Block ip Trap

Not Your Old-School HVAC System: AI proactively saves energy and cuts costs

25 Aug 2020

By Andy McMahon

How many times a day do you get up and adjust your room’s temperature? Or call your office building’s landlord and ask them to lower the air conditioning? Heating, Ventilation, and Air Conditioning (HVAC) systems were built to be reactive. But what if we could use artificial intelligence (AI) to make them proactive instead? AI is all around us, from self-driving cars in the transportation industry, to the voice recognition software in our smart phones. If we applied this knowledge and technology to HVAC systems, imagine the impact it could make.

Energy efficiency in the built environment

The amount of energy being consumed by buildings is staggering. It is estimated that built environments and industry account for 70 percent of yearly energy consumption in the United States. According to the U.S. Department of Energy, heating and cooling systems - including air conditioners, boilers, chillers, furnaces, and heat pumps - account for nearly half of the energy consumed by built environments. These systems, together with lighting, are a major source of energy usage. Even new ‘state-of-the-art,’ commercial HVAC systems experience significant loss in operational efficiency post-installation because of the way they are designed, installed, and maintained. This means that achieving HVAC efficacy is a priority for tackling the problem of overall energy efficiency in the building sector. 

For some time, industry professionals have been promoting the magnitude of the potential energy savings for owners of various building types, such as commercial retail, office buildings, nursing homes, manufacturing facilities, and many others. The question has been, how to achieve those savings quickly - and without having to change the entire HVAC system that is currently in place?

When buildings move away from the reactive technology that currently manages most HVAC systems, they can achieve much greater energy efficiency and savings, as well as an increase in occupant comfort.

Comfort doesn’t equal cost and carbon footprint

For anyone who has ever spent any time in a commercial or corporate building, you know that the temperature can make or break occupant comfort. This means that an HVAC system can have a big impact on employee productivity, wellness, and even customer satisfaction. By allowing AI to study a building and learn how it operates, the technology can then identify potential improvement opportunities, and autonomously optimize the system. This means no human intervention would be necessary and, by being proactive, it ensures maximum savings and comfort. 

By allowing AI to control the HVAC system, you can allow your building to autonomously self-regulate, which will ensure energy efficiency without compromising occupant comfort. With predictive technology, the temperature only changes and equipment is only used when the AI deems it necessary. This can ultimately reduce energy bills upwards of 25 percent, and is a perfect illustration of how commercial buildings can help fight climate change.

Technically speaking, AI combines building energy equations with deep learning and time series data to calculate how each zone will react to changing conditions (e.g. weather) over time. The deep learning neural network, using the thousands of data points at hand (coming from the HVAC equipment, Building Management System and/or access control systems), can “look into the future” to predict the state of each zone in a building. From these predictions, the AI engine determines the best way to manage the energy flow for every zone in the building. Using algorithms working in real-time, the AI can then instruct the system by writing back directly to the controller of the existing HVAC system, telling it how to operate more intelligently and efficiently.

AI constantly learns and improves; the technology can make hundreds of adjustments to an HVAC system every few minutes, and becomes savvier every day. To achieve the same outcome using human intervention, a building operator would have to hire dozens of engineers to spend the day staring at a BMS system and decide, every few minutes, if they should make an adjustment. 

AI understands the built environment as an ecosystem of interconnected components, and focuses on achieving real efficiency by modulating the energy flowing throughout a building at any given time. Buildings today are complex. AI helps achieve energy savings and occupant comfort by anticipating the impact of internal and external variables on a building's thermal load, operating proactively to maintain the ideal work environment. 

 

Andy McMahon is Director at BrainBox AI. BrainBox AI uses deep learning, cloud-based computing, algorithms, and a proprietary process to support a 24/7 self-operating building that requires no human intervention, and enables maximum energy efficiency. 

BrainBox AI | http://www.brainboxai.com

 


Author: Andy McMahon