The Australian heating, ventilation and air conditioning (HVAC) sector is on the cusp of substantial growth, driven by a growing focus on energy-efficient systems, the incorporation of new technologies and advanced building materials, increasing consumer demand for comfort, and the impacts of the ever-warming climate.
HVAC systems used for indoor thermal comfort account for at least 40 per cent of energy consumed by buildings, so even small improvements in HVAC efficiency can have outsized impacts on a building’s overall energy consumption and sustainability.
Air conditioning (AC) is now regarded as an essential feature in both residential and commercial buildings, driving sustained demand growth. The sector is further supported by energy efficiency goals and the increasingly extreme Australian summer heat. Demand will be driven by rising populations, increasing income levels, urban development, and the impacts of climate change.
There will be a growing emphasis on air conditioning solutions designed to reduce energy consumption and building emissions. HVAC systems typically require a substantial upfront investment, along with installation costs that may include modifications to existing infrastructure, piping, electrical connections, and automation systems.
This is on top of the costs of running the system, making it vital for companies to implement energy-efficient cooling systems to minimise operational costs, particularly with high and fluctuating energy prices.
Integral to reducing HVAC energy consumption is waste heat and energy recovery, which can be leveraged through heat recovery equipment such as air-to-air heat exchangers and heat pipe heat exchangers.
More recent innovations to improve heat recovery and overall sustainability include liquid-to-air membrane energy exchangers, porous metal foam heat exchangers, nano fluid for heat transfer, phase change material as heat transfer media, membrane heat exchangers, and polymer heat exchangers.
Various HVAC technologies that utilise renewable energy can also be highly effective at reducing energy use, such as solar, geothermal and biomass heating and cooling, as well as desiccant HVAC and evaporative passive cooling.
Other energy-saving technologies developed for HVAC include phase change materials, night-time radiative cooling, desiccant dehumidification, and evaporative cooling.
The latter runs on water and uses up to 70 per cent less energy than air cooling, making it the most energy-efficient HVAC method available.
Recent innovations in evaporative cooling systems include corrosion-resistant heat exchangers, which limit corrosion and further reduce energy consumption, as well as the implementation of more efficient and quieter fans.
These heat exchangers utilise specialised alloy materials that inhibit the corrosion of cooling fluid and provide protection against mechanical damage, extending the system’s lifespan and minimising maintenance costs.
Recent advancements in heat transfer solutions for cooling applications have contributed to the development of more efficient systems. This includes the use of higher-efficiency materials for heat exchangers and the incorporation of thermoelectric heat pumps.
These pumps transfer heat between different locations, helping reduce energy consumption, and are environmentally friendly and cost-effective, as they operate without the need for additional power generation.
Furthermore, new cooling fans utilising DC motors have been developed, consuming less energy and producing less noise than conventional AC motors. Along with being cheaper to run, evaporative cooling systems are also more affordable to build overall and can create humidity, which is beneficial for dry environments.
MACHINE LEARNING AND PREDICTIVE CONTROL TO RESHAPE THERMAL DESIGN
Emerging technologies that are being utilised to create innovative systems include artificial intelligence, machine learning, and the Internet of Things (IoT), which maximise efficiency through smart sensors, variable speed drives, heat recovery systems, and sophisticated control algorithms.
Using highly accurate IoT-enabled sensors, HVAC systems can intelligently monitor spaces and air quality, then independently adjust thermal parameters through the application of automated andAI-based control systems.
Importantly, AI algorithms enable HVAC systems to ‘learn’ from environmental data and occupant behaviour to ensure it is operating optimally. Smart thermostats, for example, provide accurate temperature management that reduces energy waste by adjusting temperature settings depending on user preferences, occupancy patterns, and outside weather conditions.
A new study published in February by researchers from Latvia and Saudi Arabia sought to enhance the thermodynamic efficiency and demand response of a HVAC system in a simulated office building in Jeddah, Saudi Arabia, using machine learning (ML) and model predictive control (MPC) techniques.
The researchers selected support vector regression (SVR) and deep reinforcement learning (DRL) for their regression accuracy and adaptability in dynamic environments, while energy destruction analysis was used to assess thermodynamic efficiency.
Integrated with MPC, the models aimed to reduce exergy destruction and improve demand response, while the simulations evaluated room temperature prediction, HVAC energy optimisation, and energy cost reduction.
The results showed that deep reinforcement learning had superior prediction accuracy, reducing energy costs by 21.75 per cent while keeping indoor temperature increases minimal at 0.12 Kelvin.
The researchers explained that conventional HVAC control methods typically relied on fixed set points or reactive adjustments, which often failed to maintain optimal energy efficiency in responseto fluctuating environmental conditions.
They added that with recent advancements in computational power, ML approaches were gaining traction, offering predictive and adaptive control capabilities.
The researchers said: “Techniques like artificial neural networks, support vector machines, and reinforcement learning have been applied to HVAC systems, each providing unique benefits in
forecasting, energy demand response, and adaptive control.“These emerging methods enable HVAC systems to process real-time data, predict future states, and adjust their operation dynamically, ultimately leading to significant energy savings and enhanced environmental performance.
“The shift toward machine learning in HVAC optimisation reflects a broader trend toward automation and intelligent systems, aiming to address the limitations of traditional control strategies and respond more efficiently to fluctuating environmental and occupancy conditions.”
