At a time when responsible usage of AI is top of mind, do we spend enough time discussing AI applications that can truly live up to the hype? There is no Planet B, says street banners and GenZ t-shirts, but can we act fast enough to fight climate change and reset our future?
While the answers to these questions are not obvious, there is hope and tangible proof that AI can deliver to help us slash CO2 emissions.
The good news is that the technology to decarbonize human activity isn’t science-fiction or something that is far off in the future. There are technologies that already exist today that provide the tools necessary to make companies more energy efficient and help achieve net-zero goals.
By combining existing solutions with the transformative power of AI, businesses can accelerate their journey toward sustainability and make significant strides in combating climate change. Here are a few examples of how we can use AI to decrease our carbon footprint.
AI's ability to optimize energy consumption in buildings and industrial processes is one of its most impactful applications in the fight against global warming. Traditionally, optimizing these processes required physics-based modeling that takes significant expertise and sometimes is not even feasible. AI has changed this by rapidly analyzing data, creating models learning from data that can be used to optimize processes, detect inefficiencies and suggest energy-saving scenarios.
AI-driven platforms can optimize energy use in real-time, ensuring that buildings and industrial operations function as efficiently as possible. This not only reduces energy consumption but also significantly lowers carbon emissions, as the cleanest energy is the one we don’t use at all. AI's ability to model and optimize complex systems is particularly valuable in industries with high energy demands, such as manufacturing plants, data centers, and large-scale commercial buildings. By identifying inefficiencies and providing actionable insights, AI enables businesses to make data-driven decisions that can make long-lasting impact.
AI's capabilities extend beyond optimizing specific processes; AI also plays a crucial role in managing overall energy demand, particularly during peak periods. Peak demand often results in the increased use of fossil fuels as electricity grids struggle to meet heightened demand. AI-based optimization techniques allow businesses and consumers to manage their energy consumption more effectively, reducing strain on the grid and minimizing reliance on non-renewable energy sources.
This is put into practice in two ways:
1. Energy Storage Management: AI optimizes the use of energy storage systems, ensuring stored energy is used efficiently and storage capacity is maximized.
2. Demand Response Programs: AI enables sophisticated demand response programs, where energy use is adjusted in response to real-time price signals or grid needs, helping to reduce costs and support grid stability.
AI-enabled energy management systems can predict energy needs and optimize the use of renewable energy, shifting consumption to off-peak times when renewable energy is more abundant. This helps reduce both energy costs and the overall carbon footprint. By intelligently managing the energy mix, AI helps balance the use of renewable and non-renewable energy sources, ensuring grid stability while minimizing carbon emissions.
As the energy landscape diversifies, distributed energy resources (DERs), such as solar panels, wind turbines, and energy storage systems, are becoming increasingly common. However, managing these resources efficiently poses new challenges, particularly in ensuring they are integrated into the grid in a way that maximizes both efficiency and sustainability. AI can help overcome these challenges by enabling the seamless integration of DERs into broader energy systems.
Prosumers—consumers who produce their own energy—rely on AI to manage their energy production and consumption effectively. AI-driven tools optimize the use of renewable energy resources, reducing dependence on non-renewable sources. AI also has an important role in enabling prosumers to achieve greater energy independence, reducing reliance on the grid in case of power blackout, and contributing to a more resilient energy system. The scalability of AI systems from managing energy in individual homes to overseeing large-scale renewable energy projects, makes them indispensable in the transition to a sustainable energy future.
The omnipresent Generative AI (GenAI) is not the first choice when looking for sustainability-driven solutions, due to its high energy demand and corresponding carbon footprint. When looking for cases where it can advance sustainability, Environmental, Social, and Governance (ESG) reporting and resource management comes as a good example. The complexity of sustainability data can be overwhelming, but the conversational capability offered by GenAI can simplify access to data and analytics and enable employees to access data quickly and efficiently.
A GenAI-enabled Copilot offered on top of one of our solutions allow managers to interact with complex data through natural language interfaces, making it easier to track, interpret, and report on sustainability metrics. This reduces the need for manual data analysis, freeing up time and resources that can be redirected toward implementing sustainability initiatives. By making sustainability management more accessible, this type of AI empowers organizations to stay ahead in an increasingly complex resource landscape.
AI is more than just a technological advancement; it is a catalyst for the energy transition that can help us answer some of today’s most pressing questions. By optimizing energy use, managing demand, and enabling the integration of renewable resources, AI is helping businesses take significant strides toward sustainability.
As the world grapples with the challenges of climate change, the combination of existing technologies and AI offers a clear path forward. The tools to make a substantial impact on our climate goals are already here and now is the time to put them to use.
Philippe Rambach is SVP and Chief AI Officer at Schneider Electric, driving AI innovation for efficiency and sustainability. With over 20 years of experience in strategy and business leadership, he previously led industrial automation. A graduate of École Polytechnique, he joined Schneider in 2010.
