Harnessing the Sun: Estimating Solar Energy with Satellites and Ground Stations

Large-scale solar energy projects are becoming increasingly important in Finland, driven by the global shift toward renewable energy and the country’s commitment to reducing carbon emissions. Despite Finland’s northern location and relatively low annual sunlight levels compared to southern Europe, advancements in solar technology and energy storage solutions have made solar power a viable and rapidly growing option. According to the Finnish Energy Agency, there are currently 24 active solar plants with production capacities exceeding 1 MW, 12 more under construction—including one with a capacity of up to 200 MW—and over 150 projects in the authorization phase. This highlights the remarkable expansion of the sector.

Accurate solar energy yield estimations rely on meteorological datasets sourced from geostationary satellites and ground-based measurement stations. The key variables in these datasets are solar irradiance and ambient temperature, both of which are essential for modeling photovoltaic (PV) performance.

Satellites provide worldwide solar energy data, making them useful for early site evaluations and large-scale planning. However, they have some limitations: the data is updated only every 10-15 minutes and covers areas in blocks of about 1 kilometer. Additionally, these satellites often don’t capture information from extreme northern and southern regions, which poses a challenge for countries like Finland.

On the other hand, ground-based stations provide highly detailed, real-time weather data for specific locations, updating every minute. This information is crucial for fine-tuning solar energy estimates and understanding local conditions. However, these stations are rare and unevenly spread out, as they are usually managed by meteorological and research organizations. This scarcity can make it difficult for developers to access precise data in less monitored areas.

High-latitude regions, such as Finland, present additional challenges for solar irradiance estimation. Studies have shown that satellite-based irradiance data often underestimate solar radiation in these areas, particularly during winter when snow cover is prevalent. This underestimation occurs because satellite algorithms struggle to differentiate between snow-covered surfaces and cloud cover. Additionally, it has been demonstrated that latitude significantly influence the accuracy of satellite-derived solar data, with discrepancies increasing as one moves further from the equator.

The study will deliver a detailed comparison of satellite and ground-based data, highlighting their respective influences on PV yield estimations. It will identify the main causes of discrepancies between the datasets and examine environmental factors unique to high-latitude regions, such as prolonged snow cover and seasonal variability, that impact energy estimation accuracy.