Forecasting Renewable Energy and Climate Modeling

This chapter explores the ability of modeling to forecast renewable energy, focusing on solar and wind. It discusses the analysis of declining wind resources and its implications for utility-scale wind production. The chapter also discusses the challenges and future prospects of wind and solar energy markets, as well as the impact of climate change on forecasts.

Transcript

chevron_right

Play full episode

chevron_right

Transcript

Episode notes

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, we are joined by Rob Cirincione, founder and CEO of Sunairio. They have a model which 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 it was 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 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 decarbonisation 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

00:00:00: Introduction to the show

00:00:01: Rob's career and the start of Sunairio

00:00:06: The weather's impact on energy supply and demand

00:00:37: Tools used to predict weather-driven variability in energy

00:01:01: The limitations of using historical weather data

00:01:47: The reason for creating Sunairio

00:02:02: Sunairio's role in the industry

00:03:18: Investment analysis and planning in regards to weather events

00:03:32: Current practices in solar industry

00:04:38: Flaws in using historical data for future predictions

00:07:18: The impact of changing climate trends on the solar performance

00:09:02: The importance of this analysis for investors and project managers