Dive Brief:<\/p>\n
Energy use and electricity costs dropped more than 60% when the software powering smart HVAC systems used quantum computing, and the better results occurred without lowering indoor air quality, researchers said. Dive Insight:<\/p>\n Smart HVAC systems use data about occupancy patterns to help building operators reduce energy consumption by linking temperature settings to where people are. Other variables like weather and heat loads add complexity to the calculation. When data about indoor air quality gets added to the mix, the complexity increases further.\u00a0\u00a0\u00a0<\/p>\n \u201cAdvanced HVAC control methods \u2026 struggle with the complexity of real world building dynamics,\u201d the researchers said.\u00a0<\/p>\n Developers of traditional smart systems have had some success switching from a rules-based computing approach to one that leverages machine learning, the researchers said. They\u2019ve found that ML is better at adapting to real-world conditions, but quantum processing can improve the systems even more.<\/p>\n \u201cQuantum reinforcement learning \u2026 leverages quantum computing<\/a> principles to handle high dimensional control problems more efficiently than conventional approaches,\u201d they said.\u00a0<\/p>\n Absent the quantum component, ML based systems can take time learning how to optimize the system, resulting in start-up periods in which conditions can be uncomfortable for occupants.\u00a0<\/p>\n
\nIn quantum computing, processors analyze data on a simultaneous basis, so more complex data can be processed faster than with conventional, linear processing. When simultaneous processing is applied to smart systems, the software that controls HVAC temperatures in different parts of a building can make decisions more quickly and more accurately based on an analysis of complex data, said the researchers at Hanbat National University in South Korea.
\n\u201cQuantum reinforcement learning [can] handle high dimensional control problems more efficiently than conventional approaches,\u201d the researchers said in their report, published in the journal<\/a> Energy and AI. <\/p>\n