Energy management is evolving from simple conservation tactics to smarter, physics-aware systems. Physics-Informed Neural Networks (PINNs) bring the laws of nature into AI, helping buildings optimise energy use with accuracy, efficiency, and sustainability in mind.

Executive Summary 

For decades, energy has been treated as a commodity managed with blunt tools: turn the lights off, lower the thermostat, and add more solar panels. Today, a quiet revolution is happening. Physics-Informed Neural Networks (PINNs) are helping us teach machines not just to crunch numbers, but to obey the physical laws that govern our world. When paired with digital twins, this approach is reshaping how smart buildings consume energy. It’s not just efficiency. It’s the foundation of a sustainable, intelligent energy future. 

The Blind Spot in AI 

Artificial intelligence has dazzled us with its ability to recognise faces and translate languages. It has distilled the world’s knowledge into a chat interface we can query like a five-year-old. But in critical industries like energy, traditional AI still stumbles. This is because most algorithms continue to treat the world as a binary of inputs and outputs, far removed from the physical laws that govern us. That is the difference between broad, general-purpose AI and applied systems built for specific domains like energy. 

(See: General vs Applied AI: Why Refineries Need Specialist Intelligence) 

Enter Physics-Informed Neural Networks 

PINNs are a different breed. Instead of learning purely from historical data, they embed the laws of physics, be it thermodynamics, electricity flow or conservation of energy, directly into the training process. Think of it this way: you’re not just telling a child to memorise math problems; you’re teaching them why math works. 

In the context of buildings, PINNs help systems understand not just how energy was used yesterday, but how energy must behave under natural constraints tomorrow. When combined with digital twins, aka virtual replicas of physical systems, you suddenly get something remarkably efficient: a simulation engine that is both intelligent and faithful to reality. 

Buildings That Think in Real Time 

Imagine your office tower as a living, breathing organism. Its digital twin is constantly fed by smart meters, IoT sensors, and renewable energy data. A reinforcement learning agent (RLA), trained on data from this digital replica, learns how to schedule heating, cooling, and appliance use with almost surgical precision. 

In one recent study published by Cornell University, when Physics-Informed Neural Networks were embedded into such a model, the results were striking. Energy use was predicted with over 97% precision, with an error margin so small it was practically negligible. More importantly, this wasn’t just an academic thesis. The PINN-embedded model cut building energy costs by 35% and raised renewable energy utilisation to 40%. 

From Data Centres to Living Rooms 

This is not the first instance reinforcement learning has bent the cost curve in energy. Google DeepMind used machine learning algorithms to cut its data centre cooling bills by 40%. What the Cornell publication demonstrates is that these breakthroughs are not just constrained to hyperscale facilities, but to everyday homes and offices as well. 

This democratisation matters. In the United States, household appliances alone account for nearly 30% of energy consumption in the residential building sector. Scaling smarter systems across millions of homes could do more for climate goals than any single policy change. But here’s the nuance: applying general-purpose AI isn’t enough. You need applied intelligence. You need systems tailored to the physical laws of energy grids and renewable variability.

Physics-Informed Neural Network models cut building energy costs by 35% and raised renewable energy utilisation to 40%, unlocking substantial financial and sustainability gains

Why This Matters for the Next Decade 

Every leap in managing energy consumption carries compounding effects: lower costs, cleaner air, and more resilient grids. By fusing physics-grounded AI with digital twins, the focus shifts from simply optimising kilowatts to building a playbook for sustainable growth. 

The transition will not be without challenges. Legacy grids, cybersecurity risks, and upfront costs introduce friction. The positive is there are several trends shaping the energy industry that signal momentum, particularly as global energy systems evolve toward smarter, more adaptive grids. 

A Physics-AI Future 

PINNs represent a shift toward intelligence that is grounded in the natural order of the world. When buildings can learn the rules of physics, they stop being passive consumers of energy, and instead become active participants in balancing costs and sustainability. The future of energy isn’t about doing more with less; it’s about doing better with what we already have.  

 Yet PINNs are just one approach within the broader landscape of physics-informed AI. Orbital, a frontier model purpose built for energy operations, has outperformed PINNs on key benchmarks, delivering not just greater accuracy, but also the scalability and efficiency required in real-world deployment. 

Physics-grounded intelligence offers the roadmap to a smarter, cleaner and more resilient energy future, where every watt saved, every dollar cut, and every tonne of carbon avoided becomes the compounding interest of a more sustainable world.