Scaling sustainable energy through intelligent systems

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In the fight against climate change, achieving a high return on carbon investment (carbon ROI) is essential. When applied thoughtfully, AI can achieve significant carbon reductions, often achieving a ratio of up to 1:500 with CO savings (avoiding 500 tonnes of CO2 emissions due to one tonne of CO2 being released by running the model). However, this possibility is only realized when AI is implemented at scale, aligns with business goals and optimized for energy efficiency.

My experience leading AI initiatives at Schneider Electric has shown that organizations can leverage this potential by focusing on three key pillars: energy efficiency, electrification and scalable adoption. Here’s how these pillars turn AI into a powerful tool for sustainability:

1. Prioritizing energy efficiency through AI

The first and most immediate effect of AI on decarbonisation is through energy efficiency by reducing energy use without sacrificing performance. The most sustainable energy is the ones that are not used. AI algorithms can optimize construction operations, manufacturing processes and industrial systems to reduce waste and maximize efficiency.

Schneider Electric used AI-based algorithms to optimize energy management for commercial buildings. One of our projects was to deploy machine learning to monitor and coordinate HVAC systems in real time. result? While an average-sized building reduces CO2 by about 2 tons a year, the AI system itself consumes approximately 0.003 tons of CO2 by an annual rate of 1:600 carbon ROI.

For large applications, such as optimizing university microgrids, AI can save 300-500 tonnes of CO2 each year, minimizing carbon investment. This 1:3000-1:5000 ratio shows the immeasurable potential of AI when used strategically for energy efficiency

2. Utilize decarbonization electrification

Moving away from fossil fuels is the cornerstone of achieving the net-zero goal, and AI plays a key dual role by optimizing the way green electrical energy is integrated, distributed and consumed throughout the system.
Renewable energy sources are inherently fluctuating, and managing this fluctuation requires intelligent systems that can predict energy demand and balanced loads. For example, AI-powered demand response programs reduce carbon-intensive grid stocks by optimizing energy usage during peak times and shifting energy consumption to periods where renewable energy is more available.

In addition to optimizing grid operations, AI can also help plan and deploy renewable infrastructure. One initiative in California will use AI to assess the feasibility of residential solar panel installations, significantly reducing the need for on-site assessments and promote the adoption of green energy

3. Scaling AI adoption to make a bigger impact

The third pillar is often overlooked: scalability. Even the most advanced AI solutions are ineffective if they remain isolated in pilot programs or small deployments. To overcome this, companies need to integrate AI into core operations, streamline their deployment processes, and promote a culture of continuous learning. This approach not only accelerates the adoption of green technology, but also ensures that AI offers concrete climate benefits at scale.

For example, moving towards more electrified words results in more electrical installations. To reduce ownership costs and maximize uptime, predictive maintenance systems use AI to identify potential equipment failures before they occur, reduce downtime and extend the life of machines that reduce both operating costs and carbon emissions.

Furthermore, aligning your AI projects with business goals from the start will increase your chances of successful recruitment. Our AI teams work directly with business units, from ideas to full deployment, to eliminate the common pitfalls of isolated proof-of-concept projects that never expand. This sensual collaboration ensures that AI solutions are not only technically sound, but are also practically relevant and scalable.

Avoid pitfalls: Choosing the right AI model

AI has the power to significantly reduce carbon footprint, but not all AI models are created equally. Choosing the most efficient model is important to balance the energy costs of the calculation with the achieved carbon savings.

For example, the state-of-the-art generation AI models could be resource-intensive, but a more streamlined model found to be sufficient for use cases and significantly lower carbon emissions. By carefully selecting the right model for each application, companies can maximize the carbon ROI of their AI initiatives.

The path ahead: Purpose-driven AI

Achieving high carbon ROI is not just about implementing AI, but responsibly. Companies need to focus on leveraging AI deployments to enable energy efficiency optimization, electrification support, and scalable adoption.

By adhering to these three pillars, organizations can turn their AI strategies from isolated experiments to global solutions that promote sustainable progress. As digital transformation continues to rebuild its industry, those who responsibly use AI not only work towards a net zero goal, but also urge others to follow suit.
Is it an important point? Deploying AI is not because new, but exciting, and serves a clear purpose, but because it reduces emissions and builds a sustainable future. With the right AI strategy, this conversion is not only possible, but is already within reach.