Tuesday 04 March 2025
Researchers have developed a new method for creating highly accurate maps of solar energy potential across Norway, using a combination of satellite data and machine learning algorithms.
Solar power is becoming an increasingly important source of renewable energy globally, but its potential can vary greatly depending on factors such as location and time of year. To harness this energy effectively, accurate maps of solar irradiance – the amount of solar energy that reaches the Earth’s surface – are crucial.
Traditionally, these maps have been created using data from satellites and weather models, but these sources can be limited by their resolution and accuracy. In Norway, for example, high-latitude regions receive less sunlight throughout the year, making it challenging to generate accurate maps of solar energy potential.
To overcome this challenge, researchers used a machine learning approach, combining data from multiple sources including satellite imagery, weather models, and in-situ measurements taken at ground-level radiation stations. By training a neural network on this data, they were able to create highly accurate maps of solar irradiance across Norway, with an error rate significantly lower than previous methods.
The new maps provide a detailed picture of the country’s solar energy potential, showing how much energy can be generated per square meter at different times of day and throughout the year. This information is crucial for planners and policymakers, as it allows them to identify areas where solar power could be harnessed most effectively and invest in infrastructure accordingly.
One of the key advantages of this new approach is its ability to capture regional variations in solar irradiance. Traditional methods often rely on coarse-resolution data, which can struggle to accurately represent local differences in climate and geography. By using a machine learning algorithm trained on high-resolution data, researchers were able to create maps that reflect these subtle variations.
The implications of this research are significant, not just for Norway but also for other countries with varying climates and geographical features. As the world continues to transition towards renewable energy sources, accurate mapping of solar energy potential will become increasingly important for effective planning and deployment.
In the long term, this technology could be used to create detailed maps of solar energy potential globally, helping policymakers and industry leaders to make informed decisions about where to invest in renewable energy infrastructure. With the increasing importance of sustainable energy solutions, this research has significant potential to contribute to a cleaner, more efficient future for our planet.
Cite this article: “Accurate Mapping of Solar Energy Potential in Norway Using Machine Learning and Satellite Data”, The Science Archive, 2025.
Solar Energy, Norway, Machine Learning, Satellite Data, Renewable Energy, Solar Irradiance, Weather Models, Ground-Level Radiation Stations, Neural Network, Geographical Features
