Continuous Efficiency Improvement


The energy use in commercial buildings is not constant. It changes with the seasons, from year to year, as tenants come and go, and with changes to building components. Building science researchers know that the energy performance of commercial buildings declines over time – different parts of the system start to age and building occupants invariably alter the “optimal” settings. The Consortium for Building Energy Innovation (CBEI) envisions the opposite scenario – buildings that get more efficient over time – in which Continuous Efficiency Improvement (CEI) is standard practice in the construction, retrofitting and operation of commercial buildings. The CEI continuous loop begins with data collection, typically by meters that are installed in the building, scaled for the building’s size and uses. However, data collection could be as simple as gathering utility bills. The collected data is applied to calibrate building models and “tune” them to the building’s real-world behavior.

Initially, the data and building models set a baseline that can be used later to determine whether the building is using more or less energy (when normalized to weather conditions, occupancy, and other factors). At this stage, the CEI starts to get really powerful, giving users feedback on energy efficiency measures (EEMs) that could be installed in the building and confirming whether these EEMs really worked as expected. While quite useful for individual buildings, the benefits of CEI skyrocket when the process is applied to a portfolio of similar buildings. Then, lessons learned from one building can be directly fed into the decision-making process for other buildings in the portfolio, saving time in planning and design. The process loop continues to evolve, so that buildings are reevaluated to determine if new data collection is needed for decision making, models are updated, and other EEMs are considered and implemented.

CBEI has been working with a number of commercial building owners and operators in the Philadelphia region with the goal of developing a standard practice CEI process applicable to their market segments.

Success will help the building owners achieve greater operational competitiveness while increasing their properties asset values, as well as advancing CBEI’s mission. The building owners working with CBEI have portfolios spanning a range of building types and fleet sizes. Despite the differences in commercial building functionalities and fleet size, all portfolios use one universal CEI process loop.

Convenience Store Retail Chain

Project goal: to identify specific packages of EEMs to lower energy operating costs without impacting store sales and operations.

CBEI is partnered with one of the largest full service convenience store retail chains in the United States, which operates in several business segments: convenience, food service, and fuel service. CBEI investigators performed Building Benchmarking of the convenience store portfolio of about 600 company-owned convenience stores in the Mid-Atlantic region, ranging in size from about 4,550 to 6,000 sq. ft. The median energy use intensity (EUI) of the portfolio is approximately 425 kBTU/sq. ft./year, on par with the EUI of a 30,000 to 35,000 sq. ft. commercial office building. Thus the 600 convenience stores use the energy of five times as many medium-sized commercial office buildings.

Results: The full-service convenience stores are internal load dominated. Multi-parameter, regression model for annual and monthly energy use indicated that energy use was strongly tied to store age and floor area, only moderately tied to customer transaction number, and had no significant association with whether or not the store included fuel pumping service. Strikingly, natural gas usage was negligible as compared to electric energy usage and had minimal influence on model results.

Building Metering and Sub-metering was already in place. The electric distribution in a representative number of stores is organized into subpanels dedicated to specific end uses—refrigeration, HVAC, lighting, hot food equipment, fuel pumps—and that data was made available to CBEI researchers for analysis during this project period. The data assisted in detangling the internal load from the envelope and weather related load demand, and in discerning their relative contributions to total energy use.

Energy and Performance Modeling followed, utilizing building design detail, equipment specifications, utility annual energy use, and sub-metered data. Annual and monthly energy utilization simulation models were constructed using EQUEST, Energy Plus, and Integrated Environmental Solutions energy utilization simulation models.

The two whole building energy use regression models will be utilized for new store designs and to predictively identify viable Proposed EEMs in existing stores. The monthly models are also used for Energy Use and Performance Deviation Assessment to identify system control, equipment operation, performance issues, and employee practices that either decrease or increase energy efficient operation of a particular store. For this latter purpose, CBEI used inverse modeling techniques (IMT) to establish expected operational energy use (EOEU) baseline models of specific store configurations using monthly whole-building energy use and temperature data for multiple years.

Upon fleet-wide implementation of the EOEU process, the convenience store fleet (or any similarly operated set of facilities) would be able to identify common operational issues to be addressed and energy efficiency practices that can be generalized to the fleet.

Commercial Building Owner A (small fleet of buildings)

Project Goal: to conduct a retrospective analysis of the energy and financial impact of EEMs Commercial Building Owner A had installed over time in two buildings in its portfolio, a mixed-use building and an office building.

Commercial Building Owner A is a progressive owner of about ten commercial properties in the Mid-Atlantic region. Their properties are a mix of high- and low-rise commercial and mixed-use buildings, mostly medium-sized. The mixed-use building implemented two major EEMs: triple-glazed low-e windows and energy efficient lighting. The office building installed triple-glazed windows and advanced controls for the HVAC system.

Results: CBEI found that the results of implementing EEMs were inconclusive. Analysis of both buildings revealed only slight reductions in natural gas and electricity energy use from pre- to post-EEM time periods, and indicated EEM payback periods of 30+ years.

Commercial Building Owner A is currently in the Lessons Learned phase. CBEI researchers were concerned that previous experience with another Commercial Building Owner A property would foreshadow complications with interpreting results. Former analysis of data at Commercial Building Owner A other property during 2005-2013 showed natural gas savings of about 20% mainly attributed to the installation of variable frequency drives on pumps and HVAC fans.

In this latest evaluation, CBEI was not able to easily identify the impacts of installed EEMs in Commercial Building Owner A’s properties due to measurement uncertainty. This uncertainty resulted from a lack of specific source energy use, because electrical subpanels had not segregated circuits for lighting, plug load, and HVAC components. Further, unrecorded changes in building occupancy made it difficult to use inverse modeling techniques to quantify EEM impacts. The challenges of insufficient submeter data and confounding occupancy changes led to marginal observed energy savings and long payback periods. EEM verification becomes unscientific when occupancy load factors change during the time periods of analyses; using average occupancy load factors and plug load values will simply not yield the accuracy and certainty required to attribute energy utilization changes to a specific EEM.

CBEI researchers are to meet with Commercial Building Owner A management to discuss occupancy records, if available, to attempt to refine the analyses.

Commercial Building Owner B (large fleet of buildings)

Project Goals: to conduct measurement and verification (M&V) in order to evaluate the building energy savings in completed pilot Phase 1 projects in order to apply them to certain Phase 2 buildings.

Commercial Building Owner B owns a large fleet of real estate holdings, primarily large commercial buildings. As part of a unique program, Commercial Building Owner B partnered with the local utility in a matching fund arrangement, independent of CBEI, to transform ten office buildings into “Smart Buildings.” The building retrofit scopes mainly included upgrades to the existing interior lighting system to fully programmable, dimmable lights with automatic occupancy and day-light control systems, installation of smart meters for sub-metering the major building end-use systems, and upgrade of HVAC system controls. The project has multiple phases. As opposed to the two Phase 1 buildings, the four buildings designated for Phase 2 consist of a more a diverse commercial office building stock, especially with regard to building size, complexity and system configuration.

The goals of the smart building program are as follows:

  • to utilize the project sites to act as a living lab to determine the value proposition of energy efficiency related technologies
  • to quantify the potential for energy and environmental savings of EEM implementations
  • to develop the tools and methods of the CEI process for a large scale property owner and developer with resources to implement monitoring technologies across a broad fleet range by type and size

In the Phase I warehouse building, CBEI evaluated the impact of a substantial warehouse energy efficient lighting retrofit coupled to automated occupancy sensors relative to pre-retrofit conventional lighting with manual controls. Both actual energy reduction and the accuracy of the lighting vendor’s proposed energy reduction claims were evaluated. CBEI also used inverse modeling techniques to analyze the success of EEMs in two Phase 2 properties.

Results: Insufficient sub-metering proved to be a barrier to analysis for both Phase 2 projects, but the analysis of the warehouse property EEMs will help to establish best practices in energy efficient operation for all future warehouses in the Commercial Building Owner B portfolio.

Both Phase 2 buildings implemented less sub-metering than Phase 1 properties, making accurate EEM impact analysis difficult. Neither property had thoroughly documented the occupancy load factor. Likely due to these factors, one of the properties showed little EEM impact after IMT analysis. CBEI could not even apply pre- and post-retrofit IMT analysis to the other building, because only post retrofit data was available.

Commercial Building Owner B has recently increased their building portfolio to include more storage facilities, so they see tremendous value in CBEI’s evaluation approach to lighting retrofit projects.

Key Takeaways

The valuable takeaway from these case studies is that successful Deviation Assessment during Continuous Efficiency Improvement requires some basic Performance Monitoring, including clear documentation of occupancy load factors, along with some form of subsystem sub-metering during pre- and post-retrofits periods. As demonstrated with the convenience store portfolio, a facility with a large fraction of plug load energy use requires greater sub-metered energy use data to isolate plug loads.

Given the demonstrated need for more precise energy utilization data in buildings, it is recommended that building codes specify isolation of sub-system wiring in order to facilitate end-use energy monitoring.