4 Limitations of Building Management
System (BMS) Data
With the growing excitement around the possibilities of tomorrow’s buildings, there is faith that “building management systems are the backbone of smart buildings.” Owners and operators are envisioning ways in which the traditional BMS can be expanded to become the source of data for how a portfolio of buildings is performing. Even for properties that have not installed a BMS, there is faith that advancements in sensor technology will make investment in a BMS more cost effective.
As such, a number of solutions have been developed to extract and visualize the notoriously dense BMS data for building operators. Armed with this data, the theory goes, operators will be able to produce the ideal environment for occupants in a more effective and precise way than currently possible, all while reducing wasted operating costs.
We’ve already covered why building automation doesn’t necessarily equate to building intelligence. However, there is an additional issue with this vision of the future: there are severe limitations with the data that can be captured by building management systems, and thus a limitation on the extent to which BMS data can facilitate the future envisioned for tomorrow's buildings.
Data are Expensive to Acquire and Utilize
The cost of sensors has dropped precipitously in recent years. In 2004, the average cost per sensor was $1.30. In 2020, the average cost per sensor is expected to be $0.38. Unfortunately, the cost reductions in sensors have not resulted in a significant decrease in the cost of a full BMS installation. As of 2014, the cost to deploy a basic BMS was at least $2.50 per square foot and could be as high as $7.00 per square foot. While the cost of sensors has plummeted, the cost of equipment controls has remained stubbornly high.
This is not to say the building management systems do not drive value and a return on investment – they certainly do. The point is that as a collection method, the cost to acquire data is too high to justify. Even solving a specific problem, such as basic lighting controls, can cost as much to install as a dedicated data collection solution for an entire building.
Beyond the costs of installing and maintaining a building management system, there are costs associated with using the data. A dedicated building engineer needs to be focused on analyzing the data, and adjusting set points and configurations to derive value. This raw analysis by highly skilled labor is time consuming and costly. If the property decides to hire a vendor to visualize the data in a more intuitive software, there will be additional costs, either directly or through a “commission” on the savings discovered with the software.
To quickly bring about the future that smart buildings promise, data collection needs to be significantly less expensive than currently available through building management systems.
There May Be Limited Value
Data have little value on its own. A data set is only as good as the insights that can be derived. In the case of BMS data, insights usually involve equipment schedules, set points, and system configuration optimizations. For example, by identifying that the HVAC system is running when the building is unoccupied, a building can make significant reductions to operating expenses through utility consumption. Likewise, highly granular data sets around startup and shutdown processes may yield optimization insights for system configuration.
There are plenty of examples of poorly configured building management systems that can yield significant savings if optimized. However, this is much more likely to be the case in unique building types, such as hotels and stadiums that have constantly varying occupancy rates and schedules. For office and multifamily apartment buildings, which have relatively consistent schedules and occupancy rates year-round, the BMS may already be close to optimized.
While there is likely to be “performance drift” in any building type over time, the point is that no amount of data will yield significant results if the system is already close to optimized and rarely requires changes.
It's Likely Missing the Small Equipment
It should be noted that schedules in office and multifamily buildings do get changed for maintenance purposes, and are sometimes not changed back. This is a serious issue that can lead to large spikes in operating costs and put tenant health or comfort in jeopardy. As such, there should be a building performance solution in place to ensure that operators are notified immediately if any equipment remains on when it should be off or visa-versa.
There is no reason that this function cannot be done with software that utilizes BMS data. The issue is that there will be large blind spots because most building management systems do not control smaller equipment. Because the cost to install, maintain, and utilize is so high, most properties with a BMS only have it installed on the major loads, such as large HVAC equipment and lighting. With limited resources, it makes sense to focus on the building systems that will have the largest impact on operating expenses and tenant comfort.
Schedule mishaps or equipment malfunctions can have significant impacts on tenant comfort as well. For example, if an exhaust fan breaks, the lack of ventilation can cause unwanted smells and other indoor air quality issues. If an elevator breaks down, tenants may be stuck inside. Buildings are made up of hundreds of pieces of equipment, it is severely limiting if operators can only gain visibility into a small fraction of the total.
The final limitation of using BMS data to optimize a portfolio of buildings is the inherent lack of scalability. Perhaps each building in the portfolio has the same BMS vendor, but that is highly unlikely. Each vendor is going to have its own proprietary data protocol, which requires a developing and maintaining a number of different processes and integrations. Not only is this hard to manage and maintain, but the BMS vendors often have competing products and thus are incentivized to make their data inaccessible to third parties.
While part of the difficulty of extracting BMS data may be competitive, there is also a legitimate concern about security. Building data is not only valuable to competitors, because it is tied to controls of building equipment, it can be extremely dangerous to occupancy if manipulated. Most BMS are confined to the buildings intranet for this very reason. Any attempts to extract this data to a cloud-based solution will have serious security concerns, significantly higher costs for the appropriate security systems, or both.
There is also the consideration that a portfolio will contain a mix of properties with and without building management systems installed. Leveraging BMS data to find operational waste may be effective in portions of the portfolio, but a separate solution will be required for the buildings without a BMS. This requires an additional round of diligence and technology evaluations that will slow down rollout and stifle scalability. The point is, relying on BMS data to drive operational improvements will necessarily run into scalability issues.
Each building is different, and the applicability of a BMS for its core functionality comes down to the specific needs and goals of the property. However, to create a “smart” building that is fully optimized to produce the ideal environment for tenants while consuming the fewest resources possible, a BMS should not be relied upon to provide data and insights.
The limitations of cost, ongoing value, breadth, and scalability of BMS data are not compatible with tomorrow’s buildings. The precise controls enable by building management systems will likely have a role to play in smart buildings, but a BMS should not be considered a requirement to achieve and maintain a top-performing building.
In the early days of Enertiv, there was a realization that existing data streams from building management systems, utility meters, sensors, etc. were not sufficient. For that reason, we developed the EnertivTwo to track every individual piece of equipment independently. This allows us to affordably find value from data insights, even in office and multifamily buildings that have consistent occupancy patterns. Moreover, because we are not limited to the BMS, there is near unlimited scalability across building types and types of equipment.
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