In utility-scale solar, this means ARISTOTELES turns complex and unstructured technical, financial and meteorological data into actionable, real-time intelligence.
For O&M month here on the site, we reached out to Kaiserwetter CEO Hanno Schoklitsch to learn how artificial intelligence is being used to maximize solar plant profitability.
How can AI help solar investors and owners minimize risk and maximize profits?
There are real challenges in getting the $1 trillion per year in U.S. clean energy investment needed to limit global temperature rise to below 1.5 degrees Celsius and avoid the worst impacts of climate change. The good news is that advancements in AI and predictive analytics can catalyze investment in renewable energy and reduce greenhouse gas emission.
Harnessing and leveraging continuous, decentralized and unstructured data from a wide-range of renewable energy assets through IoT, our ARISTOTELES cloud-based platform processes technical data along with meteorological and financial data into structured, easily understood and actionable intelligence for our customers.
To minimize risk, we are using AI to predict asset failures before they occur. This is huge since asset failures can have very negative financial consequences, reducing profitability and cash flow dedicated to loan repayments.
Unpredictable weather can mean that energy captured via solar can fluctuate suddenly, resulting in a less reliable energy stream. But utilizing AI we are improving the efficacy, productivity and reliability needed to offset the whims of Mother Nature.
We are also using AI to model and simulate real power curves at solar parks. Prior to AI, this wasn’t possible. The real power curve gives our customers intelligence to push the performance of inverters and PV modules…this is a big advantage for maximizing returns. This innovation is invented by Kaiserwetter and brought to the market by us.
What are a few scenarios that show the various ways AI could impact solar development?
The renewable energy sector no longer needs subsidies to artificially prop it up. AI is ready to step in. Once the subsidies are gone, the value of AI integration in renewable energy asset management can be fully seen. AI can give investors and lending banks the confidence they need.
Data is reliable — government tax incentives aren’t. The actionable and clear takeaways that AI provides on asset performance is how tomorrow’s investors and lending banks can mitigate their risk and reap high reward.
The FT recently reported on a study that interviewed 45 chief investment officers, portfolio managers, and strategists about the investment challenges posed by AI and climate change, saying “two-thirds of the survey participants said developments in AI would ‘definitely’ rewrite the future of asset management globally.”
It’s no secret that production and distribution in the energy sector has been advancing at a glacial pace, and unfortunately, the same was becoming true for the renewable subsector, too. But AI, chiefly powered by machine learning, is changing the game. Now there’s ever-evolving, online assessments on asset performance that’s boosting efficiency, predictability and investors’ bottom lines.
Consider the case that institutional investors or a lending banks typically hold more than 100 single wind farms and solar parks and need more than hundred different reports each month. Even after hours and hours of reading, the portfolio management teams won’t be able to evaluate all this information, and this leads to problems. Our AI platform, our DI (Data Intelligence) platform can provide online insights on asset status and produce actionable intelligence for portfolio management teams of investors to maximize their profits and minimize their risks. Meanwhile the implementation of data intelligence leads to a much a higher organizational efficiency – less people can manage a much larger portfolio.
What are some ARTISTOTLES-specific features or updates that distinguish it in this area from similar systems?
One very important aspect is that ARISTOTELES is capable to provide insights into the status of assets not only from a technical point of view, but especially from a financial point of view by aggregating the financial data deriving from the main ledger of the SPV accounts. We are creating transparency on a monthly basis on EBT levels and also showing cash flows. For banks, we have implemented a special feature that predicts Debt Service Coverage Ratios (DSCR) to avoid loan defaults. This is one example of many functionalities.