Sign up today to get the best of our expert insight in your inbox.

For details on how your data is used and stored, see our Privacy Notice.

How climate modelling can shape the renewable energy landscape | Podcast

New forecasts for weather patterns could help the solar and wind industries make better investment decisions in the long term

Climate trends are accelerating rapidly. Global temperatures hovered consistently at around 1.5 degrees above pre-industrial levels from January to August. Then in September, they shot up to 1.8 degrees. Dr Zeke Hausfather, research scientist at Berkeley Earth, opined in a recent NYT piece that global warming has actually accelerated in the last 15 years, rather than continuing at a gradual pace.

The effects of climate change are no longer something for the next generation to worry about; they’re being felt here, and now. As a result, it’s crucial to deploy renewables as quickly and efficiently as possible. This involves continuing to invest in the two largest sectors – wind and solar.

There’s a strong correlation between the effectiveness of these energy sources and the weather predictions we make to inform our long-term planning and investment decisions.

Anticipating and planning for variability in supply and demand comes from analyzing historical weather and climate data.

On the Interchange Recharged today, David Banmiller is joined by Rob Cirincione, founder and CEO of Sunairio. They have a model that they say can make better predictions for solar and wind demand and supply, helping the industry to make better investment decisions and deploy more quickly.

Traditionally, historical data has been the primary tool for making predictions about future weather events and their possible impact on supply-demand imbalances. Historical data has its limits and does not always provide an accurate representation of future weather events. With climate change accelerating faster than we thought, and with a limited amount of historical data available, there’s a need for modeled projections to fill this gap.

For instance, in the solar industry, historical average models like the typical meteorological year (TMY) are used to predict future performance and returns. However, the assumption that the climate is the same as when the model was developed is flawed. Therefore, it's essential to continually measure and observe the impact of climate trends on irradiance and thus, the performance and returns of solar projects.

Rob explores with David the tools used to predict weather-driven variability in energy, what the solar industry currently uses to predict long-term performance, how to apply the predictive model Sunairio is developing to make better investment decisions, and how progress with decarbonization efforts could impact future forecasts.

Subscribe to the show on your podcast platform of choice and visit woodmac.com/podcasts to listen back to previous episodes. Join in the conversation on X – we’re @interchangeshow