March 15, 2022
Sophia Bhaumick
Senior Marketing Manager, Arloid Automation

Why Arloid is Leading the Way: In Conversation with Mohd Asjad

“What makes us different? It’s the meticulousness of our modelling team, the level of precision we achieve – our pinpoint accuracy. That’s what guarantees HVAC optimisation and AI performance.”

From creation of the Digital Twin to the way we train our AI using Deep Reinforcement Learning, the Arloid solution is all about detail and effectiveness. The secret to understanding what makes Arloid different lies in our processes. Meticulous and exact, the way we work allows us to optimise faster than our competitors and deliver superior results for our clients.

In this interview, Senior Marketing Manager Sophia Bhaumick talks to Arloid’s Building Energy Modelling Engineer, Mohd Asjad about why highly accurate Digital Twin modelling and AI implementation is the key differentiating factor.

Sophia Bhaumick:

What do you think makes Arloid’s work unique – how are we different to our competitors?

Mohd Asjad:

The key thing that separates us from our competitors is the level of detail we go into when preparing our building models. To be as accurate as possible, we deal with each and every individual HVAC device in the system which means modelling each and every thermal zone in the building. And that’s not the case for other companies; their solution does not require the same level of detail or the modelling of different zones and devices. What makes us different? It’s the meticulousness of our modelling team, the level of precision we achieve – our pinpoint accuracy. That’s that guarantees HVAC optimisation and AI performance.

Sophia Bhaumick:

Could you explain a bit more about your role in the process as the Building Energy Modelling Engineer?

Mohd Asjad:

I come in as soon as the building model has been created. My role is essentially to implement the building model and the AI through running simulations that take into account the building’s specific architecture and geometry and a range of conditions to determine comfort indices in each thermal zone. It’s a complex process and accuracy is of paramount importance to ensure the AI understands how to react to different environmental states. We use a Deep Reinforcement Learning algorithm to achieve this.

Sophia Bhaumick:

How long the implementation of the building model take?

Mohd Asjad:

Since our work is so intricately tailored to individual structures, this is totally dependent on the building in question and its geometry. Typically, a small building with around 100-200 different thermal zones can take 3-4 days although this can go up to a week. For large buildings that require more complex zoning it might take around a month to prepare and calibrate the building model and implement the AI – but, even so, this is leaps and bounds ahead of our competitors.

Sophia Bhaumick:

Finally, in your opinion, what makes our product the best on the market?

Mohd Asjad:

In my opinion, it’s about control – the way we control devices on an individual level and, of course, the way we implement the AI. We prepare the building model – the Digital Twin – and then we use actual data to simulate conditions and train the AI using Deep Reinforcement Learning. That’s the key point here – our key differentiator.

Sophia Bhaumick:

And that’s what enables us to optimise faster, isn’t that right?

Mohd Asjad:

Exactly. We can optimise HVAC infrastructure in 30 days – much faster than our competitors – and this is all down to the way we use our AI. We simulate for general values and then the AI formulates the equation which will allow it to predict all relevant values going forward. That means we don’t need to simulate every single parameter – saving vast amounts of time. We optimise more effectively and more efficiently because the AI learns to predict the values for us.

Optimise Faster with arloid.ai

Thank you to Mohd Asjad for providing our readers with an insight into the Arloid process and why we are ahead of the competition. Our commitment to accuracy and AI performance means we enable clients in the real estate sector to optimise their HVAC infrastructure and deliver flawless thermal comfort every time. We do not require extensive datasets or a lengthy integration process – we deliver results quicker and better every time.

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