In recent years, we have seen a rise in new technologies aimed specifically at improving the energy efficiency of homes and buildings. Reducing energy usage is often a win-win for everyone involved. What if quantum could step in and make homes even more energy efficient? Let’s consider some of the research out of Hanbat National University, South Korea, which targets quantum reinforcement learning of HVAC (heating, ventilation, and air conditioning) systems).
Last year, researchers at the university presented a demonstration of continuous-variable, quantum-enhance reinforcement learning for resident HVAC and home power management. The technology integrates features such as multi-zone cooling and clustering in order to group similar data points and adjust cooling.
The researchers performed simulations on real world data from 26 residential households during a three-month period and discovered the quantum reinforcement learning HVAC control outperforms deep deterministic policy gradient method as well as proximal policy optimization algorithm, while maintaining thermal comfort.
The study associated with the research shows that quantum reinforcement learning can revolutionize residential HVAC. The research suggests quantum reinforcement learning can cut power use and electricity costs by more than 60% while maximizing comfort. Additionally, it can achieve 63% lower power consumption and 64% lower electricity costs than current methods.
Imagine the opportunities for homeowners, and potentially even business owners, microgrids, and power plants. The technology could provide zone-level comfort control, realtime energy optimization, and CO₂ management.
The research also suggests it could work in retrofits, as the technology works with standard HVAC equipment and sensors. Additionally, it has a generalizable framework that could be extended from apartments to small buildings and microgrids.
This is certainly something to keep an eye on in the years ahead. As technology continues to mature, the research suggests quantum-accelerated policy search could facilitate faster training for complex multi-energy systems such as HVAC, electric vehicles, and energy storage systems. In the long term, this work is expected to guide the path toward standardized secure controllers that can be certified and deployed at scale.
Here’s the real question: Could this research pave the way for quantum-powered smart homes in the near future? Imagine the opportunities to optimize energy bills, comfort, and carbon emissions. We know the future is green—and this is certainly one way to get there.
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