Artificial intelligence (AI) is continuing to develop rapidly, and there are concerns about its environmental footprint. According to a recent WTW report, the growth of data centres harms the sustainability goals of technology firms such as Google, Amazon or Microsoft. These centres currently consume 6% of all electricity in the US and are expected to double by 2026.
However, according to Exergio, a company that develops AI-based tools for energy optimisation in commercial buildings, AI doesn’t only waste energy, and AI-powered tools can already be used to do the opposite and cut energy waste.
“The common narrative suggests that AI’s growth contradicts the urgent need to reduce greenhouse emissions,” said Donatas Karčiauskas, CEO of Exergio. “However, this overlooks the fact that certain AI tools are already helping multiple industries cut energy waste. And not just more affordably, but also faster and more precisely than other existing traditional methods such as rule-based automation, manual energy audits, or reactive maintenance. Big tech has to simply utilise it.”
According to Karčiauskas, the buildings sector can be used as an example. It is a major energy consumer, responsible for approximately 40% of global energy usage and 33% of greenhouse gas emissions. Inefficiencies in heating, ventilation, and air conditioning (HVAC) systems cause buildings to consume more energy.
Existing AI solutions, however, are able to rearrange the concept of how energy can be managed in properties. They analyse real-time data and then optimise HVAC operations, lighting, and other energy-intensive systems.
For instance, AI can predict occupancy patterns, and adjust heating and cooling systems accordingly.
“It’s a common misconception that AI just wastes – this technology, in such cases, also ensures that energy is used only where and when it’s needed. Numbers-wise, it depends on the project, but it could reach up to 29%,” added Karčiauskas.
Focus for AI potential can be expanded from individual buildings to entire urban areas. Currently, similar technologies are being used in the development of smart grids and cities to optimise energy consumption. A smart grid is an electricity supply network that uses digital communications technology to detect and react to local changes in usage.
Smart grids primarily integrate AI to upgrade energy distribution. A study published in the International Journal of Communications, Network and System Sciences showed that smart grid implementation manages to reduce electricity transmission losses by up to 20%.
After analysis of historical consumption patterns, AI systems can predict fluctuations in energy demand, and allow utilities to adjust power flows dynamically – a process similar to what happens in buildings. Beyond grids, AI is also used in weather monitoring, which is then integrated into building systems.
“In the past, we’ve been used to reactive decisions. The industry is shifting towards learning, analysing, and predicting now,” continued Karčiauskas. “In some cities, weather analysis can inform building owners on how to better anticipate energy demand, mitigate extreme weather impacts, and optimise renewable energy use. As AI models become more sophisticated, we’ll see even greater precision in how we balance sustainability with energy efficiency.”
Integrating AI-driven weather prediction into building management systems directly improves energy efficiency. This way, AI facilitates proactive adjustments to HVAC operations. According to a Nature study, adopting AI technology at scale and combining it with existing energy policies and low-carbon power generation could lead to 90% lower carbon emissions.
According to energy experts, scaling such technologies will be crucial to make an impact. Here, robotics are being utilised as a technology that could scale AI-based tools and apply them in different environments.
“Even though AI-powered robotics are mostly used for domestic tasks and mundane jobs that are dangerous for humans, in the future they will be trained to achieve sustainability by automating tasks, especially in buildings,” said Karčiauskas. “Imagine if an HVAC sensor gets faulty–a robot gets notified, moved to a specific location, and gets to fix the problem in the end.”
While AI-powered robotics have already transformed industries like manufacturing and logistics, their role in the building sector’s energy management is still in its early stages.
“We’ve started training a humanoid robot that will be based on the NVIDIA GR00T system as well. This only feels natural. After years of collecting energy consumption data in the buildings industry, and learning how to utilise it, we see how AI-based technologies can help us take it to the next level,” concluded Karčiauskas.
There’s plenty of other editorial on our sister site, Electronic Specifier! Or you can always join in the conversation by commenting below or visiting our LinkedIn page.
