Fighting Climate Change With Big Data: Clir And SINAI Technologies
When you think about solving the climate crisis, what springs to mind?
Most people’s knee-jerk reaction is along the lines of “electrification,” “carbon sequestration,” “recycling,” or “renewable agriculture.”
While not many think of phrases like “big data” or “artificial intelligence,” several recent conversations have convinced me how important these fields are to helping our civilization thrive and survive into the next century.
The two founder / CEOs with whom I have had the pleasure to speak recently use AI in very different ways and in completely different fields, but it is clear that the ubiquity of cheap computing power, combined with smart engineers and focused, visionary entrepreneurs represents a formidable force in helping us mitigate and adapt to today’s harsher, more challenging post-climate world.
The companies featured in this article are Clir and SINAI Technologies.
Everyone knows that one big downside of renewable energy (RE) generation is intermittency.
While the grid can cushion some of the ill-effects of intermittency through large-scale battery installations, varying production levels inevitably add uncertainty to the operation of RE facilities.
Uncertainty has negative consequences for plant operators and grid managers (who must instantaneously match power supply to power demand), but it also has negative consequences for financiers of renewable energy projects.
Owners are hesitant to take on leverage (i.e., borrow money) which would otherwise improve equity returns. Banks tend to charge higher interest rates and insurers higher premiums to make up for added operating risk. M&A deal flow slows because potential RE asset acquirers are not sure how stable the facility’s cash flows will be.
One start-up, Clir, has been using innovations in data science and artificial intelligence to understand operational drivers at renewable energy plants, then leveraging that understanding to improve plant efficiency and decrease uncertainty. By doing so, Clir can bring down the cost of capital for clean electricity generators — making RE facilities more attractive investment assets.
Clir consolidates data from all the units in a wind farm or solar array – a truly enormous amount of data – and runs that data through machine learning algorithms to get a picture of how the facility operates over time and in different environmental conditions.
Clir’s AI then identifies ways to maximize the overall efficiency of the facility in ways that CEO Gareth Brown says might sometimes seem counter intuitive.
For example, for offshore wind farms or those in the middle of large US deserts, there is not much mixing between air at different elevations. In these low-mix cases, efficiency for the farm overall increases when the leading turbines are set to operate at less than peak efficiency.
The wind left un-churned by the leading, wake-creating turbines hits the blades of the trailing, wake-affected turbines with greater force, generating more power. The power generated by the wake-affected turbines more than offsets the reduction in power from the wake-creating ones. According to academic research, this process, known as “wake steering,” can increase the power generated by around 10% and, more importantly, decreases the variability of the power generated by around 70%.
A 70% decrease of uncertainty represents a big win for asset owners and the financiers that back them. With operational uncertainty decreased, banks and insurers can better assess the potential risks and returns, and price their financial products more appropriately. Investors looking to acquire renewable energy assets also have a better idea what a fair price to pay is.
The main premise behind this column is that — insofar as it represents the economic manifestation of humanity’s ability to adapt — capitalism is an irreplaceable tool for fighting climate change. Capitalism routes money toward the most successful ideas, so to the extent that Clir’s AI is helping investors make sound capital allocation decisions, it is at the tip of the spear in civilization’s transition to a renewable energy future.
For years, company managers had to concern themselves with a single, straightforward task: maximize profits for the company’s owners. While not simple, this task was made a lot less tricky because governments did not know they should be charging companies for spewing greenhouse gases (GHG) into the atmosphere.
Governments finally began to announce and implement carbon taxes over the last decade, but there is still no universal solution. Different country-level or regional carbon tax schemes force some companies in some industries to pay for some GHG emissions; coverage is spotty, and enforcement differs from one jurisdiction to another.
With recent announcements about border adjustment taxes in both the US and Europe, it looks like the two largest trading blocks are finally on the verge of forcing companies to “internalize” the costs associated with GHG waste.
While carbon border taxes would be undiluted good news for enthusiasts of life as we know it, the imposition of these taxes is forcing companies to completely rethink their capital spending and operational planning. Large companies are moving toward instituting internal carbon pricing (e.g., Microsoft, Danone), by which divisional and firm-wide profits are adjusted by an assumed cost of GHG emissions.
Corporate planning centered on internal carbon pricing is not a trivial task. Corporate accounting and planning systems were not set up to measure or report these costs, so just gathering the data is challenging and highly manual. Companies spend big bucks on armies of consultants to pull together ad hoc spreadsheets to try to gather the required data so that simple go / no-go kinds of decisions can be made.
One start-up – San Francisco-based SINAI Technologies, founded by Maria Fujihara (CEO) and Alain Rodriguez (CTO) – is aiming to change these manual processes and bring the field of de-carbonization into the 21st century.
SINAI designs automation routines to compile emissions data from different corporate divisions into a common data store, so that the firm’s overall emission profile can be accurately assessed.
In addition to internal company data, SINAI gathers regional electrical generation data and information from suppliers to make estimates of a company’s baseline Scope I, II, and II GHG emissions.* This allows SINAI’s clients a holistic view of their overall GHG footprint and insight into what part of the supply chain needs the most mitigation work.
After data is collected and the baseline created, SINAI’s systems use artificial intelligence to forecast emission levels under different mitigation scenarios. Decarbonization-related capital spending plans can be made by comparing the mitigation effects of different technology implementations along different parts of the supply chain.
The planning process made possible by SINAI’s technology allows companies to make intelligent strategic decisions regarding what decarbonization projects they should focus on to get the biggest bang for the buck. Leveraging tools like this is vital if companies are to make the enormous changes necessary to adapt to our post-warming world.
Clir’s Brown and SINAI’s Fujihara and Rodriguez know, as I know, that in order to meet the challenges of the 21st century, we need to pull out all the stops and use all the tools in our toolkit — including big data analysis and AI.
Intelligent investors take note.
This article originally appeared on forbes.com, to read the full article, click here.
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