Guest blog: How can HVAC AI contribute to the real estate race to net zero?
As the impact of human activity continues to damage our earth, governing bodies are looking to promote mitigating methods. The race to Net Zero is on.
Real Estate and Net Zero
Buildings are one of the biggest contributors to carbon emissions – in fact, buildings produce almost a fifth of the world’s carbon emissions and consume around 40% of the world’s primary energy. Even worse, 30% of energy used in commercial real estate is wasted.
Ultimately, real estate needs to minimise this gargantuan energy use. By utilising advanced technologies buildings can reduce emissions by up to 90%. Real estate energy management, therefore, has many inefficiencies, and HVAC (heating, ventilation, and air conditioning) systems are the opportune starting point.
The Application of AI for Real Estate
AI technology offers a tremendous opportunity for HVAC optimisation for cost- and time-effectiveness. It is not enough to rely on a team of staff manually adjusting temperature settings; this has proven to be inefficient.
Traditionally, HVAC systems have been managed holistically, manually, and – evidently – uneconomically. Any building has various zones requiring different settings for comfort, as well as a plethora of internal and external parameters affecting HVAC controls – occupancy rate, footfall, architecture, weather, pollution and more. On any one day, a person can’t account for all these parameters, react accordingly, or design futureproof solutions; this is too much data for a human to process. So, AI helps.
Creating a Digital Twin, unique to every project, helps AI to study the building, detect patterns, simulate the building use, and predict future requirements. This sheer volume of data required for processing demands greater computational power than humans can deliver.
Then, the trained algorithm connects to the current BMS, to control existing HVAC technologies. Once deployed, HVAC control is micromanaged automatically by AI, decreasing energy spend, and carbon footprint. Futhermore, there is no interruption to daily routine operations.
Innovative AI algorithms deliver superior results simplifying the management through automatic adjustments of HVA systems. Through accounting for all parameters (climate, zonal use, and comfort index factors etc.), AI ensures HVAC efficiencies that maximise occupant comfort, but minimise energy use.
The solution for over-consuming, under-optimised real estate is clear: leverage the power of AI to win the race to Net Zero.
The Role of Deep Reinforcement Learning
Going further than Machine Learning (ML), Deep Reinforcement Learning (DRL) is a more efficient method of mathematical modelling. The sophisticated game-theory-approach of DRL builds on pattern finding and introduces an incentive-based system to reward constant improvement and finding better results. This additional complexity results in unparalleled accuracy and a significant reduction in algorithm training time, delivering better end results.
At Arloid Automation, we apply DRL to our algorithm with impressive effectiveness. From our pilots we found that our HVAC AI solution based on DRL resulted in:
- Up to 40% reduction in total energy and coolant/heating cost
- 30-40% decrease in carbon footprint
- 60% improvement in building occupant comfort