How AI Can Reshape The Post-Subsidy Renewable Energy Market

Erstellt von Forbes |

With the status of current wind and solar subsidies in the U.S. unclear, the industry needs to brace for impact by making up for the increase in investment risk post-subsidies. How should investors prepare? A post-subsidy world means the renewable energy sector needs to successfully harness and utilize AI and smart data analytics to maximize investment returns.

Renewable Energy: The Problem And The Solution

According to a report by the International Energy Agency, carbon emissions hit a record high in 2018. A new record high is likely in 2019, too. Global energy demand is rising exponentially due to forces like globalization, industrialization and exploding populations. We can't yet offset the full impact of these massive forces with renewable energy, but with the right advancements and integrations of AI and increased investment in renewable energy, we can scale to meet the challenge.

Renewable energy is how we meet the energy challenges of the 21st century, but there are several factors that are holding the sector back from its full potential. Most notably: the weather. Unpredictable weather can mean that energy captured via solar or wind can fluctuate suddenly, resulting in a less reliable energy stream. But utilizing predictive intelligence can improve the efficacy, productivity, accuracy and reliability of weather forecasts we need to offset the whims of Mother Nature.

How AI Can Drive Renewable Energy Investments

Not only can AI improve the reliability of renewable energy streams, it's also creating smart systems that produce quantifiable and qualifiable results to secure the investments we need for a more sustainable future. 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 that through the use of real-time assessments on asset performance that can boost efficiency and predictability.

Investors need to see that they're making a sound investment with hard data and actionable conclusions. AI can identify patterns to better understand energy consumption, preparing us for a demand-side economic approach. AI can also recognize when there's an outlier in the data, helping spot and avoid inefficiencies. Additionally, when renewable energy is added to the primary grid, it can be challenging to stabilize the energy flow, but smart systems can help identify and fix distribution issues.

What The Post-Subsidy Landscape Will Look Like

In the renewable energy space, uncertainty in the market is posing a real challenge to developers and owners, resulting in an investor confidence gap. Wind and solar subsidies have historically bolstered investor confidence. However, now that renewable energy has achieved parity with traditional fossil fuel sources, many players in the industry — and on the Hill — are ready to see the subsidies subside.

Once the subsidies are gone, the value of AI integration in renewable energy asset management can be fully seen. AI can help give investors confidence. Data is reliable; government tax incentives aren't. The actionable and clear takeaways that AI provides on asset performance is how investors can mitigate their risk.

The Department of Energy is on board, too. The DOE recently announced the opening of a new office dedicated to learning about and leveraging AI.

Moving From Data To Investment

Investors should be aware that the successful use of AI-backed asset management will not be achieved simply by implementing a digital project aggregating data in a cloud. The key is getting the information out of the collected data to generate added value to the investment itself. However, doing so demands expertise not only in data analytics but also in machines.

While considering an investment in renewables, asking to see its SCADA data for the past 12-24 months can provide valuable information for an investor. By getting this data set, the quality of the data will be proved prior to running analytics, which can give the investor insight into the technical status of the potential investment and help them make their investment decision.

It's also a good idea for investors, when considering a potential investment, to use discounted-cash-flow models, which incorporate the simulation of potential energy market prices based on machine learning models. If the internal rate of return still shows an appropriate level and the investment is done, data and machine learning algorithms should be used to permanently watch the machines working on the maximum.

Although the renewable energy sector is sure to face changes post-subsidies, it can position itself well by leaning into AI and using such technology to its advantage.


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