Meteonorm’s accuracy depends on terrain complexity and data density:
Meteonorm’s default datasets are historically anchored (e.g., 1991–2010). In an era of anthropogenic climate change, the assumption of a stationary climate is increasingly flawed. A building designed today using a historical TMY or synthetic dataset may face a significantly different climate by 2040. meteonorm
A solar developer in Peru needed a feasibility study for a 50 MW PV plant. There were no weather stations within 100 km. Using Meteonorm, they generated a TMY file, imported it into PVsyst, and estimated annual yield within 7% of later on-site pyranometer data — enough to secure initial investment. A solar developer in Peru needed a feasibility
For critical projects (e.g., utility-scale solar farms), it’s common to validate Meteonorm against on-site measurements for 6–12 months. For early-stage planning, it’s industry-standard. For critical projects (e
To understand the capabilities and limitations of Meteonorm, one must dissect its core algorithmic structure. The software operates on a three-step hierarchy: data sourcing, spatial interpolation, and stochastic synthesis.
Meteonorm is famous for its sophisticated algorithms that estimate global horizontal irradiance (GHI), direct normal irradiance (DNI), and diffuse radiation — even in mountainous or shaded terrain.