Against a backdrop of climate emergency and soaring energy prices, Arloid Automation provides smart technology that can enable any building management system to produce substantial energy savings. Through efficient optimisation of heating, ventilation, and air conditioning (HVAC) system performance, boosts energy efficiency – the most effective way for real-estate to cut carbon and reduce costs.

Buildings are widely recognised as one of the major sources of energy consumption, with the global real estate market consuming 60% of the world’s electricity and emitting 28% of global carbon emissions. Fortunately, whilst the spring budget announcements fail to do more than scratch the surface of the crisis, innovative technology can provide the solution.

Arloid Automation uses Deep Reinforcement Learning to automatically manage the operation of HVAC systems in a wide range of buildings via a secure Virtual Private Network (VPN). The innovative AI makes decisions based on reinforced behaviour and real-time data to provide faster optimisation and better HVAC performance. By controlling each HVAC device in the system and dividing the building into distinct heating and cooling microzones, provides more nuanced control of the environment and better user comfort. As a result, the technology is achieving up to 30% energy savings across over 23 million square feet. Buildings all over the world from warehouses to retail premises to hotels to medical centres are realising the potential of machine learning to drive the decarbonisation of the built environment, and reduce operational costs.

The benefits of using AI to optimise building management systems are numerous. In the current climate, energy savings are the most notable, but AI can also proactively ensure better user comfort, provide nuanced thermal conditions for sensitive buildings like hospitals and logistics centres, and help businesses achieve their carbon targets. Buildings vary considerably in structure, complexity, and usage, and time is always at a premium – that means manual scheduling and manually collected data is always going to be insufficient to the task. AI trained using Deep Reinforcement Learning can process live data in real time, continuously monitoring and proactively adjusting systems to maintain the optimum settings – without the need for time-consuming external input, and quicker than other forms of machine learning.