In the era of machine learning, blockchain, and the “internet of things” (IoT), Greenprint remains focused on “small data”—monthly energy, water, and waste bills normalized by building and geographic attributes such as square footage, building type, vacancy rates, and heating and cooling degree days. Using Greenprint’s shared-data benchmark drawn from these simple data (and managed in the cloud on ULI Greenprint’s Measurabl platform), owners can identify which buildings in their portfolio are performing better or worse than the benchmark and spot opportunities for investments in cost-effective technology upgrades, training in best practices (learning from the leaders), and tenant engagement strategies to improve performance. The benchmark also encourages healthy competition among building managers and building portfolio owners, all looking to leverage data to reduce their operating expenses and improve their net operating income (NOI).

The Greenprint benchmarking tools are by no means “big data,” and this is the way that Greenprint members like it. Over the past nine years, Greenprint members have leveraged these benchmarking data and shared their best practices to cut energy consumption by more than 17 percent and greenhouse gas emissions by more than 20 percent, saving $36.4 million a year in annual energy, water, and waste expenses. But many Greenprint members see the potential for big data, IoT, and smart buildings to drive even deeper efficiencies and cost savings, and they are piloting big-data solutions to further energy and environmental performance. Key areas where big data are being tested include the following:

Optimizing energy and environmental performance: Several members are piloting projects with companies like Lucid, Verdigris, and Direct Energy’s Panoramic Power that deploy dozens or even hundreds of low-cost sensors to help provide a better snapshot of a building’s mechanical and energy performance in real time, and helping build an IoT network across the various mechanical and electrical systems within a building to enhance their connection, communication, and performance. Companies are also looking to new sensors, meters, and diagnostic tools to provide continuous commissioning, the real-time tracking of all major building mechanical systems, helping facility managers to spot and correct problems before a small issue becomes a major mechanical failure.

Smart buildings and IoT tied to enterprise asset management platforms: Some members are exploring enterprise-wide big-data asset performance management tools from Yardi, Aveva, Schneider Electric, and others, tying together building performance, maintenance, procurement, and other components of asset management into an integrated system. The value proposition behind these next-generation asset management platforms is that IoT can provide better real-time information on building energy performance and maintenance needs, as well as keep track of inventory needs (and staffing and security needs) in real time, reducing costs across asset management.

Machine learning, artificial intelligence (AI), and leveraging the “hive mind”: The next generation of big-data building tools will not only collect a tremendous amount of data, they will then deploy AI-driven automation to physically tune up and optimize a building’s mechanical systems, along with other IoT-enabled devices. While automation is not a new concept for buildings—most advanced buildings run off a building automation system—many of these new programs would require little to no touch by facility managers and would be managed off site, offering the ability to manage hundreds of buildings remotely and make repairs (or quickly deploy a maintenance crew) before a major mechanical problem develops. For buildings taking part in demand-management programs and buildings with on-site renewable energy, energy storage, and/or electric vehicles, this AI could also play the role of an energy price arbitrage, helping building owners store energy on site when prices are low and sell energy back to the grid when the building can make the most money on this excess power. Other next-generation building system tools like Comfy leverage crowdsourced data (in the case of Comfy, whether building occupants are hot or cold) to provide automated on-demand building adjustments and long-term recalibration of building zones and operating schedules to optimize comfort and system efficiency.

Healthy Skepticism
While Greenprint members and other real estate sustainability leaders believe in the power of data to cost-effectively achieve their energy and sustainability goals, many say that some of the more cutting-edge approaches to smart buildings may provide more big data than building managers can effectively digest to optimize energy and environmental performance. Some of the reasons that Greenprint members have not fully embraced the current big-data solutions on the market today include the following:

Upfront costs versus ROI: For many of the automation and big-data solutions on the market, the potential savings in utility and facility management expenses are simply not enough to justify the cost of the systems, including hardware and software. For example, Tishman Speyer’s senior director for sustainability and utilities, Jonathan P. Flaherty, has evaluated dozens of next-generation smart building tools, but has implemented only a few in Tishman properties. “We have worked hard to make our buildings as efficient as possible, and most of the solutions on the market could only reduce energy expenses maybe an additional 10 percent,” he says. “Given the costs of these technologies and the new risks many of these technologies can create, we need to ask ourselves whether there is enough savings to justify the project.”

Managing cybersecurity risk: Many real estate owners are cautious in adopting next-generation technologies because of the real and perceived cybersecurity risk associated with building systems that are managed in the cloud, or linked to other critical business systems behind a company firewall. After the massive hack of retailer Target’s systems in 2014 was traced back to access via Target’s HVAC vendor—and after cyber attackers proved they could hold building automation systems hostage in the middle of winter in 2016 in Finland, company chief information officers have been reluctant to approve new technologies that could provide a back-door route into other critical systems.

A counterpoint to this cybersecurity risk may be that some big-data solutions provide significant risk reduction for building mechanical equipment through continuous commissioning, spotting maintenance issues before they lead to catastrophic failures. Michael Chang, director of energy and sustainability at Host Hotels, puts it this way: “We shouldn’t just look at the energy and time saved through these systems; we should also recognize the protection they provide against more expensive maintenance issues that would occur if they were not spotting issues as they started to occur.” Host has deployed cloud-based analytics to provide continuous building commissioning, and has been impressed with the results for preventive maintenance.

A human face is needed for tenant satisfaction: One important measure of automated building systems is the ability to quickly respond to tenant needs. Flaherty from Tishman Speyer puts it this way: “When the building temperature is not where tenants want it to be, we can’t tell them, ‘Sorry, it’s the computer’s fault.’ We need to provide our facility managers with the data they need to make decisions, but we also need to provide our tenants a human face that can be accountable to their tenant comfort.” If a tenant is paying $20-plus per square foot ($215-plus per sq m) in rent, the relationship is likely too important to risk tenant satisfaction to save 20 cents per square foot ($2.15 per sq m) in energy expenses. Chang of Host Hotels is optimistic that automation can reduce energy consumption and enhance the tenant experience, but he also stresses the importance of humans leveraging the technology. “While advanced analytic systems can help identify and address building inefficiencies, we still need people in the buildings to address this in real time while ensuring guest comfort,” he says. “We see a lot of potential for the platform to help speed up the identification of inefficiencies that were traditionally not noticed until significant increases in utility bills.”

Drowning in data: Greenprint member Sara Neff, senior vice president for sustainability at Kilroy Realty Corporation, has evaluated several big-data solutions and found that to drive behavior change among facility managers, sometimes the simplest solutions are the best. “Many of the new smart building analytics tools have beautiful visuals tracking dozens of data points on a minute-by-minute basis,” Neff says. “While these are pretty to look at, they are not providing facility managers with the two or three most important things they can do that day to improve the building’s performance. We have had the most success with systems that send facility managers a text message once a week with a couple of things they can do in the day ahead to address maintenance issues and optimize the building’s energy performance.”

As evidenced by their membership in Greenprint, members believe in the power of data to help them achieve their sustainability goals. While they are skeptical about the ROI of many of today’s big-data solutions, they are optimistic about future big-data tools that can help them achieve their sustainability goals.

BILLY GRAYSON is executive director of the ULI Center for Sustainability and Economic Performance.