Book a time slot in your calendar for the energyPRO Release Webinar! Exciting news! We have just launched new updates and features in energyPRO, and we want to make sure you are updated. Join our free webinar on November 22nd, 2023, from 13:00 to 14:00 CET, and get an in-depth overview of all the latest changes. […]
Dear PHOTOMONTAGE user! We are pleased to inform about the new visual features in the upcoming windPRO 3.6 release, which you can try already today by becoming a beta tester. See the presentation of the new 3.6 visual features on YouTube. As a beta tester you will get: Early access to all the new features. […]
The next version of windPRO contains a new visualization module. The SketchUp Integration Module. This makes it possible to work with a windPRO project in 3D in SketchUp. You can see some of the many possibilities in a short video by clicking here: Highlights: Show surface and rotating WTG’s in 3D Show Result Layers as […]
The “ALOS World 3D 30 m mesh (AW3D30)” dataset is a recent digital surface model (DSM) with global coverage. It is produced by JAXA – Japan’s Aerospace Exploration Agency. Now users of windPRO have direct access to the data in its 30m resolution version – along with its data-quality mask. AW3D30 was generated from data […]
Unlike solar and wind power, which rely on specific weather conditions, molten salt facilities can generate and store energy for up to 10 hours, operating at anytime. It is not a surprise that energyPRO allows for modelling various energy systems. It might come as news though, that we have recently designed a Molten Salt Storage […]
The ”Near-Global Forest Canopy Heights GLAD” dataset is developed from a fusion of GEDI lidar data (Global Ecosystem Dynamics Investigation Lidar) and Landsat imagery data. It has a near-global spatial coverage ranging from latitudes S52.0 to N52.0, and it reflects the 95% relative height of the canopy with a 25m ground area footprint. The GEDI […]
As wind energy analysts, we often find ourselves using land-cover datasets as the starting point of making our roughness classification models. Until now, the spatial resolution of the global land cover datasets has been limited to about 300m. This obviously limits the accuracy at roughness shifts (such as edges of forests, coastlines or cities).With the […]