6 Steps to Create Monthly Forecasts from Solar Power Generation Calculations

When creating monthly forecasts for solar power generation calculations, simply dividing the annual generation by 12 does not accurately reflect reality. Because monthly solar irradiance, installation tilt, azimuth, shading, temperature, equipment losses, and operating conditions change, monthly generation varies even with the same installed capacity. In practice, monthly forecasts are used in many situations such as pre-installation assessments, internal briefings, facility operation, verification of generation performance, and review of maintenance plans. Here, we outline the process for practitioners to create monthly forecasts in solar power generation calculations, divided into six steps.

Table of Contents

• Step 1 Decide the purpose and assumptions for the monthly forecast

• Step 2 Organize equipment capacity and installation conditions

• Step 3 Reflect monthly solar radiation and seasonal variations

• Step 4 Include loss coefficients to bring the power generation estimate closer to reality

• Step 5 Calculate and verify the monthly power generation

• Step 6 Review the differences from actual results and update the forecast

• Summary: It's important not to create a monthly forecast once and then treat it as finished.

Step 1 Decide the purpose and assumptions of the monthly forecast

The first step in creating a monthly forecast for solar power generation calculations is to clarify what the forecast is intended for. The level of detail required in generation calculations varies depending on the purpose. For example, the points to focus on in the same monthly forecast differ between roughly checking profitability before installation and evaluating the monthly generation performance of an operating system. If you start calculations without deciding on a purpose, you are likely to chase unnecessarily detailed numbers or, conversely, omit important conditions.

Monthly forecasts are not only for understanding the breakdown of annual power generation. Knowing month-by-month generation trends helps compare generation with electricity consumption, estimate surplus power, determine inspection timing, and detect anomalies early. For example, even in regions where generation tends to increase from spring to summer, the rainy season and typhoon season can have a significant impact. In winter, while sunlight hours shorten, temperature conditions can sometimes favor equipment conversion efficiency. Organizing these monthly characteristics as monthly forecasts is therefore effective.

As a prerequisite, first determine the period of interest. When considering a new installation, it is common to create a one-year monthly forecast based on standard meteorological data. For checking an existing facility, you may create monthly forecasts for the current or following year while comparing them with past power generation performance. In either case, monthly forecasts are only estimates, and actual power generation will vary depending on the weather and the condition of the equipment. Therefore, it is important to treat forecast values not as absolute figures but as reference values for decision-making.

Next, standardize the units used in the calculations. In photovoltaic generation calculations, multiple units are mixed, such as capacity, solar irradiance, energy generation, and loss rates. Capacity is often handled in kW and generation in kWh, and for monthly forecasts it is practical to organize the results as kWh per month. If units are left ambiguous here, it can cause confusion between capacity and energy generation or lead to mixing up daily and monthly values. Especially when using the results in internal documents or explanatory materials for stakeholders, clearly stating the calculation assumptions and units makes verification easier.

Also, decide in advance how much accuracy you want for the monthly forecast. The amount of data required changes depending on whether a rough estimate is sufficient, whether accuracy suitable for design studies is needed, or whether it will be used as a standard for operations management. For a rough estimate, you can calculate approximately based on monthly solar irradiance and system capacity. However, to make the forecast closer to reality, you need to consider installation tilt, orientation, shading, conversion losses of the power conditioner, wiring losses, temperature effects, and degradation over time. By deciding the purpose and desired level of accuracy up front, it becomes easier to create a monthly forecast that is neither excessive nor insufficient.

Step 2 Organize equipment capacity and installation conditions

The conditions of the solar power generation system itself form the basis for monthly forecasts. In solar power generation calculations, you first check the installed capacity. Installed capacity refers to the total output that the solar panels can produce under standard test conditions. Actual generation does not always equal the installed capacity, but it is the most fundamental input when creating a monthly forecast. Calculate the total capacity from the capacity per panel and the number of panels, and verify that the capacity used in the calculation matches the design documents and on-site conditions.

Next, the important factors are the installation tilt angle and orientation. Solar panels produce different amounts of electricity depending on the angle at which they receive sunlight. Even with the same installed capacity, a layout facing closer to south and one tilted east–west will have different monthly generation patterns. A larger tilt angle can better match the lower solar altitude in winter, while a smaller tilt angle can better match the higher sun in summer. However, optimal conditions vary by region and installation environment, so you should avoid simply asserting that a particular angle or orientation is always correct.

Installation conditions also vary depending on the installation type, such as rooftop installation, ground-mounted installation, carport type, or installations close to walls. Rooftop installations are often constrained by the roof’s pitch and orientation, while ground-mounted installations may allow adjustment of tilt and azimuth through the racking design. When creating monthly forecasts, it is important to use the actual installation conditions rather than ideal conditions. In addition to installation drawings, checking the site’s orientation, surrounding buildings, trees, topography, and adjacent equipment can reduce the risk of large discrepancies between forecast and actual results later.

Shadows must not be overlooked in monthly forecasts. Because the sun’s path changes with the seasons, the way shadows fall from the same obstacle varies by month. In winter, when the sun’s altitude is low, shadows from surrounding buildings and trees tend to stretch longer. In summer, with a higher sun altitude, daytime shadows tend to be shorter, while morning and evening shadows can affect power generation. Even if shadows fall on only part of the panels, the system configuration can cause a reduction in output. In monthly forecasts, we organize shadow assessment not simply as present or absent, but from the perspective of which month, which time of day, and to what extent shadows are likely to have an impact.

Additionally, consider site- and environment-specific conditions such as panel soiling, snow accumulation, salt damage, dust, and fallen leaves. It can be difficult to quantify everything precisely, but it is important to record as notes any conditions that are expected to have an impact. For example, in snowy regions, winter generation can drop to an extent that cannot be explained by solar irradiance alone. In coastal areas or around factories, soiling and corrosive conditions may increase the importance of maintenance. Organizing these conditions at the monthly forecasting stage provides more leeway in interpreting forecast values.

Step 3 Reflect monthly solar radiation and seasonal variations

At the core of creating monthly forecasts is the monthly solar irradiance. In photovoltaic generation calculations, how much solar radiation reaches the solar panels determines the amount of power generated. Using only the annual average makes it difficult to represent month-to-month variations. In practice, when producing monthly forecasts it is important to use monthly solar irradiance and reflect seasonal differences. This makes it easier to grasp the peaks and troughs in monthly generation.

Solar irradiance varies greatly by region. Even with the same installed capacity, monthly power generation will change if the solar radiation conditions differ. Also, within the same region, the amount of solar radiation received differs when solar panels are placed horizontally versus when they are tilted at a fixed angle. Therefore, it is desirable to use solar irradiance data that is as close as possible to the installation conditions. In practice, referencing regional, monthly, and slope-specific data and selecting conditions close to the installation angle and orientation increases the reliability of monthly forecasts.

When looking at seasonal variations, you should consider not only solar irradiance but also sunshine duration and weather tendencies. In spring, solar irradiance tends to increase and temperatures are less likely to become extremely high, so there are months when power generation tends to grow. In summer, although solar radiation is strong, it is necessary to account for output reductions due to high temperatures and for weather instability. In autumn, power generation can be affected by typhoons and prolonged rain, and in winter sunshine hours are short and the solar altitude is low. In this way, monthly power generation is not determined solely by the simple number of sunny days; it is the result of overlapping factors such as solar irradiance, temperature, solar altitude, and weather variability.

When dealing with monthly solar radiation, what you need to be aware of is the meaning of the average. Monthly solar radiation data are, in many cases, average values based on past meteorological data. Therefore, it is natural for the actual power generation in a given year to differ from the predicted value. If sunny weather continues, generation may exceed the prediction, and if prolonged rain or snowfall occurs, it may fall below the prediction. Monthly forecasts are not meant to hit each month exactly, but are used as a guideline for the typically expected generation. Sharing this premise with stakeholders makes it easier to explain discrepancies when actual results differ.

Also, when using monthly solar irradiance data, check the difference in the number of days. February and March have different numbers of days, and even with the same average irradiance, the monthly generation will change if the number of days differs. If you use daily-average irradiance, you need to multiply by the number of days in the target month to convert it to a monthly value. If you use monthly total irradiance, you can incorporate it directly into the monthly forecast. If you mix these up, the monthly generation can end up unnaturally large or small. Before starting calculations, always confirm whether the irradiance you are using is a daily average or a monthly total.

Step 4: Include loss coefficients to approximate realistic power output

Power generation calculated using only system capacity and solar irradiance tends to represent near-ideal conditions and can overestimate actual output. Therefore, monthly forecasts account for various losses to bring the figures closer to reality. Losses considered in solar power generation calculations include output reductions due to panel temperature rise, conversion losses in power conditioners (inverters), wiring losses, soiling on panel surfaces, shading effects, equipment degradation over time, and output curtailment. Even when it is difficult to separate and calculate all of these precisely, reflecting them together as a single loss coefficient produces forecasts that are practical for operational use.

When assigning loss coefficients, it is important not to use overly optimistic values. In pre-installation presentations there may be a temptation to make projected power generation look high, but forecasts that are too optimistic compared with reality will lead to a loss of confidence after operations begin. Conversely, estimating too conservatively will prevent a proper assessment of system performance. In practice, it is necessary to build up reasonable loss allowances based on installation and regional conditions. In particular, shading, soiling, snow, and temperature effects tend to vary month to month, so relying only on a uniform annual loss can cause the shape of monthly forecasts to deviate from actual performance.

Temperature effects are a factor to keep in mind when making summer forecasts. While solar panels tend to generate more power the stronger the solar irradiance, their output also tends to decrease as panel temperature rises. For that reason, summer does not necessarily produce maximum generation just because irradiance is higher. Depending on the region and installation conditions, spring or early summer can sometimes generate more efficiently. In monthly forecasts, it is important to account for temperature-related losses so as not to overestimate generation by looking only at summer irradiance.

Conversion losses and wiring losses cannot be ignored. DC power generated by solar panels is converted into a form suitable for use or grid interconnection. Certain losses occur during this process. Losses also occur due to the length and thickness of wiring and the condition of connection points. These factors often do not change significantly from month to month, but they should be reflected as a basis for monthly forecasts. If the equipment configuration is complex or distances are long, check the design-stage conditions and avoid oversimplifying.

Output curtailment and control of self-consumption also affect how monthly forecasts are interpreted. Even when conditions for power generation are met, generation output can be curtailed by grid-side conditions or equipment-side controls. For installations that assume self-consumption, it is necessary to separate the amount of power generated itself from the amount of electrical energy that can actually be used. When preparing monthly forecasts, distinguishing whether the forecast refers to pure generation potential or to the expected usable electrical energy will make later explanations less confusing.

Step 5: Calculate and Verify Monthly Power Generation

Once you have organized the conditions, calculate the monthly power generation. The basic idea is to multiply the installed capacity by the monthly solar irradiation conditions, then apply loss factors to obtain the monthly generation. In practice, combine monthly irradiation, installed capacity, corrections for installation conditions, and loss factors to produce monthly kWh. The formula itself does not need to look complicated, but it's important to keep it in a form that allows tracing which conditions were applied in what order.

In monthly forecasts, check not only the calculation results but also the month-by-month order. Generally, months with higher solar irradiance tend to have higher power generation, and months with lower irradiance tend to have lower power generation. However, temperature effects, the rainy season, snowfall, and the way shadows fall can cause the changes not to be a simple increase or decrease. If a specific month is extremely high or low, check for input errors in solar irradiance, the handling of the number of days in the month, the setting of loss coefficients, and any unit mix-ups. When the appearance of the calculation results seems off, it is important to always return to the input conditions.

Also verify consistency with the annual generation. Check whether the sum of the 12 monthly forecasts falls within a reasonable range for the annual forecast. If the annual and monthly values are produced separately, confirm that they do not contradict each other. You can allocate the annual generation to months using monthly ratios, but even then it is preferable to use ratios based on monthly insolation and installation conditions. A simple equal division by 12 cannot reflect actual seasonal variations, so it reduces the usefulness of the monthly forecast.

When checking monthly power generation, you should look not only at the amount generated but also whether it fits the intended operational use. If self-consumption is prioritized, check the relationship between monthly generation and monthly electricity consumption. Even in months with high generation, if consumption is low, surpluses may increase. Conversely, if generation does not rise in months with high consumption, you will need to consider alternative measures. Monthly forecasts should be used not only to assess the facility’s generation capacity but also linked to overall operational planning.

Also, for materials shared with stakeholders, be prepared to succinctly explain the assumptions behind forecast values. Presenting only the calculation results makes it difficult to convey why those numbers were obtained. Organizing items such as equipment capacity, region, installation tilt, azimuth (orientation), solar irradiance, loss factors, how shading is treated, and the target period will also be helpful when reviewing later. Monthly forecasts are based on the conditions at the time they are created, so if those conditions change the calculation results will change as well. Therefore, in practice it is essential to manage not only the numbers but also the conditions.

Step 6 Review the difference from actual results and update the forecast

Monthly forecasts are not finished once they are created. If the equipment is actually operating, it is important to compare actual generation with the forecasted values and check the reasons for any differences. Monthly forecasts for solar power generation calculations are estimates based on average conditions, so they will not match exactly every month. Even so, by continuously monitoring the differences between forecast and actual performance, you can confirm the condition of the equipment and the validity of the calculation assumptions.

When actual performance falls below the forecast, it is premature to immediately conclude that the equipment is faulty. First, check the impact of the weather. Prolonged rain, cloudy skies, typhoons, snowfall, yellow sand, or dust from nearby construction — temporary factors like these can reduce power generation. Next, check for changes in shading, dirt, weeds, fallen leaves, and alterations in the surrounding environment. Expansions of buildings or growth of trees can create shadows that were not anticipated at the time of installation. After organizing these external factors, checking for equipment abnormalities and missing measurement data makes it easier to narrow down the cause.

Even when actual performance exceeds forecasts, we confirm the reasons. We check whether it was simply due to more sunny days, whether the loss coefficient had been estimated too conservatively, or whether the installation conditions were better than assumed. Exceeding the forecast itself is not a problem, but if you leave the reason unknown you may make incorrect decisions in subsequent months. Monthly forecasts should improve in accuracy through the gap with actual results. In particular, during the first year of operation it is effective to accumulate monthly results and use them as a benchmark from the second year onward.

When updating forecasts, it is not necessary to change every condition every month. In fact, overreacting to short-term weather variability can make the baseline unstable. What should be reviewed are factors that consistently create differences, such as installation conditions, shading effects, loss factors, measurement methods, and the handling of actual performance data. For example, if there is a large underperformance every winter, it may be necessary to reflect winter shading and snow conditions in the forecast. If underperformance occurs only in summer, there may be room to review how temperature effects and output curtailment are treated.

In performance management, being able to check not only monthly power generation but also daily and hourly data makes root-cause analysis easier. However, when creating a monthly forecast, it is not always necessary to handle all the detailed data. What matters is comparing the monthly forecast with actuals and being able to return to detailed data when needed. If, in months when generation drops, you can distinguish whether the cause was weather, equipment, shading, or measurement issues, maintenance actions and internal reporting will proceed more smoothly.

Summary: Monthly forecasts should not be treated as a one-time effort

To create a monthly forecast in solar power generation calculations, it is important to follow the process of setting objectives, organizing equipment conditions, reflecting monthly irradiance, setting loss coefficients, checking calculation results, and reviewing discrepancies with actual performance. A monthly forecast is not sufficient if you simply divide the annual generation by 12. Because solar power generation is affected by seasons and weather, tilt angle, azimuth, shading, temperature, and equipment losses, calculations must be based on month-by-month variations.

What matters for practitioners is not simply looking at the numerical results of calculations, but being able to explain the conditions from which those numbers were derived. By organizing equipment capacity, solar irradiance, loss coefficients, handling of shading, the analysis period, and monthly weather trends, it becomes easier to explain to stakeholders and to review later. In particular, for pre‑implementation assessments and operational evaluations, it is necessary not to regard forecasts as absolute, but to adopt an approach of updating them while cross‑checking against actual performance.

Moreover, monthly forecasts not only estimate power generation but also support comparisons with electricity consumption, maintenance planning, anomaly detection, and internal reporting. When the gap between forecasts and actual performance becomes visible, equipment condition and operational issues can be identified earlier. By continuously managing month-to-month generation trends, solar power systems become easier to operate more stably.

If you want to produce monthly forecasts more efficiently and manage them linked to local conditions and generation performance, it is effective to consider a management approach that can organize installed capacity, solar irradiance, installation conditions, loss coefficients, and performance data according to the same standards. Rather than relying solely on specific numbers, by treating calculation conditions and performance verification together, solar power generation calculations can be turned into practical decision-making materials that are easy to use in day-to-day operations.