Power shift: AI’s role in driving the transition to clean energy

By Sreedhar Sistu

 Sreedhar Sistu is the Vice-President of AI Customer Offers at Schneider Electric.

With extreme weather, resource shortages and rising greenhouse gas emissions threatening to destabilize the global ecosystem, the time to address climate change was yesterday. But it's never too late to take action.

Canada, like much of the world, faces increasing pressure to transition away from carbon-intensive energy sources toward renewable and sustainable solutions. As the energy transition takes precedence, artificial intelligence is one of the most powerful resources that can drive this shift. 

While AI cannot solve sustainability challenges alone, its ability to enhance decision-making, optimize energy use and scale renewable solutions makes it indispensable in driving meaningful change. By utilizing AI, Canada’s research and innovation ecosystem can lead global sustainability initiatives, while optimizing national energy consumption and supporting government policy on decarbonization.

AI serves as a critical enabler to accelerate sustainability initiatives in the energy sector. As we face an increasing demand for energy, alongside the need to decarbonize and reduce reliance on fossil fuels, AI-powered solutions offer efficient, scalable pathways to transition to clean energy.

By leveraging data, algorithms and machine learning techniques, AI can help organizations reduce their carbon footprint in three primary ways:

1. Optimizing energy use

AI is fundamentally transforming the way we monitor and manage energy usage, especially within sectors where high energy consumption is a constant, such as manufacturing, large-scale industrial processes and powering where we eat, work and play. 

Traditional energy management practices have often depended on manual monitoring, extensive training and highly specialized expertise to spot inefficiencies and manage loads effectively.

However, AI introduces a new level of precision and adaptability. By analyzing data from various sensors, AI can create predictive models that detect anomalies in energy consumption patterns, enabling proactive responses to inefficiencies and suggesting tailored optimization strategies that improve both operational costs and environmental impacts.

An example of energy optimization potential is provided by home energy management systems (HEMS). An AI feature in HEMS allows homeowners to reduce electricity costs by enrolling high-energy devices like electric vehicle chargers or resistive water boilers. The Al creates smart schedules for enrolled devices and limits their usage when electricity prices are high and clean energy access is limited, while following the user’s habits.

If we look at a more complex example that illustrates the relation between the resiliency of grid infrastructure and energy optimization scenarios, Consolidated Edison achieved great results when introducing underground fault detection.  Consolidated Edison operates one of the world’s largest energy delivery systems, providing energy for the 10 million people in New York. 

Due to the number of people who  rely on this system, rapid fault identification is essential to prevent outages that could disrupt critical infrastructure. During peak demand periods, like hot summer months, underground faults pose a particularly challenging issue. Delays in locating these faults can lead to the activation of diesel backup generators, which emit more pollutants and increase the system's carbon footprint.

Historically, fault location relied on physics-based calculations – which are accurate for single-phase faults, but less effective for multi-phase faults common in medium-voltage feeder cables (13, 27, and 33 kilovolt systems).

To address this challenge, an AI-based solution was developed and tested across a representative sample of substations. These substations were selected to cover a range of characteristics, ensuring the solution could adapt to different operational contexts and fault types.

By leveraging machine learning models, the system could more accurately predict both single-phase and multi-phase faults' locations. This improved precision is expected to save up to 570 hours annually in pinpointing faults, reducing the need for high-potential tests, which are costly and time-intensive.

Additionally, the AI model minimizes recalculations for scaling factors, saving an estimated 380 hours annually for recalibration alone.

The integration of AI in fault detection not only reduces operational overhead but also contributes to more sustainable energy delivery by limiting reliance on polluting backup power sources.

2. Optimizing energy demand and mix

Managing energy demand and ensuring the optimal use of renewable energy is another area where AI excels.

During peak demand, many energy grids rely on carbon-intensive sources like coal and natural gas. AI-based optimization models can forecast energy needs and manage energy storage in a way that minimizes the use of fossil fuels during these peaks. 

By shifting energy consumption to off-peak times, when renewable energy is more abundant, AI reduces reliance on carbon-heavy sources, contributing to both cost savings and emission reductions.

This concept extends beyond businesses to encompass individual homes and buildings. In homes equipped with renewable energy technologies, such as solar panels, AI can forecast both energy production and consumption patterns. This enables homeowners to utilize stored energy during high-demand times, reducing reliance on the grid and balancing supply with demand more effectively.

This technology also supports demand-response programs, where users can voluntarily reduce energy usage during peak periods in exchange for incentives. Such programs play an essential role in regions with abundant renewable resources, like Canada, where high energy demand often coincides with times when renewable generation is limited.

By shifting demand patterns, AI can make renewable energy a more viable source during these periods, contributing to reduced emissions and a more sustainable energy landscape.

3. Breaking down barriers to sustainability at scale

While renewable energy technologies are quickly advancing, many organizations and governments still face barriers to adopting them at scale, including high costs and the complexity of integrating these solutions into existing infrastructure.

AI can play a new role in overcoming these hurdles by automating processes, reducing costs and simplifying the implementation of renewable energy solutions.

In particular, expanding access to home EV charging is essential for advancing clean energy adoption among prosumers, and AI provides the key to making this expansion more efficient and accessible. Through predictive analysis and streamlined installation assessments, AI simplifies the complexities of integrating EV charging at home, accelerating the shift to sustainable energy use.

An example is Schneider Electric’s collaboration with strategic partner Qmerit and its certified electrician network. To streamline the EV charging installation process, an AI-enabled tool was developed to assess home electrical panels quickly and accurately. By analyzing a single photo of the panel, the tool evaluates key factors such as available circuit space, tandem breakers and overall electrical capacity, helping both homeowners and electricians determine whether the panel is suitable for EV charger installation.

This tool is part of a broader AI solution that includes calculating the available capacity for EV charging based on the panel data.

This approach simplifies the often complex process of electrical upgrades, reducing costs and increasing convenience for homeowners. Additionally, it improves the accuracy of cost estimates, supporting higher rates of EV adoption by making installations more accessible and efficient for both consumers and installers.

In Canada, where policies increasingly support the adoption of EVs and green infrastructure, AI will be crucial in helping the country scale sustainable energy technologies across industries. Whether through automation or data-driven decision-making, AI removes friction and accelerates the deployment of these vital technologies.

Addressing policy and investment in Canada’s innovation ecosystem

The Canadian government has committed to achieving net-zero emissions by 2050, with key investments in clean technology, infrastructure and research. However, AI’s role in these plans is still evolving. 

To make the most of AI’s potential, it’s critical for Canada’s research and innovation ecosystem to invest in foundational technologies, such as automation, data infrastructure and renewable energy systems. AI can be the missing link that helps maximize the effectiveness of these technologies, especially in areas like energy optimization and climate resilience.

Government policies supporting AI-driven clean technologies, along with strategic investment in AI research, are essential to scaling these solutions across industries. AI has the potential to turn sustainability from a high-cost ambition into a practical, scalable solution for both businesses and individual consumers.

To meet global and national climate goals, AI must be embraced as a core component of energy transition strategies. It’s not just about adopting AI, but about using AI responsibly and thoughtfully. Governments, businesses, and industries need to work together to scale AI’s potential to optimize energy use, integrate renewable solutions and help meet sustainability targets.

AI offers the ability to automate complex processes, deliver faster results and provide more precise insights than ever before. By leveraging these strengths, we can address the urgent need to reduce our carbon footprint and transition to a more sustainable future.

It’s time to use the power of AI to drive the energy transition forward, ensuring a more resilient and sustainable planet for future generations.

R$