Green Ovations: Mapping the Building Genome - How data analytics enables a better understanding of how buildings consume energy, and why open data can be a game-changer in the energy efficiency sector

Forty percent – that’s the portion of energy that commercial and residential buildings consume.¹ Yet, in spite of the fact that buildings are driving nearly half of all global energy consumption, 30 to 50 percent of that consumed energy is wasted.²

And energy usage is increasing. The US Energy Information Administration estimates that the commercial building sector will account for the second-largest increase in the US’ total primary energy use – 3.3 quadrillion BTU from 2012 to 2040.³

In many areas throughout the United States, this increase in commercial building usage is putting more stress on the electric grid. The American Association of Civil Engineers estimates that an investment of more than $670 billion is needed by 2020 just to keep the power grid functioning.⁴

A viable solution to head off some of these costs is to reduce both overall consumption and peak demand through energy efficiency – America’s cheapest, most scalable energy resource that we have at our displosal.⁵

While efficiency is an obvious approach, it’s not necessarily a simple one to attack. Buildings are complex and difficult to understand. The traditional process to better understand which buildings are ripe for energy efficiency projects and to evaluate which specific actions are required to achieve those savings is time consuming and expensive. The traditional process relies heavily on manual approaches to identify energy-saving opportunities. Because buildings are not being evaluated in a timely and cost-effective way, many building owners and managers do not understand their opportunity to save. This creates an information barrier that prevents the industry from achieving deeper savings.

Fortunately, the growing availability of utility meter and building data, the proliferation of cloud computing, and new advancements in analytics are enabling a fundamental shift in the way utilities, energy service providers, government organizations, and building managers can address this problem. Combined, these factors allow for rapid, mass-scale generation of data-driven insights and models that provide a sophisticated understanding of how a building uses and wastes energy.

The Building Genome Project
The adoption of analytic tools for utility efficiency programs is growing, but many stakeholders do not fully understand how powerful they can be to tackle this problem. The Building Genome Project was established for this reason – to mine, collect, and organize publically available building data for the purpose of gaining a deeper understanding of the energy efficiency opportunities present. Through the Building Genome Project, publically available building data is combined with advanced analytics to create unique physics-based energy models of commercial buildings in minutes. Data is the starting point to develop an energy model, which is typically building asset and/ or consumption data. With these models, the Building Genome Project can demonstrate how both small and large changes can influence energy consumption and drive savings.

Like the human genome, the Building Genome is a detailed mapping of hundreds of distinct markers that influence how a building consumes energy. These markers are composed of a variety of mechanical equipment, construction materials and configurations, energy fuel sources, and operational characteristics. These can include lighting markers like fixture type, utilization, and building coverage; HVAC markers like equipment type, fuel type, and system performance; or building envelope markers like roof, wall, and window type, the number of glass panes, and insulation performance. The building markers can become very intricate – each building’s occupancy, geometry, hot water system, and many other types of equipment can contain additional markers that impact energy usage.

Once a building’s markers are properly understood and ordered, they can be combined to create an energy model of each building, helping to provide critical insight into how a building consumes energy, every hour of every day. The energy model can also help determine the most effective equipment and operational changes to save energy. Physics-based energy models also account for interactions between building systems, including how elements like window and wall performance or lighting systems affect heating and cooling requirements for a space.

Another benefit of energy models, in addition to better understanding a building’s energy usage and drastically cutting time to insight, is the ability to run scenarios to determine how equipment or operational changes to a building may impact its energy usage. By gaining these insights, utilities, policy makers, energy service companies, and building owners can make more informed portfolio-wide and building-specific decisions about energy efficiency.

Energy Savings Potential for New York City
New York City was the first city analyzed for the Building Genome Project, which included the development of unique energy models for more than 30,000 buildings and uncovered over $380M in annual savings potential in just a few days. For New York City, the Building Genome Project was able to tap into public data sources, including tax assessors’ information (e.g., basic building information) and consumption data – annual site energy use intensity for most buildings over 50,000 square feet and zip code level consumption data for electricity, gas, and steam. In addition, data on buildings with oil boilers was also available for the city. The energy models were developed by supplementing this information with privately sourced, hyper-local weather data and statistical inference algorithms based on data from tens of thousands of previous audits.

To better understand how sample equipment and operational changes may impact New York City’s commercial building portfolio, three scenarios were run against these energy models. Each scenario not only showed the potential for significant financial savings and portfolio energy savings, but also gleaned further insights to be used when implementing an energy efficiency program.

First Scenario
In the first scenario, the Building Genome Project looked at the potential impact if every commercial building turned the thermostat up one degree in the summer and down one degree in the winter. The analysis found that New York City could save $145M annually as well as a portfolio energy savings (MBTU) of 1.9 percent. With this finding, it’s important to note that even though buildings can often have similar issues that result in inefficient energy use, the most effective measure or treatment for a particular issue can vary from building to building.

For example, there are many behavioral and educational efforts that can be employed to encourage multi-family building tenants to change their thermostat settings, and these efforts can usually be implemented quickly and at no cost. However, in the commercial market, automated, controls-based measures are usually necessary to help achieve these goals. Measures like re-commissioning building management systems to improve HVAC operations can include aggressively changing thermostat settings during certain periods of the year or times of the day. In buildings or smaller spaces with no centralized controls, new advanced thermostats can be more easily programmed and monitored to ensure persistence.

Second Scenario
The Building Genome Project’s second scenario looked at the impact of upgrading old windows to new windows. The analysis found that if buildings with old windows installed new, efficient ones, the city could save $227M annually with an MBTU savings of 4.5 percent. In addition, sorting New York City’s zip codes by potential energy savings found that the top 35 zip codes had an estimated savings three times greater than the 35 zip codes with the lowest savings potential. Many of the high potential zip codes were located in Manhattan, where tall, skinny, glass-laden buildings are plentiful and are the ideal target for high performance window upgrades, from an energy savings perspective. This demonstrates that, in certain instances, it may be beneficial for utilities and policy makers should evaluate their own geographical areas to determine whether more tailored, appropriate solutions in different parts of a given region.

Other considerations should be factored in before deciding to promote or incentivize window retrofit technologies. For example, a full building window retrofit for many buildings in a place like New York City may be disruptive and may or may not yield attractive paybacks for each building. Instead, alternative complementary technologies like window film could also be evaluated for each building.

Third Scenario
In the third scenario, the Project focused on the impact on financial and energy savings if every building with an oil boiler that burned grades #4 or #6 oil replaced it with a high efficiency natural gas boiler. This scenario showed that the city could save $10M and 0.4 percent MBTU annually. In 2011, New York announced that it would phase out these boilers since they burn the dirtiest heating oil types available in New York. At the time of the City’s announcement for this plan, it noted only one percent of the City’s buildings still burn #4 and #6 heating oil but they account for more soot pollution than all the cars and trucks in New York City combined.⁶

Compared with the other scenarios, scenario three offers the lowest absolute dollar savings across the portfolio, since it applies to only a small subset of buildings, but the energy savings to those buildings are significant at an average savings of 10 percent per building. This scenario highlights that it’s important to consider the economic impact, in addition to considering the environmental impact of a regulation.

Enhancing the Building Genome
The Building Genome Project leverages only publically available data (unless private data is supplied to the project) to drive the analytic-based insights. The information contained in public data tends to vary by geographic location, but typically offers high-level data points about a building. Due to the limited data used to inform these energy models, the Building Genome Project focuses on portfolio-level and zip code level insights.

The more data that is provided about a particular building, the more accurate a building energy model becomes. For instance, if energy consumption data were added to the Building Genome Project models, which are based on public data, these models could be used to evaluate the savings potential of an individual building.

Greater availability of public data could support further innovations and enable the efficiency market to reach its full potential – a market that is estimated to hold $370B of annual energy savings worldwide. For the New York City analysis, its annual energy benchmarking data was extremely helpful.

There are a number of beneficial applications that can come out of mapping the building genome and enhancing it with data analytics and rapid energy modeling. With an energy model of a building, utilities, and energy service providers can identify the highest potential customers, increase their interest by sharing specific insights about their buildings, comprehensively and quickly evaluate projects, and constantly scan for new efficiency opportunities. In addition, the building genome can help government agencies, utilities and building owners and managers more easily deploy energy efficiency solutions to geographically target constrained areas instead of increasing capacity, and better forecast load requirements in the future. Finally, leveraging the building genome can help to better understand market potential and more effectively and strategically plan for efficiency programs. While robust approaches exist to support these efforts, running scenarios like those for the New York City Building Genome Project enables us to ask and answer more questions and consider more scenarios, better, faster, and cheaper.

About the Author

Bennett Fisher is CEO and co-founder of Retroficiency, the building intelligence company for utilities and energy service providers. In 2009, Bennett co-founded Retroficiency to help address the manual bottleneck of identifying and evaluating commercial buildings for efficiency upgrades, which was preventing the market from realizing its true savings potential. Retroficiency enables utilities and energy service providers to target the right buildings, engage customers with building specific insights, convert real projects, and track opportunities at scale to achieve critical energy efficiency targets. Bennett has a BA in economics and entrepreneurship from The Johns Hopkins University and an MBA from the Massachusetts Institute of Technology Sloan School of Management.