Heating, Ventilation and Air Conditioning (HVAC) Systems, the Next Building Load to Optimize

Heating, ventilation, and air conditioning (HVAC) systems are a building’s main energy consumer, accounting for 40% of all electricity used. Measures to update and replace existing heating and cooling systems could improve the energy efficiency of these systems by around 43%. Unfortunately, the cost and payback on energy savings from hardware upgrades/replacements are often not attractive enough for building owners. With the advent of low-cost IoT solutions, software is becoming an increasingly popular option to modernize monitoring and analyzing systems because they can control and optimize in real-time with little or no human intervention. With a current growth rate of 19.1%, the smart HVAC control market is predicted to reach $28.3 billion, up from $8.3 billion last year. The target markets are large commercial enterprise buildings, with the small/medium building market experiencing accelerated growth with the advent of business model innovation and new methods of project financing.

US energy use in the building sector
Figure 1 shows the US building sector energy consumption, courtesy of Sunproject

HVAC, the next load to be fully optimized

Over the last decade companies in the commercial building market have capitalized on energy efficiency’s opportunities using light emitting diode (LED) lighting, replacing more intensive systems and using sensors for lighting control. However today those opportunities, as well as the government incentives, have been mostly exhausted. As a result, HVAC systems are being looked at as the next building load to be fully optimized. Once an area lacking innovation, HVAC software is drawing on from adjacent markets (such as digital twins in industrial plants), leveraging easily deployable, low-cost IoT analytics. As more assets in buildings are becoming connected, capabilities that were previously only obtainable via hardware control can now be delivered via software automation.

What is closed-loop control?

As mentioned in my previous article on industrial analytics, closed-loop optimization is delivered via prescriptive analytics, where assets “self-diagnose”.  In automated HVAC control, prescriptive closed-loop automation controls airflow by dynamically predicting cooling requirements in buildings, before automating control to drive a steady-state. Advanced analytical capabilities produce predictive models for areas such as system reliability and energy capacity, so that the system can achieve optimized cooling management, reducing energy usage by up to 30%. Innovators including Encycle, Vigilent, Enerbrain, MiniBEMS and Leanheat are all activity using some level of dynamic optimization for heating and cooling. Automated optimization delivers value without burdening staff resources, whilst also lowering the need for staff to be trained in the latest software.

closed loop automation system for heating efficiency.
Figure 2 shows Vigilent’s closed loop automation system, courtesy of Vigilent,

Executive VP Chris Hensley at Encycle, a company active in HVAC control since 2006, recently decided to pivot from hardware to software after seeing the accelerated opportunities of cloud-based technologies and the development of machine learning techniques.


As countries drive energy efficiency regulations and targets out to 2050, like California’s building efficiency targets and the UK’s renewed 2050 goals, software-enabled HVAC optimization could play a key role in the decarbonization pathway.


Business Model Innovation

Start-ups are engaging with original equipment manufacturers (OEMs) through channel partnerships and platform integration. Instead of competing with the disruptors, incumbents including Siemens, ABB, Honeywell and Schneider Electric are viewing their cloud-platforms as marketplaces, where functionality is delivered by third parties with core domain expertise.  Encycle has integrated with Honeywell and Ecoplant with ABB. Through these partnerships, the start-ups gain exposure to global sales channels, and may ultimately be acquired by the OEM. For example, Powerhouse was recently acquired by Middlby and Encycle partnered with Carrier and Lightstat.

Whilst both partnership models work well for customers who are already paying for, or, have existing resources to utilize advanced analytics, many customers do not have the balance sheet, vendor sophistication or expertise. As a result, some start-ups such as Equota, Faunder and Barghest Building Performance have taken the managed-service business model, whereby a team will be brought in to upgrade, monitor and control all building assets. Some innovators such as Carbon Lighthouse for example, offer an efficiency PPA to reduce upfront project expenses and will only start taking payment via the longer-term efficiency savings. Combining automated optimization solutions with a managed service finance model provides a pathway for rapid energy savings at zero cost to the building owner. This approach can also open widows to the small-medium building market, where energy savings are lower down the priority ladder.

Corporates and start-ups go hand in hand

The building management market is fragmented with a range of turnkey players offering full building control down to players with specific domain expertise. While some broader players have seen success (See SkyCentrics and 75F) it is difficult for anyone to be best in class when it comes to optimizing all assets in all building types. As a result, innovators who have a strong core IP are finding opportunities within one layer in the value stack, alongside other ecosystem participants. Direct competitors in the market include in-house research and development teams of HVAC corporates, but ultimately their success lies in how they can adapt to new control algorithms in the market.

Keep an eye on:

  • increasing market penetration in small and medium buildings as software integration becomes easier and outdated equipment becomes easier to utilize
  • mesh network algorithms which can connect to, model and dynamically control very high volumes of assets utilizing millions of sensor inputs
  • multi megabyte control software which can run on edge computing units, rather than centralized cloud control systems
  • sector winners who will be decided based on the strength of their industry partnerships, as well as the strength of their AI optimization algorithm, and its respective ability to identify further cost savings.

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