Since I was little, I have always been passionate about nature and the impact we may have on it as a species. Energy is everywhere and is a significant part of that impact. First interested in renewable energies I slowly transitioned to the energy efficiency sector - after all, the cleanest energy is the one we do not consume! Heating, Ventilation and Air-Conditioning (HVAC) systems represent on average 40% of a building consumption - totalling to 16% of worldwide consumed electricity! That seemed like a good place to start!

Trained as an IT and automation engineer from the school of water, energy and environment, it was a natural step for me to onboard a company like BeeBryte who uses a mix of IoT, automation and data science to achieve energy reductions.

Indeed, BeeBryte improves buildings’ energy efficiency and users’ comfort using AI prediction and patented technology, providing an IoT Gateway along with a software-as-a-service to optimise energy consumption of commercial and industrial buildings. It uses weather forecast, building occupancy and activity to automatically and dynamically control air-conditioning systems to make them more efficient. Artificial intelligence is allowing us to grasp all the complexity of the systems while optimising the comfort and reducing the costs.

Another thing that appeals to me in this approach is that it aims at making existing systems more energy friendly. Too many energy efficiency strategies today consist in just buying brand new equipment - newer technology means better efficiency. It doesn't always make sense, environmentally speaking to change the whole system.

Energy Efficiency corresponds to a reduction of the energy consumption of a system for a given output/production. Energy Conservation is a reduction of the energy consumption through - for instance - a better management of the output. To take an example, changing a lightbulb with LED is a typical Energy Efficiency measure, but turning off the light when it is not needed is an Energy Conservation measure. Both are important aspects of energy reduction strategies, and a balance is important for an effective program.

Focusing purely on Energy Efficiency may create a bias in the approach and measurement. Indeed, for some machines - and HVAC are part of those - you may achieve a better efficiency ratio with a higher output. For HVAC we are looking at the Coefficient of Performance (or COP) which measures - in Asia - the amount of electricity (kWh) needed to produce one unit of cold (measured in Refrigerant Ton or RT). If purely looking at this COP as a measure of performance - one may be tempted to increase the output, and the amount of cold produced (or cooling load) for a better efficiency - even though this may lead to more electricity consumed overall.

Energy Efficiency measures are primordial, but they cannot be taken as sole metrics and measurement of how well an equipment, a building is faring on this approach. In Singapore - and the region in general, there is still today a bias towards these metrics, and it is important to ensure Energy Conservation has its place as well.

As such, the most common sources of energy waste in HVAC systems are due to lack of implementation of Energy Conservation Strategies. One of the reasons for that is that HVAC equipment and the automation systems controlling them operate on fixed setpoints. To ensure that the system is always delivering at least the amount of cold required, it is set for the worst-case scenario: very hot weather and a fully occupied facility. Based on this, in any other configuration (colder weather and/or less people) the system is delivering too much cold and creating a potentially uncomfortable temperature, on top of wasting energy - and this is particularly flagrant when it starts raining for instance.

For more info: Energy Conservation vs. Energy Efficiency

It is definitely quite new in the HVAC sector. There is a lot of education required on the concept and the type of optimization strategies that can be applied for energy conservation and their success!

The main questions and doubts that we are faced with are the following:

• Comfort: HVAC being mostly used for comfort, and these types of strategy looking at the amount of cold produced not being common, there is genuine concern that these strategies will impact the comfort level in the building. While it is true that over-enthusiastic energy conservation measures (turning the HVAC off completely) can lead to discomfort, when applied carefully they will lead to a higher level of comfort by avoiding over-cooling during rainstorms for instance, and better managing the spatial delivery of the cold air.
• Reversibility: the questions that come up most when people are not sure about a solution are “What if I don’t like it? Can I remove it?”. That is the upside of such purely software solutions, going back to how it was operated before can be done with the simple push of a button, and is extremely important to reassure potential users.
• Calculation of savings: Building a reference or baseline for energy efficiency measures only is relatively easy as the COP depends on one main parameter: the cooling load. However, the estimation of the electrical consumption of an entire HVAC system will depend on various parameters: temperature, cloud coverage, windspeed, occupancy in the different zones, covid measures, etc. Those baselines are less common in the sector and users need to be reassured and taken through the steps of establishment of these baselines.

IoT and data science in general allow energy management to go one step further and include energy conservation strategies even for systems as complex as HVAC.

Among other things they allow to:

(i) capture more relevant data to fuel the analysis,

(ii) analyse more complex data sets and understand correlations for more efficient actions taken and

(iii) analyse larger datasets.

This last point is especially important in order to apply energy conservation measures on HVAC systems as such strategies usually apply to air side/terminal equipment which are in much larger numbers than the production side equipment.