Show Me The Money: Boosting Investor Confidence Through Better Building Energy Efficiency Modeling
Ellen Franconi is an invited speaker to the World Summit on Building Simulation Research hosted by the T.C. Chan Center at the University of Pennsylvania on March 28, 2013. The summit will bring together leading international experts in building simulation, and will be live-streamed the day of the event at http://tcchancenter.com/bsimworldsummit/.
U.S. buildings consume a tremendous amount of energy, requiring 42 percent of the nation’s primary energy use. If American buildings were a country, they’d rank third—behind China and the U.S.—in their primary energy use, according to RMI’s Reinventing Fire analysis. The inefficiency associated with our aging building stock presents a challenge for meeting aggressive energy-use reduction goals, especially if we’re going to transition the U.S. off fossil fuels and address global climate change.
This problem provides tangible opportunities for business investments to profit from packaged efficiency solutions that provide deep savings and added value to tenants and owners. A Rockefeller Foundation study last year assessed the U.S. building retrofit market opportunity at $279 billion. Such an investment could reduce the energy use of U.S. buildings built in 1980 or earlier by 30 percent and realize more than $1 trillion in energy cost savings over a 10-year period. In addition, scaling retrofits could reduce U.S. greenhouse gas emissions by 10 percent per year and create more than 3.3 million jobs.
Despite a seemingly attractive economic-meets-energy opportunity, though, there’s a hitch. Deploying private capital for energy efficiency retrofits could be transformational, but investors lack the confidence in energy savings estimates against which lenders would underwrite loans. How do we boost that confidence? The short answer: through improved building energy modeling that better communicates risk.
Those seeking capital for energy project investments need to do a better job presenting risk information to decision makers. Simple payback, return on investment, or even life-cycle-cost models do not provide sufficient information on risks and rewards. Investors cannot properly assess cash flow forecasts without a discussion of risks and risk mitigation. For example, imagine two five-year streams of cash flow, one that generates a 15 percent return and one that generates a seven percent return. Which is better? The answer depends on the level of risk in achieving the forecasted benefits. If the seven percent return is based on seasoned existing cash flows it might be highly preferable to a 15 percent return predicated on executing construction, lease-up, and other risks.
The initial estimated energy cost savings is a critical value for investors considering energy projects. The risk side of this economic opportunity is that a building will fail to live up to performance expectations and the anticipated cost savings are not achieved.
Unfortunately, savings estimates are typically calculated as a single number and do not indicate a probable range or an estimated uncertainty. In addition, risk can be introduced into the project through poor process execution and energy-efficient feature underperformance. Failure to provide information about uncertainty leaves the financial analyst with no means to price the appropriate rate of return. This causes the financial analyst to increase the required rate of return or to de-rate the savings before applying the financial model. This practice undermines the viability of energy projects.
Yet recent efforts have highlighted attempts to address these confidence deficiencies. The Environmental Defense Fund—through its Investor Confidence Project—is working with engineers, financial firms, insurers, regulators and utilities to unlock the flow of private capital into building efficiency investments. They are developing protocols so that energy and cost savings from retrofit projects can be predicted more accurately and realized more consistently. And building energy modelers in particular are a critical link between hypothetical and actual building performance and assessing performance risk.
The modeler utilizes building simulation software to project energy use and costs. By evaluating and comparing performance, the modeler determines the benefits of building siting, space layout, passive design elements, and energy-efficient components. The modeler also identifies occupant comfort issues. Building energy modeling (BEM) is often applied in the design and retrofit of buildings to evaluate proposed and alternate integrated-design solutions that satisfy project performance targets. To support investors, information must be provided that describes the risk associated with cost savings estimates and indirect benefits of improvements so their value beyond costs can be considered.
The level of risk associated with the energy savings for retrofit projects can be addressed in two general ways: 1) the approach for quantifying energy savings, and 2) methods for managing risk introduced by the modeling process.
Current practice for quantifying estimated savings through energy modeling involves only providing a single number to represent estimated savings. In reality, not all model input parameters can be known with certainty. These less-certain parameters are better expressed as a probable range instead of a single input value. In acknowledgement of this, over the last decade building modeling researchers have been investigating methods for producing results that reflect the uncertainty of the input values. Such efforts require conducting 1000s of runs, utilizing statistical sampling strategies, and producing results as a probability distribution function. The next release of OpenStudio, an open-source application suite and software development kit utilizing the EnergyPlus Simulation engine and funded by the Department of Energy, will include options for completing uncertainty analysis. The OpenStudio uncertainty analysis draws from the DAKOTA project, an engineering optimization and uncertainty analysis modeling library developed by Sandia National Laboratories. Results are presented as a probability density function, as illustrated in Figure 1. In the figure, the degraded office building and the retrofit office building were modeled with a wider and narrower range of input parameters, respectively. Having this capability available in commercial-building software brings modeling closer to meeting investor needs.
Managing risks refers to controlling the process for identifying opportunities and assessing savings through building energy modeling. Currently, detailed industry-accepted best practice procedures are not defined for the modeling process. Most modelers learn their skill on the job and applied methods are inevitably inconsistent. Investors will have greater confidence in energy savings estimates if they can be assured that the modeler is competent and their methods consistent. This can be accomplished in two ways: through credentialing and establishing project requirements. A modeler’s credentials can be demonstrated through professional certification. Currently, the American Society of Heating, Refrigeration and Air-Conditioning Engineers (ASHRAE) and the Association of Energy Engineers (AEE) offer modeling certification programs. These programs, which are outlined in Table 1, allow individuals to demonstrate mastery of best practice methods. Process risks can also be managed by encouraging consistency through the specification of modeling requirements for efficiency requirements. To support this, DOE is funding the creation of the BEM Library, which is a freely accessible electronic repository of best-practice methods. RMI is involved in structuring and developing this body of work. It is envisioned that the effort will support increased modeling consistency, facilitate developing modeling requirements, and instill greater confidence in results.
Today’s interest in energy efficiency is perhaps the strongest it has ever been. To capitalize on the investment potential the risk associated with retrofit energy savings estimates need to be identified. Project savings risk can be partially addressed through uncertainty analysis, modeler credentialing, and establishing modeling process requirements. Continued progress in these areas will help energy projects be considered alongside traditional investments. Coupling these advancements with new financing mechanisms that target outcome-based performance provides the levers needed to realize retrofit building investment opportunities. The approach offers a winning triple play that will provide business profit, new U.S. jobs, and environmental benefits.
For more on this topic, read Franconi’s white paper for the American Council for an Energy-Efficient Economy “Risk-Based Building Energy Modeling to Support Investments in Energy Efficiency.”
House energy photo courtesy of Shutterstock.com.