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Harnessing AI to save energy and improve user comfort

With buildings widely recognised as a major source of energy consumption – the global real estate market consumes 60% of the world’s electricity, and emits 28% of global carbon emissions – Arloid says its ‘innovative’ Artificial Intelligence (AI) ‘enables any building to cut energy bills amid the global fuel crisis’.

With offices in London, Singapore, and Dubai, Arloid claims to have built the most powerful AI platform ‘to win the race to Net Zero for real estate’. The company said: “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 HVAC system performance, boosts energy efficiency – the most effective way for real-estate to cut carbon and reduce costs.”

The AI specialist explains that Arloid Automation uses ‘Deep Reinforcement Learning’ to automatically manage the operation of HVAC systems in a wide range of buildings – including hospitals and other healthcare facilities – via a secure Virtual Private Network (VPN). It said: “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’. Consequently, 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.”

Arloid emphasises that while energy savings are the most notable benefit of using AI to optimise building management systems, the technology 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’. It added: “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.”

The Arloid ‘solution’ to HVAC optimisation functions as follows:

  • Building modelling engineers create a Digital Twin of the building, including everything from construction materials to occupancy rate, pollution levels, historic local weather data, ‘and more’. The building model includes existing HVAC infrastructure locations, and is divided into micro thermal zones ‘for nuanced control’.
  • Once the Digital Twin is complete, the AI begins to learn. During this period, Arloid runs 300,000 iterations of a simulated year, enabling to gather live data on ‘the correct response to different conditions and occupancy levels’.
  • The training process provides the Arloid team with building performance insights that enable them to define the best settings for each microzone, reducing coolant, energy, and fuel consumption, minimising comfort index deviation, and aligning with carbon targets. The result, the company claims, is ‘energy savings of 30% in just 60 days, with zero upfront costs to building managers’.



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