While it has been interesting to gather insights into how the industry is assessing the natural resources on which they rely, it has also revealed the fact that the renewable industry is vulnerable to drawing long-term conclusions on short-term measurements: Often the wind resource is only measured for one year in a particular location and that is what the productivity estimate is based upon.

This approach endangers renewable energy producers to lose sight of natural climate variability which can cause large variations from year to year. Another factor currently not being considered widely so far is the effects of climate change on renewable energy resources and the financial outcomes of renewable projects – which could be a costly mistake as renewable energy is becoming an increasingly competitive market space. While the direct wind-speed output of climate models might not be the best dataset to estimate future climate change consequences, using climate projections can allow us to study future weather patterns and their changes.

The solution for the renewable industry to overcome the challenges of changing conditions would be to integrate the anticipatory meteorological analysis model to overview the potential of the assets during their lifespan.

In this project, as GBE, we endorsed and implemented the practice of adding value to resource assessment by (1) extending time series and considering natural climate variability as well as (2) applying a multivariate statistical modelling approach to assess future trends in resource productivity and variability. While it would be unrealistic to expect a point forecast, we as well can provide a narrower corridor of resource trends and give a competitive edge.