Navigant Research Blog

Rethinking Intelligent Building ROI: Follow the Money to Transactions

— November 14, 2017

The intelligent buildings market has undergone a makeover in recent years that has yet to move the needle on widespread investment. The narrative has shifted in the last 2-3 years from a focus on energy efficiency to business insight. The logic behind the push makes sense when you consider the financial impacts of energy costs relative to employee costs in terms of building ownership (remember the omnipresent JLL 3:30:300 calculator). The problem is, metrics that impact payroll or employee costs are complex and interactive. There is no mutually exclusive measure of productivity—if a workspace has the perfect temperature and lighting, an employee may still fail to meet a deadline because of so many hard-to-measure issues: personal life, management, workplace culture. The healthy building approach has been a pathway many stakeholders are taking to frame workplace conditions and worker productivity, but the reality is the numbers are still soft.

Energy efficiency remains a straightforward way to measure the impact of technology deployment. You invest in controls and automation in your office building, you see a reduction in your energy bill by 10%—that is a defensible measure of ROI. However, energy remains a small overall share of operating costs for many building owners, particularly relative to other business costs, so what can make building energy performance bare real weight in business? It seems a one-two punch of public disclosure and financial due diligence may be the answer.

Public Disclosure and Real Estate Valuation

Many US cities have aimed at building energy use as a lever to tackle greenhouse gas emissions. Public disclosure programs range from voluntary to mandatory but are generally limited to reporting, without mandates for efficiency improvements because of the politicization of climate change in the US. It seems there must be a bottom-line pressure that aligns with the energy performance rating to drive investment in energy efficiency. And now it seems there is.

A recent article in Urban Land explains, “If energy efficiency can be correlated to mortgage default rates, it could have a significant impact on energy disclosure and possibly even mortgage interest rates. Underwriters on new projects may consider requiring energy disclosure before issuing a new loan, or charging a higher interest rate (all else being equal) for energy-intensive properties. Mortgage companies looking to reduce their default risk may also look to engage their current portfolio in strategies to improve their energy efficiency.” This article was based on findings from a 2017 Lawrence Berkeley National Lab study, which aimed to correlate commercial mortgage default rates and energy efficiency. The study concludes that “building-level source energy use intensity (EUI) and the electricity price gap are statistically and economically associated with commercial mortgage defaults. Using building energy simulations, we find that building asset characteristics and operational practices that affect source EUI have very important effects on the likelihood of default.”

So, there it is, a roadmap for quantifying the relationship between building energy performance and real estate asset value. The argument could even go a step further and assert that intelligent building solutions are worthwhile investments to provide a foundation for minimizing EUI and ensuring ongoing energy efficiency gains. The analytics at the center of leading intelligent building solutions will monitor, report, and even predict changes in energy consumption based on space use. This insight can become strategic guideposts for business decisions around real estate. As more data is collected, there will be greater opportunity to tackle the challenge of quantifying those softer, yet significant, employee costs over time. Today, energy efficiency remains paramount in showcasing ROI.

 

AMI Data Brings New Possibilities for Energy Efficiency Measurement and Verification: Part 2

— August 4, 2017

Coauthored by Emily Cross and Peter Steele-Mosey

Part 1 of this blog series covered operational improvements and provided background on the role of advanced metering infrastructure (AMI) data in energy efficiency program evaluation, measurement, and verification (EM&V). This blog continues the discussion with a focus on program impact evaluation. Navigant Research examines these topics in detail in its report, Utility Strategies for Smart Meter Innovation: Energy Efficiency Measurement and Verification.

Program Impact Evaluation

The use of AMI data for program evaluation has the potential to substantially reduce evaluation costs. A major cost associated with the evaluation of large customer (commercial and industrial) energy efficiency program evaluation is onsite verification and metering. For some programs, it is possible to reduce the number of site visits required or reduce the frequency of site visits. Another opportunity for faster program evaluation using AMI data is large-scale validation of coincident demand savings for energy efficiency programs. With AMI data, it is a straightforward matter to isolate demand impacts occurring during utility system peak performance hours.

Programs operating in utility services areas with full penetration of smart meters, deploying energy efficiency measures with consistent load reduction patterns, are excellent candidates for evaluation using hourly or subhourly AMI data. Energy efficiency impact analysis using utility data assumes enough of the program participant savings are above a minimum measurable threshold. That is, the signal-to-noise, savings-to-baseline ratio must be high enough to see the savings in the meter data for a substantial number of participants in the program. Otherwise, program savings estimates may be statistically non‑significant even if savings are being achieved.

A hybrid EM&V approach, using a combination of advanced, automated AMI data analytics and targeted in‑depth evaluation, provides the most value to utility clients and regulators. Automated impact analysis using AMI data (M&V 2.0) can serve as an initial screening of participants. It can quantify realized savings measured at each participant meter using high accuracy pre-post time-of-week/time-of-year and temperature normalized savings models. Participant projects screened out of the automated analysis can be identified and sampled for deeper analysis, providing targeted insights to utility clients and regulators for more complex projects.

Where automated screening methods provide sufficient program feedback without further investigation into the reasons for measured savings, evaluation costs could be reduced relative to traditional methods. Automated M&V2.0 screening provides a high level, statistically significant measure of program performance without the need for follow-up evaluation for programs with established performance. The evaluator using such methods must demonstrate there is no bias introduced by using only projects with measurable savings to characterize program performance.

For mass-market (residential and small commercial) programs, traditional evaluation often involves the application of survey findings to validate and update deemed savings values. In many cases, an empirical econometric approach can deliver an answer with just customer AMI data and program tracking data if the key question to be answered is simply: how many kilowatt-hours (kWh) and kilowatts (kW) is this program giving me? Such empirically-based savings could reduce program implementation and evaluation costs by streamlining or eliminating the ex ante customer application process, provided savings are measurable at the meter.

 

New Analytics Solutions Give Consumers More Energy Choice

— July 13, 2017

Residential consumers are becoming increasingly aware of their energy consumption and are interested in how they can reduce their use, save money on energy bills, and become more environmentally conscious. More and more customers are receiving home energy reports, which detail energy consumed and compare usage to that of neighbors. Opower (Oracle) achieved more than 11 TWh of energy reduction across 100 utility partners with these types of reports. Consumers are also logging into mobile apps that disaggregate devices to help them make smarter choices about where to target energy saving efforts.

Despite increasing efforts and awareness about energy, many consumers still do not know where their energy actually comes from. Most people may have a vague sense of their country’s energy mix and imports, such as the US energy mix depicted in the figure below, or that the UK imports 60% of its electricity-generating fuel. However, when a consumer flips a light switch, turns on their TV, or adjusts their thermostat, the energy that powers those actions is coming from whatever power plant is turned on to meet that incremental demand. This means the energy your light bulb is using could be drawing power from a coal plant, a natural gas facility, or a solar panel.

US Energy Mix: 2016

(Source: US Energy Information Administration)

New Technology Helps Track Generation Sources

In the past, there hasn’t been a method for determining the generation source that is meeting demand in real time. However, a non-profit called WattTime has developed a data analytics software that solves this problem. The software, which was the brainchild of a hackathon event in 2013, detects where the electricity powering the grid is coming from and the actual emissions impacts of people and companies using electricity. Not only does it detect this information, but it can also automatically power devices when energy sources are the cleanest. It can be installed in any Internet-connected device, making it flexible and easy to implement. This tool empowers customers to have a choice in the type of energy they are using and how much they are emitting when they consume electricity. WattTime’s software is gaining traction, having partnered with companies like Microsoft, Energate, and most recently, the Rocky Mountain Institute (RMI). WattTime has joined RMI as a subsidiary organization to foster the transition to a cleaner, more decentralized grid.

Looking Forward to a Cleaner Energy Future

Data analytics solutions like these are empowering consumers to make smarter energy choices, facilitating the transition to a cleaner, more decentralized and optimized grid, and solving challenges associated with reducing carbon emissions. Currently, emissions are calculated based on average factors, not based on the actual emissions that are generated depending on the source providing the next kilowatt-hour of power. As countries and organizations around the world move forward with reducing greenhouse gases, real, data-based information on emissions can help consumers understand how their actions directly affect greenhouse gas emissions and contribute to the overall goal of a cleaner, greener world.

 

AMI Data Brings New Possibilities for Energy Efficiency Measurement and Verification: Part 1

— June 29, 2017

Coauthored by Emily Cross and Peter Steele-Mosey

Utility industry stakeholders have been debating whether the proliferation of advanced metering infrastructure (AMI), also known as smart meters, will change the way energy efficiency program evaluation, measurement, and verification (EM&V) are conducted. Many utilities remain unsure about what is realistically possible. This uncertainty is compounded by the fact that new firms seem to emerge each year, claiming to provide increasingly deep insights into customers’ energy reduction potential (such as appliance-level load disaggregation and building-specific identification and targeting) using little more than consumption data from the utility.

How Can AMI Data Be Used?

In the field of EM&V, what is AMI data good for? How can it be used by utilities, regulators, and stakeholders to reduce evaluation costs, deliver more accurate and precise estimated program results, and improve the effectiveness of program delivery?

To answer these questions, it is helpful to define the two key evaluation-driven use cases for AMI data:

  1. Operational improvements: Early indications of program achievement provide the opportunity for course correction. Due to the continual collection of AMI data, it should be possible to quantify the impacts of changes in marketing approach and customer targeting on energy efficiency achievement more quickly than is traditionally required for program evaluation.
  2. Program impact evaluation: What is the best estimate of the energy and demand savings that a program delivered? This type of information is required to track utilities’ progress against mandated energy efficiency targets, to enable energy efficiency programs to be bid into energy and capacity markets as resources, and to quantify overall program cost-effectiveness.

Part 1 of this blog covers operational improvements, while part 2 will cover program impact evaluation. This topic is covered in detail in Navigant Research’s new report, Utility Strategies for Smart Meter Innovation: Energy Efficiency Measurement and Verification.

Operational Improvements

Utilities are all too familiar with the frustration of waiting for results from evaluators. Typically, a full year of data is required and the evaluation itself may take several months. This lag between implementation and assessment limits the ability of program administrators to course correct underperforming programs or understand how to tailor messaging to maximize the recruitment of high potential customers.

AMI data is collected continually, and several firms have recently come to market with prebuilt software solutions designed to quickly plug and play with this data. In theory and depending on the type of program, it should be possible to obtain ongoing updates of program performance long before the actual evaluation even begins.

These software packages have their limitations and are no substitute for a custom econometric evaluation, as they tend to be one size fits most. Additionally, the innovative approaches they employ sometimes lack the support of academic and professional literature from which econometric approaches benefit.

There is no denying, however, that these prebuilt software solutions can deliver results much more quickly than the traditional approaches. The results may not be sufficiently robust for a regulatory environment, but they may (depending on the program and the vendor) be sufficient to allow program administrators to take greater control of their programs and monitor their progress in near real-time. Program administrators would have the opportunity to make more effective use of program budgets and increase the value of their programs for their shareholders and ratepayers. They could use these software solutions for programs where simply multiplying the implementer‑reported savings by the prior year’s realization rates is not expected to be accurate.

 

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