows for a somewhat more detailed view. In other words, the strength of solar irradiance data is that it can be used both for initial comparisons and for improving the accuracy of proposals. ([NEDO][3])

Table of Contents

• What it means to calculate from solar irradiance data

• Step 1 Align the types of solar irradiance data and units to be used

• Step 2 Select data that matches the site, orientation, and tilt conditions

• Step 3 Convert daily integrated solar radiation into monthly and annual values

• Step 4 Express the relationship between system capacity and energy generation as an equation

• Step 5 Adjust for losses and site conditions

• Step 6 Connect to self-consumption and surplus

• Common misconceptions in solar irradiance data calculations

• Summary

The Significance of Calculations from Solar Radiation Data

The purpose of calculating electricity generation from solar radiation data is to translate regional and seasonal differences that cannot be seen from system capacity in kW alone into numbers. For example, even with the same 10 kW system, the annual kWh will differ between areas with good solar conditions and areas that are more affected by cloudy weather or snowfall. Furthermore, even within the same region the output changes in spring, summer, autumn, and winter. By using solar radiation data, these differences become readable as monthly and annual figures rather than just a vague sense. ([NEDO][3])

In particular, monthly average tilted-surface irradiance data have long been widely used for approximate power output estimation of residential photovoltaic systems. This is because they more readily reflect the incident light conditions according to the tilt of roofs and mounting racks. Rather than looking only at irradiance on the horizontal plane, examining the tilted-surface irradiance that the equipment actually receives brings the initial estimate of power generation closer to on-site conditions. ([NEDO][3])

Also, using time-of-day data allows you to discern finer differences between time periods than monthly averages. This is particularly effective when considering self-consumption and battery storage. Public solar radiation databases provide both monthly average data and time-of-day (hourly) data, which are used depending on the stage of the project. In other words, calculations based on solar radiation data are not mere theoretical exercises but a practical method that bridges simple estimates and detailed analyses. ([NEDO][1])

The value of using solar irradiance data lies in bringing the size of a system closer to the reality of power generation. Simply talking about how many kW a system has makes it hard to form a picture of self-consumption, surplus, or reduction effects. However, by applying solar irradiance data it becomes easy to convert to annual kWh or monthly kWh, making it simpler to connect those figures to the system’s value. In other words, it’s helpful to think of solar irradiance data as the material that gives a site-level time axis to estimates of photovoltaic generation. ([NEDO][3])

Step 1: Standardize the types and units of solar radiation data to be used

The first step is to make the types and units of the solar irradiance data you use consistent. The most important point here is not to confuse irradiance figures with power generation figures. In the Japan Meteorological Agency’s classification, instantaneous values are expressed in kW/m² (kW/ft²) and cumulative amounts in MJ/m² (MJ/ft²). In other words, solar irradiance data indicate how much solar energy falls on 1 m² (10.8 ft²), and do not represent the total power output of a facility as-is. ([Japan Meteorological Agency][2])

In practice, monthly average tilted-surface irradiance data and time-of-day irradiance data are often used. Public solar radiation databases organize monthly average and time-of-day data, allowing you to check monthly average daily-integrated irradiance and irradiance for each time of day. First, depending on the project's purpose, decide whether monthly averages are sufficient or whether time-of-day data are necessary. If you only need to grasp the outline of annual power generation, monthly averages are often sufficient; if you want to assess self-consumption or battery storage, time-of-day data are useful. ([NEDO][1])

When aligning units, treat the units of solar irradiance data and system capacity as separate. System capacity is in kW, solar irradiance is in kW/㎡ (kW/ft^2) or MJ/㎡ (MJ/ft^2), and the resulting generated energy is in kWh. In other words, if you clearly separate each unit at the outset of the calculation, it becomes much easier to see where conversions are required later. If this is left ambiguous, it's easy to mistake the irradiance value for generated energy or to convert system capacity into kWh without any notion of time. ([気象庁][2])

Also, in practice, units of energy such as Wh, kWh, and MWh can easily get mixed. For small-scale systems or household estimates, kWh is the main unit, but for large-scale systems MWh can be easier to read. When making comparisons, it is important to standardize the units. Organizing the roles—system capacity in kW, generated energy in kWh, and solar irradiance in kW/m^2 (kW/ft^2) or MJ/m^2 (MJ/ft^2)—is the first step in making use of solar irradiance data. ([Japan Meteorological Agency][2])

Step 2 Select data that matches the location, orientation, and tilt conditions

The second step is to select data that match the site and orientation/tilt conditions. When calculating electricity generation using solar irradiance data, it is not sufficient to look only at national averages or irradiance on a horizontal surface. Photovoltaic systems are installed at specific locations, and roofs or mounting racks have particular orientations and tilts. Therefore, it is important to choose data that are as close as possible to the installation site and to conditions similar to the system’s orientation and tilt. ([NEDO][3])

Public solar radiation databases allow you to check monthly mean cumulative solar irradiation and annual averages by azimuth and by tilt angle. This is because they are organized on the premise that the solar radiation received at the same location varies depending on whether it is south-facing or east–/west-facing and on differences in slope. In other words, you need to match not only the location but also the orientation and tilt of the installation in the data. Using values for irradiation on tilted surfaces brings the initial estimate of power generation much closer to actual site conditions than using horizontal-plane values. ([NEDO][4])

If you skip this step, for example, you may end up evaluating the east and west faces using the values for a well-oriented south-facing face, or conversely applying unfavorable conditions to the whole. In particular, for detached houses, factories, warehouses, and other buildings with multiple roof surfaces, each surface faces a different direction, so calculating the whole using a single condition becomes quite coarse. In practice, it is convenient to select data separately for the south, east, and west faces and then sum the generated electricity for each at the end. ([NEDO][4])

Also, even with the same orientation, the irradiation conditions change if the tilt changes. The reason public databases provide monthly average solar radiation by tilt angle is precisely because this difference is large. In other words, selecting data by the combination of location, orientation, and tilt forms the foundation when calculating power generation from solar radiation data. If this step is done carefully, you can often avoid having to apply overly strong orientation or tilt corrections later. ([NEDO][4])

Step 3: Convert daily cumulative solar radiation to monthly and annual totals

The third step is to convert daily integrated solar radiation into monthly and annual values. When using solar radiation data, simply looking at the monthly average daily integrated radiation or the radiation by time of day does not yet translate into the total system power generation in kWh. What is needed, therefore, is the approach of aggregating solar radiation information at hourly or daily scales into monthly or annual energy amounts. ([NEDO][4])

If the monthly average of daily integrated solar radiation is available, multiplying it by the number of days in that month reveals the total solar resource for the month. Furthermore, by linking that to the installed capacity and system conditions, it can be converted into monthly power generation. If time-resolved data are available, the idea is to aggregate the solar radiation at each time step to form one day's total and then expand that to the month or year. In either case, understanding the flow of converting daily data to monthly data and monthly data to annual data makes it considerably easier to organize things even without figures. ([NEDO][1])

The point of this procedure is to give the solar irradiance data a time dimension so you can understand how much energy the installation receives over the course of a year. For example, in summer months, if the daily cumulative solar irradiance is high and there are more days, the monthly generation potential will be large. In winter months, because the daily cumulative solar irradiance is low and sunlight hours tend to be shorter, the monthly potential will be small. In other words, by expanding daily cumulative solar irradiance into monthly and annual values, the differences among spring, summer, autumn, and winter become much clearer. ([NEDO][3])

It is important here not to confuse the units of solar radiation with the units of electricity generation. A high daily integrated solar radiation does not, by itself, determine a system’s electricity output. Only by accounting for system capacity, the conditions of the receiving surface, losses, and so on, do you arrive at the final kWh. In other words, this procedure is merely the stage of organizing solar radiation data along the time axis; the next step is to combine it with the system conditions. ([気象庁][2])

Step 4 Formulate the relationship between installed capacity and power generation as an equation

The fourth step is to express the relationship between system capacity and generated energy in an equation. Here, finally, you link the system capacity in kW with the generation-equivalent hours and insolation conditions derived from solar radiation data and convert them into actual generation in kWh. A practical approach for use in the field is to multiply the system capacity by the generation-equivalent hours for that month or year. For annual values, use the expected generation per 1 kW per year; for monthly values, use the generation-equivalent hours for each month. ([NEDO][3])

For example, monthly generation is easier to organize if you think of it in the form: installed capacity (kW) × the month's average equivalent generating hours (h) × number of days in the month × a correction factor. If the installed capacity is 5 kW, the average equivalent generating hours are 4.0 h, there are 30 days, and the correction factor is 0.82, the monthly generation is 492 kWh. This formula means you are multiplying the system's capacity by how many hours' worth of generation you can expect in that month. Solar radiation data are used as the underlying input data that support this equivalent generating time. ([NEDO][4])

The same idea applies to annual values. Multiply the guideline annual generation per 1 kW that corresponds to the region and orientation/tilt conditions by the installed capacity to obtain the annual input value. The important point here is not to misunderstand the insolation data itself as kWh. Insolation is only the baseline of energy received by the installation, and it is only when you factor in the installed capacity and system conditions that it is converted into electrical energy. In other words, you must clearly separate the insolation stage from the generation stage. ([気象庁][2])

Also, at this stage you should simultaneously check whether the installed capacity in kW can actually be accommodated on site. For example, even if back-calculating from the required power generation suggests you need 10 kW, if the roof's usable area and panel layout only allow 8 kW, the target generation should be revised at that point. In other words, including installed capacity in the equation is not simply multiplying numbers; it also means verifying whether the capacity can realistically be achieved. ([NEDO][3])

Step 5 Adjust for Losses and Site Conditions

The fifth step is to correct for losses and site conditions. Up to this point, by linking the system capacity and solar irradiance data you can produce initial annual kWh and monthly kWh values, but those figures are still theoretical. Actual generation changes slightly due to orientation deviations, shading, losses in conversion equipment, wiring losses, output reduction from high temperatures, soiling, and so on. That is why corrections are essential to make the numbers usable in practice. ([NEDO][3])

As a way of thinking, it's easier to understand if you organize it as: actual generation (kWh) = system capacity (kW) × reference generation appropriate to the region, orientation, and tilt × shading correction × loss correction. Orientation and tilt may be partly reflected in the insolation data stage, but keeping shading and losses separate makes the meaning of the numbers easier to see. For example, conditions such as shading only in the morning, snow affecting only in winter, or equipment shadows falling in the afternoon are difficult to capture with reference insolation alone. ([NEDO][4])

Also, when considering loss corrections, it is important to be aware of what is included and to what extent. You can lump together all losses such as losses due to high temperatures, wiring losses, conversion losses, and soiling, or you can separate only the items that are important for the project. The key is to clarify which losses are estimated from solar irradiance data and which are site-specific. Otherwise, you are likely to double-count the same loss or, conversely, overlook it. ([Japan Meteorological Agency][2])

Especially in practical work, it is important not to underestimate the impact that shading conditions have on power generation. Rather than judging shading as simply "present" or "absent," it is better to consider "at what times," "on which surfaces," and "to what extent" it affects output. Furthermore, because the solar altitude is lower in winter and shadows tend to be longer, there can be considerable differences in monthly power generation. In other words, instead of applying a correction once to the annual value, it is more realistic to apply corrections by month or by surface where necessary. ([NEDO][3])

Step 6: Connect to Self-Consumption and Surplus

The final step is to link generation to self-consumption and surplus. Calculating power output from solar irradiance data alone still reveals only half of the system’s value. What you really need to know in practice is how much of that generated electricity can be used on-site and how much will be exported as surplus. In particular, for homes, factories, warehouses, and offices, the value of the system varies considerably depending on daytime demand. ([meti.go.jp](https://www.meti.go.jp/shingikai/santeii/pdf/100_01_00.pdf?utm_source=chatgpt.com))

The approach is to estimate self-consumption (kWh) and express surplus electricity (kWh) = generation (kWh) − self-consumption (kWh). For example, if the annual generation is 10,000 kWh and 4,000 kWh of that can be used for daytime loads, the surplus is 6,000 kWh. This simple subtraction, however, can greatly change the significance of the system. Even with the same annual generation, projects with high self-consumption and projects with large surplus have different economic outcomes. ([meti.go.jp](https://www.meti.go.jp/shingikai/enecho/denryoku_gas/saisei_kano/pdf/062_02_00.pdf?utm_source=chatgpt.com))

Also, when viewed by month or by time of day, this gap becomes even larger. In summer, with high generation and high air-conditioning demand, self-consumption may be more likely to increase. In spring and autumn, even if generation is high, surpluses tend to grow if usage is moderate. In winter, because generation falls, the self-consumption rate may appear high even when surpluses are small. In other words, only after deriving generation from solar irradiance data and then examining how that electricity is used does the value of the installation become quite concrete. ([nedo.go.jp](https://www.nedo.go.jp/content/100778067.pdf?utm_source=chatgpt.com))

In practical work, once you've organized things this far, it becomes much easier to directly connect to electricity bill savings, the amount of power sold, battery capacity, and payback period. Calculations from solar irradiance data are not merely academic estimates of generation; they are also an entry point for understanding how the equipment is used. Even without diagrams, if you grasp this final reinterpretation, the meaning of solar irradiance data becomes much easier to appreciate. ([meti.go.jp](https://www.meti.go.jp/shingikai/santeii/pdf/100_01_00.pdf?utm_source=chatgpt.com))

Common Misconceptions in Solar Radiation Data Calculations

One common misconception when calculating generation from solar irradiance data is to mistake the irradiance figures themselves for generation. Values in kWh/㎡・day (kWh/ft²·day) or MJ/㎡・day (MJ/ft²·day) are merely the amount of solar energy incident per unit area, and are not the total generation of the installation as-is. They are converted to kWh only through the installed capacity and the system conditions, so skipping that step will cause the meaning of the numbers to be distorted. ([気象庁][2])

Another common mistake is to use the solar radiation on a horizontal plane as-is. Public databases provide monthly average daily integrated solar radiation for each azimuth and tilt angle, and these data are widely used to estimate rough power generation. In other words, if the equipment is actually mounted on an inclined surface, it is better to use data that reflect tilted‑surface irradiance. Proceeding with only horizontal‑plane data tends to obscure differences in orientation and tilt. ([NEDO][4])

Also, it is risky to stop with only the annual input value. Annual kWh is convenient for comparing system sizes, but assessing self-consumption and surplus and understanding summer–winter differences require monthly and time‑of‑day perspectives. The strength of insolation data is that it makes estimating such seasonal variations easier, so it would be a shame to stop at the annual average alone. ([NEDO][3])

Furthermore, it is a common mistake to determine power generation solely from solar irradiance data without inspecting on-site conditions. Solar irradiance data provides information about regional and orientation conditions, but it does not automatically correct for shading, equipment, or the actual layout. In other words, solar irradiance data is extremely important, but it is not sufficient on its own; you must always overlay the actual on-site positional relationships as the final step. ([NEDO][3])

Summary

To calculate photovoltaic power generation from solar irradiance data, it is easier to organize the work by thinking in six steps: first align the types and units of the data to be used; next select data that match the site and the orientation/tilt conditions; convert daily cumulative irradiance to monthly and annual values; then express the relationship between system capacity and generation as an equation; correct for losses and on-site conditions; and finally extend the result to self-consumption and surplus. Solar irradiance data are not the answer by themselves, but they are extremely useful as the foundation for converting system capability into on-site generation. ([NEDO][1])

What is important is not simply looking at solar irradiance data, but understanding the meaning of the units, the differences in orientation and tilt, the monthly and annual distributions, and the role of loss corrections. In doing so, calculations of photovoltaic generation become figures that are not merely theoretical values but connect to equipment comparisons, verification of self-consumption, and the management of surplus and electricity sales. In other words, calculations derived from irradiance data can be said to be the gateway that links generation estimates to practical operations. ([NEDO][4])

If you really want to improve the accuracy of such calculations, it is essential to accurately grasp the on-site conditions. If the roof orientation, the positions of obstacles, elevation differences, and how shadows fall are ambiguous, then no matter how carefully you select solar irradiance data, the final generated energy in kWh will tend to vary. In particular, shadows and the effective area are aspects where the on-site spatial relationships directly affect the value of the installation. ([NEDO][3])

In that respect, LRTK, an iPhone-mounted GNSS high-precision positioning device, is extremely effective as a means of accurately grasping on-site positional relationships. Because it makes it easier to accurately record the positions of roof edges and obstacles in the field, it facilitates linking irradiance data that accounts for orientation, shading, and layout conditions to power generation estimates. If you want solar power generation figures derived from irradiance data to be truly usable numbers, firmly capturing site conditions with methods like LRTK is a major practical advantage.