Introduction
In today’s industrial world, buildings are responsible for approximately 40% of the total energy consumption and surprisingly Heating, ventilation, and air conditioning (HVAC) systems and lightening systems take more than half of this consumed energy. Plus, now HVAC systems have become an inevitable part of all new buildings and offices. And that means even more contribution to higher energy consumption by buildings.
The efficiency of the HVAC systems during the design is considerably high 80%-90%. However, not all the installed HVACs are used at the nominal condition, which brings the efficiency down to 50%-70%.
Moreover, decay of the critical components brings the efficiency even more down. How can this be identified and optimized? Is there a smart solution that can learn the HVAC and correct the behavior of pumps, compressors, motors, etc.?
4i4 is proposing that by using N-CΛSTDS, HVAC systems can save up to 20% efficiency per year. What does this 20% mean in terms of energy and CO2 footprint?
Let’s take an example:
An office building with 1000m2 needs at least a 30 kW HVAC system. The basic assumption is that the efficiency is 70% which means it produces 48 tons of CO2/year. Using N-CΛSTDS reduces the footprint to 35 tons of CO2/year.
How
The good news is that in large facilities, the equipment controlled by building automation systems (BAS) frequently includes HVAC, lighting, fire alarm, and access/security systems. In Fig. 1 the central BAS (CBAS) is shown. There is a bidirectional communication between HVAC and CBAS and there is a good infrastructure already built for data-collection.
4i4’s software as N-CΛSTDS will be communicating with the CBAS and uses the collected data to optimize the energy consumption of the HVAC system as shown in fig. 2.
