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digital energy twin

Digital Energy Twins Can Help Predict How Changes to a Building Will Effect Its Sustainability

There’s been a lot of buzz in the commercial real estate industry lately about building “digital twins” and how they can improve a host of processes for real estate professionals. This term “digital twin” gets thrown around quite a bit and the definition is often a bit fuzzy since this concept is still in the early stages of being understood and embraced by the market. A true “digital twin” needs to be able to associate and connect a building’s design and utilization, in a single model, that mimics and therefore predicts the building’s operation and performance based upon a multitude of parameters.

Often the concepts of “digital twins” and “building information modeling” are confused. While BIM models can streamline building design and construction, and if kept up to date, can be a wealth of information about a building’s components and systems, they are not intended to provide feedback on the building’s performance over time. A true digital twin can help owners navigate how to best operate and upgrade their buildings while minimizing required capital outlays and maximizing investment returns. This represents perhaps the most compelling use case that a “digital twin” can provide to the real estate industry. In light of sustainability initiatives being proposed or implemented in many markets (not to mention that today is Earth Day), the power of digital twin technology to drive decision making that positively impacts both tenant work environments and building financial performance while making a substantial impact on emissions is hard to understate.

The notion that a building’s energy use is important information is not a new one. Electricity, heating, and cooling costs comprise a sizable portion of a building’s overall expenses, and it has long been understood that the right capital improvements could positively impact a building’s bottom line. But historically, when owners and their property teams analyzed potential energy-saving building upgrades, they relied largely on fragmented and siloed analysis from engineering firms that used little real-time data to analyze the ROI of million-dollar improvements. Additionally, opportunities to optimize existing systems via fast payback projects with minimal required capital outlays were typically overlooked. The cursory nature of traditional industry analysis and the associated complexity of systems have made it virtually impossible to accurately predict how building upgrades would ultimately impact building performance, related operating costs, and investment returns. While energy efficiency and attendant cost reduction were always goals, they were very challenging ones to achieve.

Now, the regulatory climate is increasing the consequences associated with this lack of knowledge. Last Thursday, the New York City Council voted in favor of the “Climate Mobilization Act,” a series of climate-related bills centered around a measure requiring buildings over 25,000 square feet to meet energy efficiency benchmarks, with the goal of the city cutting carbon emissions by 40 percent by 2030.

With the debate about how to address climate change heating up across society, energy efficiency in buildings is becoming a legal requirement rather than just a logical way to cut costs. In this environment, it’s particularly vital for building owners to use digital twins, which allow them to gauge exactly what energy impact their building improvements will have and ensure their investments achieve their desired returns. I would note that, when it comes to urban environments, cutting an office building’s emissions by 40 percent is fairly ambitious, and most buildings also will have to offset their emissions with renewable energy credits or significantly change the building’s utilization, i.e. reduce the number of occupants.

At Bractlet, we’ve developed what we call a “digital energy twin,” which reflects a building’s actual performance and energy consumption by incorporating building design and utilization information with an abundance of real-time operating data ranging from building automation system (BAS) equipment commands and sensor data to equipment-level energy consumption to weather data.Once the building’s operations are modeled to 98 percent plus accuracy, we can evaluate the impact of different energy-saving system optimizations and upgrades, empowering building owners to make better investment decisions.

We have seen numerous examples of the hazards associated with evaluating buildings in a non-holistic manner. One of these was a large building in Texas that installed a new energy-efficient chiller at a cost of $500,000. Unbeknownst to the building owner, the system was installed incorrectly, and instead of saving money, it was actually consuming significantly more energy than the previous chiller at a rate which would have resulted in $1,000,000 in lost savings over the chiller’s life. With a digital energy twin, we were able to evaluate the multitude of factors that impact the chiller’s performance and identify that it was not being supplied the proper amount of water due to the control of the system’s related chilled water pumps and thus causing inefficient operation.

Part of the benefit of a digital energy twin is that it can model a host of scenarios, beyond just HVAC operation, to holistically assess all the variables that affect building performance and energy consumption, including occupancy rates. A building with 70 percent occupancy has different energy requirements than a stabilized one at 95 percent occupancy with high-density tenants (i.e. tech firms or co-working spaces). With a digital energy twin, building owners and their property teams are able to understand what system capabilities they require at various occupancy and density rates. This results in avoiding situations where existing equipment is replaced with like equipment that cannot sufficiently and/or efficiently heat or cool a building based on its evolving operating requirements.

The key to making intelligent decisions about a building starts with the understanding that each building is not a collection of siloed systems, but an interconnected ecosystem where each part interacts with one another in a unique way based on the governing scientific principles of physics and thermodynamics. Digital twin technology, which can simulate the building’s operations, provides building owners with a tool to help them better understand the building in a holistic manner that reflects its actual performance. For asset managers looking to make the most informed decision on building investments, or those in municipalities with stringent emissions reduction requirements, digital twin technology is a valuable decision-making tool. It can use modeling tools to allow owners to cut energy consumption and operating costs across entire portfolios. Most promisingly, it can help make better investment decisions by creating a representation of the present that can predict the future.

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