Empowering Owner-Operators of Small and Medium Commercial Buildings to Identify Energy Retrofit Opportunities

Abstract

Small and medium commercial buildings account for nearly half of the energy consumed by commercial buildings in the United States. While energy retrofits can significantly reduce building energy consumption, buildings’ owners often lack the capital and experience to perform detailed energy audits and retrofit assessments. The purpose of this paper is to introduce a low-investment, bottom-up and simplified methodology for identifying energy retrofit opportunities that benefit the owners of small and medium sized office buildings In particular, the paper addresses small and medium commercial buildings on a university campus as a proof-of-concept for other owner-operators that have small and medium commercial facilities in their portfolio. The methodology consists of an eight-step framework using publicly-available and simplified tools. While energy audits and retrofit opportunity assessments are not new, a low-cost methodology for owner-operators of small and medium commercial buildings to analyze energy consumption and identify retrofit opportunities represents a contribution to knowledge. A medium office building on a university campus in Arizona served as a case study to validate the methodology. The case study showed a maximum potential energy reduction of an estimated 50%, but the figure varies based on the types of retrofit (deep versus light), energy conservation measures selected and implemented, invested resources, and interactive effects between measures. This methodology is extensible to other owner-operators that have building utility data and would like to perform retrofit opportunity assessments themselves.

1. Introduction

Buildings account for nearly 40% of the total primary energy consumption in the U.S., of which 46% is consumed by commercial edifices [1]. About 95% of the commercial buildings in the U.S. were smaller than 50,000 square feet and accounted for 47% of the energy consumption of the non-mall commercial facilities [2]. Over 45% of such consumption could be saved by an energy retrofit [3]. However, small and medium commercial buildings’ (SMCB) owners rarely have sufficient resources to conduct a comprehensive energy audit by themselves or hire someone to do it [3,4,5]. For example, a Level I energy audit, defined by the American Society for Heating, Refrigeration, and Air-conditioning Engineers (ASHRAE) as a “walk-through analysis”, would cost between six and ten thousand dollars, excluding the cost of implementing any energy conservation measures [6]. Thus, SMCB’s owners may find it challenging to identify the most cost-effective and time efficient and retrofit measures that could reduce their buildings’ energy consumption [5,7,8,9,10]. This study introduces a framework that combines a bottom-up energy audit approach with a publicly-available web-based energy retrofit toolkit, the Commercial Building Energy Saver [11], to identify and predict savings resulting from energy conservation measures. The authors present this work through a case study project.

This paper addresses a gap in the literature related to energy retrofits for small and medium commercial buildings. While studies do address energy retrofits in SMCB (i.e., [2,5,8,12]), many of these discuss time-consuming and laborious approaches. Moreover, such studies usually involve technical assistance for SMCB owners. While the study presented herein was conducted by the authors, it leverages publicly available data and tools, alongside the owner’s utility bill, to analyze energy consumption and identify retrofit opportunities. To the authors’ knowledge, this study is the first of its kind, in that it develops a low-cost methodology that SMCB owners themselves can implement to identify buildings that need retrofits as well as potential retrofits for these buildings. Moreover, in targeting owner-operators, this methodology addresses two oft-cited barriers to implementing energy retrofits in SMCBs [8,9,12,13,14]: (1) that SMCB users may not own the facility, and thus may not capture the energy cost savings and (2) that the SMCB user’s core business is not energy efficiency, so by extension, time and effort spent on energy retrofits would be deemed “non value added.”

2. Literature Review: Low-Cost Methodologies for Energy Audits of Buildings

This section presents a literature review on a variety of low-cost methodologies for energy audits of commercial buildings.

2.1. Energy Audits

Building energy audits support identifying energy waste and uncovering opportunities for improving energy efficiency and enhancing sustainability within the building. The process entails analyzing various elements of the building that consume energy, such as heating and cooling systems, lighting fixtures, and appliances; the process also involves documenting construction details, i.e., envelope materials and insulation thickness, to the extent possible [6,15]. The primary objective of an energy audit is to identify actionable steps to minimize waste and enhance overall efficiency without disrupting the comfort of people or negatively impacting performance of a building [16]. By conducting these audits, building owners or operators gain valuable insights into their current energy consumption patterns and make well-informed decisions regarding their energy investments. ASHRAE defines 3 levels of energy audits [6]:

• Level 1: Walk-through assessment
• Level 2: Energy survey and analysis
• Level 3: Detailed analysis of capital intensive modifications

Multiple researchers have documented how energy audits can, and do, support improved energy efficiency in buildings. Alajmi et al. [17] investigated the energy audit process, revealing that time, budget constraints, building complexity, and client requirements are critical factors in determining which level of energy audit is appropriate for a given owner. Alajmi’s work [17] highlights the need for an energy audit team to perform assessments using ASHRAE’s Level 1 and Level 2 audits, as well as the need for the owner to develop their own set of retrofit opportunities. Indarto et al. [18] takes a similar approach to energy auditing as ASHRAE and leverages energy auditing to assess the viability of using non-traditional refrigerants. Mudhusudan et al. [19] discuss the efficacy of energy audits for identifying efficiency opportunities in a medium commercial building, and highlight how energy audit teams with expertise in building science can identify energy savings opportunities of nearly 50%. Similarly, Wahhaj et al. [20] present encouraging energy and water saving results for an office building in Saudi Arabia that were identified during an energy audit. Finally, Glick et al. [21] explored how virtual energy audits could alleviate some of the cost and time burden associated with traditional energy audits, and concluded that while effective, the virtual audits still require an energy audit team.

2.2. Infrared Thermography and Thermal Imaging

One tool that can support building energy audits is infrared thermography (IRT). IRT can be used to locate: (1) regions in a building where heat and air are lost, (2) sources of moisture, (3) building components. This method works by measuring the heat radiation that a target emits [22]. IRT is performed with an infrared camera that is capable of measuring heat radiation [22,23]. The camera, in turn, provides images that map thermal gradients in the image, allowing users to identify “hot spots” in the images. These hot spots often correlate to energy inefficiency, and illustrate where building insulation, ducting, or piping should be repaired or replaced to mitigate temperature gradients and reduce energy consumption. Indeed, Acquaah et al. [24] discuss how thermal imaging can support occupancy based ventilation in buildings, supporting increased energy efficiency. Youssef et al. [25] explore the potential for completing energy assessments on multiple buildings leveraging thermal images collected by unmanned aerial vehicles in Saudi Arabia.

Vibrothermography, also referred to as ultrasonic thermography, is a specialist application used in building diagnostics [22]. This technique induces a temperature reaction in the substance under investigation using ultrasonic pulses [22,26,27]. To capture the thermal reaction, an infrared camera is used. At the locations of the material’s flaws, these vibrations cause heat to be produced through fiction. The infrared camera then detects the heats, creating a thermal image that can be examined to find and locate the flaws [22,26,27,28,29,30]. Vibrothermography is particularly useful for finding flaws in pipes or ducting. These flaws, in turn, can lead to increased use of energy to move water or air through the compromised or damaged section of pipe or duct, respectively.

In addition to use for energy auditing, thermal imaging can support routine maintenance, e.g., locating the positions of the hot and cold pipes inside building walls [31]. This method lessens the need to rely on speculation on their location when pipes need to be inspected, repaired, or replaced [32]. Similarly, thermal imaging can be used to determine the location and state of subsurface utility lines [33].

Ultimately, infrared and thermal imaging technologies aim to provide intelligence about the thermal performance of a building envelope. In particular, previous work explored how to measure a building’s transmittance parameter, or U-value, in-situ [34]. More recently, advancements in technology support determining a building’s U-value via low-cost approaches, e.g., the Hyper Efficient Arduino Transmittance-meter (HEAT) [35], which allows building owners and operators to “monitor [the building’s] U-value based on low-cost technology.” The developers of the HEAT-meter note that correct placement of the meters is vital to obtaining accurate readings of the building’s U-value, and they suggest approaches for ensuring that U-value calculations are reliable even when the HEAT-meter is not placed in the optimal location [36].

2.3. Non-Destructive Testing (NDT)

Non Destructive Techniques (NDT) have emerged as indispensable tools for energy audits, determining the condition of structures and identifying areas for improvement [37]. These techniques play a crucial role in creating thorough energy management strategies since they are competent at identifying structural problems and energy leaks without impairing a building’s performance. When it comes to the necessity of renovations, the possibility for energy conservation, and the attainment of sustainability goals, they guide informed decision-making processes.

The authors highlight four NDT techniques and their relevance for building energy efficiency:

• Infrared Thermography (IRT): This method is employed to find thermal irregularities, often in building envelopes. It is a non-contact approach for detecting thermal bridges, insulation flaws, and air leaks; it uses infrared cameras to monitor the temperature distribution on an object’s surface [32,38].
• Ultrasound: High-frequency sound waves are used in the ultrasound technique to find flaws or changes in a material’s characteristics. Its primary application in buildings is finding defects or damage to a building’s structural system [28].
• Through Wall Imaging Radar (TWIR): TWIR is a technique that detects objects or defects behind walls by using radar signals. It can be used to detect structural defects as well as to locate pipes, wires, and other hidden objects within walls [39,40].
• Light Detection and Ranging (LiDAR)/Laser Scanning: LiDAR is a remote sensing technique that measures distances using light in the form of a pulsed laser. It can be used to construct high-resolution 3D models of buildings and their surroundings in the context of building inspections. This is accomplished through the use of Mobile Laser Scanning (MLS), a system that combines a Global Navigation Satellite System (GNSS) device, an Inertial Measurement Unit (IMU), and an RGB camera. With a high density of dots per square meter, this configuration can deliver a high level of accuracy, up to the millimeter level [41]. While this technique does not support energy assessments, it can be invaluable at providing a clear as-built condition model that energy audit or energy assessment teams can use when preparing plans for a building’s retrofit.

While each of the aforementioned NDT techniques can support building energy efficiency to some degree, all require a skilled engineer or technician to conduct the test, interpret the results, or both.

2.4. Gap in the Literature

While substantial study has been conducted to investigate energy efficiency and optimization measures in commercial buildings, there appears to be a large void in the literature with regard to techniques that are designed to be implemented by building owners or operators themselves. In particular, there is an absence of comprehensive studies on how the owners and operators of small and medium commercial buildings may be taught and equipped to discover and capitalize on energy opportunities themselves, rather than by hiring an energy auditor or energy consultant. Given the fact that owner-operators of small and medium commercial buildings have a clear financial interest in having their stock operate more efficiently, this seems like a missed opportunity. This study intends to close this gap by creating and testing a methodology for small and medium commercial building owner-operators to use to identify energy savings opportunities themselves, leveraging their own data about these facilities.

3. Materials and Methods

The authors leverage a single case study approach for this paper [42]. The case study presented herein is of a medium-size office building in Arizona. A sustainability consulting firm occupies about 70% of the building’s net area and classrooms occupy the remaining 30%. The building was constructed in 1966, and retrofit in 2008, achieving the United States Green Building Council (USGBC) LEED (Leadership in Environmental Design) Silver for Existing Buildings. The renovation and retrofit included energy, water and material efficiency solutions, such as solar panels, wind turbines, water efficient faucets and occupancy and photo sensors on the lights. However, the roof has limited area for a solar energy system, and the wind turbines generate an insignificant amount of energy. Thus, the amount of renewable energy generated is insignificant.

3.1. Applicability of This Framework

The authors tested the 8-step methodology on a 51,000 square foot office building in Arizona, and the results are presented in this paper. Although CBES has proven to be effective in the case study, it does not achieve the same level of detail and reliability of a professional energy audit. For instance, a professional energy audit would result in a more accurate projection of Return on Investment and provide better and more comprehensive advice and potential solutions to a building owner, outlining the best combination of energy conservation measures. A professional auditor has the latest knowledge of the state-of-the-art equipment and energy solutions that an online toolkit may not necessarily contain in its database. Therefore, an online toolkit (e.g., [11]) does not replace a professional energy auditor. However, there are many cases where professional energy audits are financially prohibitive, such as in smaller and medium sized commercial buildings. These building owners may lack the financial resources to engage professional energy auditors. The framework presented in this paper aims to assist these types of buildings to maximize the benefits of energy retrofits without the financial burden of an audit.

3.2. Objective

This paper documents the development of a low-investment framework to guide the owner-operators of small and medium buildings in making decisions on building energy retrofits that would reduce building energy consumption without incurring significant costs. Colleges and universities are ideal test cases for small and medium commercial building energy retrofits because these organizations generally own and operate SMCBs; they have access to building plans and utility data; they may have submetered energy data; and such organizations have human resources, including facilities and maintenance staff, capital projects staff, and students and faculty that can collect and analyze utility data to identify energy retrofit opportunities. At the same time, these owners may lack capital for external energy audits.

The framework leverages existing data collection and analysis tools to develop the simplified energy audit approach for SMCB owner-operators that do not have the resources to undertake an energy audit or invest in the labour or materials required for one of the other low-cost approaches (discussed in Section 2) to identify energy retrofit opportunities. To further support implementation, the authors chose to leverage a Microsoft (MS) Excel spreadsheet for data analyses and to generate outputs presented in this paper. This simplifies the framework implementation, as most people are familiar with MS Excel. MS Excel appears to be a ubiquitous tool for data analysis and does not appear to “intimidate” users the way that some more efficient computational tools, e.g., MATLAB, Python, may (e.g., [43,44,45]). As such, the authors felt that MS Excel would be most accessible for SMCB owner-operators.

While not explicitly the objective of this paper, the authors note the approach presented herein could be extended to other large owner-operators, e.g., owners of retail stores, banks, healthcare facilities, etc.

3.3. Data Collection

Extensive data was collected to develop the approach. The data reduces the need for assumptions and enhances the reliability of the outputs. Both the as-built plans and building operation field data were collected, as both influence the overall energy consumption of the building. Field data is used to identify possible discrepancies with the as-built plans.

Figure 1 illustrates the data types, relationships and sources used for the project. The facility manager for the building provided the authors more than four years of energy bills and the corresponding utility data, which allowed the authors to identify the trend of energy consumption over time. The building’s operational schedules were also provided to the authors. The building has diverse uses (e.g., classrooms, offices, libraries and restaurants) that affect the performance and function of different energy systems. The operational schedule is used to improve the energy consumption estimates. Other information used in the analysis includes the year of construction, and the building’s gross and net area.

Some of the information is missing from the as-built plans, e.g., location and number of electrical appliances; additional field data was collected to fill those gaps, including the number and power consumption of MELs, and the heating, ventilation and air-conditioning (HVAC) system power consumption. The power consumption of each piece of equipment is calculated from the equipment specifications. The field data also included surveys on the building’s occupants. The survey provided extensive details on how the occupants use different electrical appliances (e.g., how often they use each device if/when they turn it off or set the standby mode, whether the room temperature is comfortable). The survey’s results improve the reliability of the assumptions of the equipment operations schedule. The surveys also enhance the understanding of potential effects of power management strategies.

Electrical appliances’ power consumption is difficult to collect in a short period of time, and thus the authors relied on other sources, such as the Building Energy Data Book [46], and appliance manufacturer’s websites, to collect information, including average energy consumption of various appliances and their efficiencies. The Energy Star website has a Product Finder tool for electrical appliances [47] and manufacturers’ websites contain extensive power consumption data for the products they made. Rather than collecting data for each and every MEL, the authors focused on the most commonly used MELs in the building.

Collecting data from similar buildings in the same region is also highly recommended as energy consumption patterns are driven by building type and regional weather. An efficient manner to identify anomalies in a building’s energy consumption is to compare it with similar buildings located in the same region. Anomalies are detected from the collected data by comparing the energy use intensity (EUI), electricity energy intensity (EEI), and energy consumption by end use of the case study and the comparison building. For the case study, the authors collected data from other buildings in the same institution to use as a comparison group. When data was missing for the comparison group building, the authors supplemented data with other publicly available data such as power consumption data from websites, and end use data from the U.S. Commercial Buildings Energy Consumption Survey (CBECS). CBECS contains the average energy consumption data for commercial buildings in different regions and building characteristics [48]. The data for CBECS is collected by U.S. Energy Information Administration (EIA) through questionnaires and interviews of over 1200 buildings across the United States to create a statistically significant sample of the U.S. building stock.

The CBES Toolkit

Even though there are many online retrofit assessment toolkits [49,50,51], CBES is considered the most comprehensive and thus it is selected for this project [3]. It offers a quick and reliable retrofit assessment and comprises a database of energy efficiency performance (DEEP), that gathers over ten million Energy Plus simulation results [3]. The authors found CBES particularly attractive because it provides climate-specific guidance, at the zip code level, for SMCB owner-operators considering a retrofit. Moreover, rather than providing a “laundry list” of potential measures like other tools may do (e.g., [52,53]), CBES provides a limited set of ECMs that are recommended for users based on building geometry, climate, and potential payback period.

CBES results include: (1) a group of recommended Energy Conservation Measures (ECMs) based on a buildings’ characteristics, (2) estimated energy cost savings, (3) an estimated retrofit investment, (4) payback period, and (5) the Indoor Environmental Quality (IEQ) impact for each ECM. Finally, the CBES generates an Energy Star score for benchmarking purposes. Note the authors package ECMs provided by CBES, as packages allow owners to take advantage of synergistic behaviors (i.e., reducing lighting in the facility results in a lower heat load, which in turn, allows for the air conditioning to run less frequently). Generally, packages of ECMs are more effective than installing various single-system ECMs over time [54,55,56].

4. Results

This section documents the development of the eight-steps in the simplified energy audit processes (see Figure 2). The authors discuss each of the steps in detail and describe how they applied them to the case study. The paper discusses the eight audit steps progressively, from the first to the eighth. However, some of the data are repeatedly used in more than two of the steps, thus the order of discussion was altered in some cases. The data were collected and then populated in Microsoft Excel spreadsheet. It was cleaned before being used in the analyses.

4.1. Step 1: Compare Sample Building with Similar Buildings

The first step is to compare the building’s eui and eei with other similar buildings’ in the same region and in the cbecs database. the building’s eui is obtained by dividing the total energy consumption (including heating and cooling loads) by the building’s gross area. The building’s EEI is the ratio of total electricity consumption over the net area. If the building’s EEI or EUI is high compared to the average of similar buildings that suggests the building is a good candidate for a potential energy retrofit.

Table 1. Summary of project information for the case study building and similar buildings. All Data from 2014.

Information	Case Study	Building A	Building B	Building C	CBECS 2012 (US Mountain)
Year	1966	1950	1969	2005
Gross Area (sqm)	4806	4107	8799	15,232
Net Area (sqm)	2406	2383	4893	7269
% classrooms	29%	36%	25%	0%
% office	70%	52%	64%	65%
% research	0%	0%	2%	0%
% class lab	0%	0%	6%	0%
Electricity Energy Intensity—EEI (KWh/sf/year)	226.58	85.36	152.85	127.23	149.62
Gross Energy Use Intensity—EUI (KBtu/sf/year)	1865.92	516.99	708.48	553.37	902.02
Electricity (KWh) %	20.73%	33%	41%	37%

summarizes the data collected for the case study, along with data collected for similar buildings in the same region with the same functions/uses and rates (e.g., buildings with a mixture of offices, classrooms and laboratory spaces). Table 1 also lists CBECS data for the case study’s building type. Note the comparison buildings have different ages, area, and construction materials than the case study building; thus, variations in the EEI and EUI are expected. Nonetheless, the case study building’s EEI and EUI are significant compared to the CBECS average and the other similar buildings in the region. The most intriguing result was the comparison of energy for end use with other similar buildings. The case study spent 26% of its energy for indoor heating, compared with less than 2% spent on heating by the comparison buildings. Even if the same data for similar buildings was not available, this high percentage of indoor heating shows inefficiency, particularly in Arizona’s hot arid climate. As a consequence, the case study’s EUI is twice as large as the commercial buildings’ average for the same region.

Table 2. Case Study Building’s energy consumption in 2014. Values converted to MWh.

Total Energy Consumption MWh	% Energy	Total Energy Consumption MWh
Heating (mmBTU to MWh)	700	26.62%
Cooling (TonHrs to MWh)	1385	52.66%
Electricity (MWh)	545	20.72%
Total (MWh)	2630	100%

summarizes the case study’s energy use in 2014 by end use, including heating, cooling, and other electricity uses. The data was collected from the building’s energy bills. The opportunity for energy savings in the case study was evident, and the investigation proceeded.

4.2. Step 2: Analyze the Energy Bills

The energy bills were provided for both electricity and natural gas. The building involved in the case study uses natural gas sfor heating, and electricity for cooling. The cooling energy is separated from the other uses (e.g., MELs, lighting, and HVAC equipment) in the energy bills. The authors first analyzed both heating and cooling energy loads and then the electricity loads.

Figure 3 illustrates how the case study building’s consumption of cooling electricity, natural gas for heating, and electricity for other uses varies over time; the lack of consistent consumption, and indeed, rising cooling and heating consumption, indicate that the case study building should be audited. While it is possible that changing weather conditions (i.e., hotter summers and colder winters) account for the increased cooling and heating consumption, this is not evidenced in the data. For example, the case study’s energy bills exhibit strange patterns for its natural gas use.

Table 3. Case study building’s natural gas consumption for indoor heating, 2011–2014 (mmBTU).

Months	2011	2012	2013	2014
January	20,281	32,220	80,099	115,476
Febuary	21,872	25,107	44,570	306,051
March	25,113	463,703	46,821	260,130
April	2043	144,801	8268	18,449
May	26	30,172	199	0
June	0	23,504	18	0
July	0	15,128	0	0
August	0	23,853	0	0
September	0	29,843	0	0
October	123	23,572	0	0
November	16,380	1527	0	0
December	46,510	51,765	0	0

presents the case study’s monthly natural gas consumption from 2011 to 2014. In August 2012, the natural gas consumption was 81.39 mmBTU (or $1033.66) for indoor heating, however, the average outdoor temperature in Arizona was higher than 100 °F (37.8 °C) during the same period. (Note that the lowest temperature in Tempe, Arizona in August is over 30 °C.) Following 2011, the heating system clearly stops working properly, evidenced by high heating energy consumption in the summer of 2012, and no heating energy consumption in the fall and winter of 2013 and 2014, when outdoor temperatures in Arizona often warrant indoor heating.

In fact, the authors discovered during the surveys that commercial building owners rarely examined their utility bills and thus, they rarely explored potential savings. The heating issue cost the owner more than $80,000.

Repair to the leaking and defective valves, dampers and metering would eliminate energy waste. Reducing energy use in the spaces would also eliminate energy waste without sacrificing occupants’ comfort. The cost of maintenance will continue to rise as the building ages (already 50 years old at the time of survey), and the manager suggested that replacing the valves could potentially save $20,000 on the energy bills annually.

4.3. Step 3: Analyze the Electricity Consumption (HVAC Equipment)

Electricity is used to run HVAC equipment and most of the power is consumed by the motor. The research team collected energy consumed by and the specifications of the HVAC equipment. Figure 4 shows the label that contains the specification of the single Air Handling Unit (AHU) used by the case study building. Equation (1) is used to convert the equivalent power factor, stated in the specification, into power consumed by the equipment. The Air handling unit (AHU) converted power consumption was 76.48 KW.

$$\begin{gathered} \mathrm{P}\left(\mathrm{W}\right)=\sqrt{3}\times \mathrm{P}\mathrm{F}\times I\times V; \mathrm{where}\mathrm{P}\left(\mathrm{W}\right)=\mathrm{Power} \left[\mathrm{Watts}\right]; \mathrm{PF}=\mathrm{Power\; Factor}; I=\mathrm{Current} \left[\mathrm{Amps}\right]; \\ V=\mathrm{line}-\mathrm{to}-\mathrm{line} \mathrm{voltage} \left[\mathrm{Volts}\right] \end{gathered}$$

(1)

Equation (1): Determining power required for various equipment.

Next, the authors used the operational HVAC schedule data to calculate the AHU’s annual electricity consumption in KWh.

Table 4. Case study building’s HVAC operation schedule.

Regular Schedule Hours (36 Weeks)
Week day	Start time	End time	Hours/day
Sunday	7:00	16:30	8.5
Monday	2:50	21:30	18.5
Tuesday	5:50	21:30	15.5
Wednesday	5:50	21:30	15.5
Thursday	3:50	21:30	17.5
Friday	5:50	21:30	15.5
Saturday	7:00	16:30	8.5
Winter and Summer Breaks (16 Weeks)
Week day	Start time	End time	Hours/day
Sunday	7:00	16:30	8.5
Monday	0:30	17:30	17
Tuesday	3:30	17:30	14
Wednesday	3:30	17:30	14
Thursday	3:30	17:30	14
Friday	3:30	17:30	14
Saturday	7:00	16:30	8.5
Total hours			5022

presents the case study’s HVAC operation hours. Based on that schedule, the AHU consumed 384,082.56 KWh annually.

The authors compared the assumed HVAC operational schedule with the actual building occupancy hours. The comparison assists the authors in better identifying energy savings opportunities, and the authors focused on the weekdays, where saving could be maximized. The operational schedule showed that the HVAC system started early in the morning twice a week during the regular schedule and five days each week during the summer and winter breaks. Classes start at 7:00 in the case study building, so anytime the authors note the system on prior to 5:00 raises concern, especially since these earlier operating hours accounted for about 30 thousand KWh, or about $30,000 a year. The energy manager explained that the HVAC needed to start early twice a week to reach ambient temperatures for the occupants, as the building had severe thermal mass issues. Based on the thermal mass issues, the authors recommended a comparison between the costs of a retrofit on the thermal isolation systems, and the energy cost savings associated with such a change.

The weekend operating hours also posed an opportunity for energy savings. While the building’s occupancy was significantly reduced during weekends, the HVAC schedule was not changed. Our calculation suggested that changing the HVAC weekend operation schedule would result in and estimated 80,000 KWh (or $80,000) of saving annually.

Other than the schedule inconsistencies, the HVAC equipment’s annual electricity consumption shown in the case study accounted for 70% of the total building’s electricity consumption in 2014. This is almost twice the average of the other commercial buildings (36%) in the U.S. Mountain region according to the CBECS 2012 [48]. The age of the HVAC system, the lack of adequate maintenance, and the efficiency affected by the building’s thermal mass were possible causes this increased energy consumption.

4.4. Step 4: Analyze the Lighting System Electricity Consumption

Lighting consumes a significant amount of energy. Energy consumed by lighting fixtures is simpler to calculate than plug and heating/cooling loads. The authors collected data for the number of fixtures, lamps per fixtures, individual lamp power consumption, as well as the lighting schedule (from the building’s operating and design schedule). The schedules were separated based on the type of building activities during different time periods and at different locations (e.g., classrooms, offices, common areas). The total energy consumed by lighting was calculated by multiplying the total number of lighting fixtures by the power load for the appliances.

Table 5. Case study building’s lighting electricity summary.

Location	Qty Fixtures	Qty Lamps	KW 1	Hours/Year 2	KWh	%
Conference Rooms	46	92	0.015	1560.00	2152.80	4
Classrooms	75	150	0.015	2880.00	6480.00	12
Common Areas	239	478	0.015	3160.00	22,657.20	42
Office areas	257	514	0.015	2880.00	22,204.80	42
Total (KWh)					53,494.80	100
Savings in KWh (occupancy sensors, 30%)					16,048.44
Total consumption with savings (KWh)					37,446.36

¹ The case study has T8 (15 W) fluorescent lamps. The building to be retrofitted may have several different types of lamps. In this case, a percentage of the total quantity should be calculated or assumed for each type of lamp. ² The hours/year are divided by each building location type (conference, classrooms, office, common areas), because each has its own schedule. This itemization is very important for the reliability of the results.

shows the lighting system electricity consumption for the case study building and shows how the annual consumption was calculated. After occupancy sensors were installed in the building, the authors assumed 30% lighting energy savings.

4.5. Step 5: Analyze MELs Energy Consumption

The fifth step is to analyze the energy consumption of MELs. MELs are grouped by their locations and the function(s) of each location (e.g., classroom). It is critical to collect and use data on equipment’s active and standby power modes, as the energy consumed in each mode is different. The energy consumed by different groups of MELs are combined with the occupants’ behavioral survey to estimate the appliances’ active and standby periods.

Table 6. Case Study Building Conference Room MELs consumption.

Equipment (Qty and Type)		Hours/Year 1	Active KW	Low Power KW	Total KWh
4	Projectors	1560	0.33	0.003	973.44
3	Desktops	1560	0.04	0.03	175.50
7	Monitors LCD 19″	1560	0.025	0.0003	205.57
Total consumption (KWh)					1354.51

¹ Schedule based on 30 h operation/week. Equipment assumed active 75% of the time.

,

Table 7. Case Study Building Equipment and Consumption.

Equipment (Qty and Type)		Hours/Year 1	Active KW	Low Power KW	Total KWh
5	Projectors	1269	0.207	0.003	989.82
7	Desktops	1292	0.04	0.03	339.08
46	Laptops 2	1292	0.03	0.02	1641.12
Total consumption (KWh)					2970.02

¹ Real classroom hours for the schedule of 2014. Includes 15-min intervals between classes. Equipment assumed to be 75% of the time in the active mode. ² The quantity of laptops was based on the assumption of 30% of students for each class (using real data for students’ enrollment).

,

Table 8. Case Study Building Common Areas MELs consumption.

Equipment (Qty and Type)	Hours/Year	Active KW	Low Power KW	Total KWh
2 Drinking Fountains				868.70
2 Elevators	3160	0.08	1.332	8339.11
Total consumption (KWh)				9207.81

and

Table 9. Case Study Building Office Area MELs consumption.

Equipment	Quantity (No.)	Active KW	Active Mode %Time	Low Power KW	Low Power %Time	Hours/Year	Total KWh
Monitors LCD 19″	201	0.025	65%	0.001	35%	2080	6940.13
Desktops	110	0.040	35%	0.030	35%	2080	5605.60
Laser printers	21	0.78	10%	0.004	90%	2080	3561.93
Coffee Makers	5	1.000	2%	0.070	98%	2080	921.44
Inkjet printers	4	0.385	10%	0.001	90%	2080	327.81
Fridges							660.00
Microwaves	4	1.500	8%	0.005	92%	2080	1036.67
Fax machines	3	0.220	1%	0.003	99%	2080	26.77
Oven	2	2.400	10%	0.000	90%	2080	998.40
Plotters	1	0.170	1%	0.120	99%	2080	250.64
Dishwasher							120.00
Total Consumption (KWh)							20,449.39

show the MELs electricity calculations for the case study in the conference rooms, classrooms, common areas, and office areas. Figure 5 illustrates the electricity consumption for every office appliance involved the case study.

Table 10. Data sources for equipment power consumption.

Equipment	Data Source
Monitors LCD 19″	[57] 1
Desktops
Laptops
Plotter
Fridge	[48]
Coffee Maker
Dishwasher
Microwave
Oven	[58]
Laser Printers	[59]
Inkjet Printers	[59]
Elevators	[60]
Drinking fountains	[61]
Projectors	[62]

¹ Monitors and desktops have the largest consumption among the MELs in an office building. This study is a comprehensive investigation of office power equipment, with emphasis on computers and their monitors.

lists the data sources for each appliance. A building owner could simulate the occupants’ energy use behavior and consumption using a similar technique to that shown in Table 10, focusing on active and standby energy modes.

4.6. Step 6: Measure or Estimate Overnight Loads and Phantom Loads

The overnight load describes energy consumed by the building when it is not occupied (i.e., in an office building overnight). While some appliances are normally turned off after working hours, others have to stay operational (e.g., refrigerators and elevators). These loads can be estimated by determining the types of appliances that contribute to the overnight load. Such data could be collected through smart meters (often installed by utility companies). Short interval data on electricity consumption, normally collected through sensors and smart meters, would be extremely helpful to accurately estimate such load. As the case study building has an extensive network of real-time sensors and an energy management system, such data is readily available. Smaller commercial buildings would have to rely on their utility companies for such data. The overnight load could also be estimated by determining the operational appliances’ after work hours and their standby mode energy consumption. The overnight load also generally includes “phantom” loads or loads that do not appear to be required for building operations, but still contribute to energy consumption in unoccupied hours. For example, a cell phone charger that is plugged into a wall, but not charging a cell phone, would be considered a phantom load.

Figure 6 shows electricity consumption of the case study building in April 2014. The shaded regions represent the phantom loads, or the load in the building during unoccupied hours. As expected, the phantom loads are nearly constant over the course of the month. This may indicate that the building is performing well, as would be the case if the ~25 kWh load/night was what was required for emergency and 24/7 equipment. However, based on discussions with the facility management staff, it seems that this load should have been closer to ~15 kWh/night based on the equipment in the building. Thus, the phantom loads, or those that are not accounted for, make up ~40% of the overnight load in the case study building. and energy analysis was performed for several months on this load. This overnight load, which includes phantom loads, accounted for about 86,000 KWh (or $8600) annually.

4.7. Step 7: Summarize the Total Electricity Consumption by End-Uses

Step 7 combines the analyses and results from Steps 3 through 6. This process broadens the comprehensive understanding of energy used in a building, as combining the results provides a more complete picture of how energy is used. The combined results are then summarized and compared with the results from similar commercial buildings. The gaps would shed light on potential energy saving opportunities.

Table 11. Case study building’s electricity consumption by end use (based on 2014 data).

Equipment	KWh	% Total	CBECS 2012 1
MELs
office	20,449.39
classrooms	2970.02
common areas	9207.81
conference rooms	1354.51
Total	33,981.73	6%	36.83% (18.69–64.26%)
Lighting
office	22,204.80
classrooms	6480.00
conference rooms	2152.80
common areas	22,657.20
Savings	30%
Total	37,446.36	7%	10.61% (9.22–11.83%)
HVAC system
AHU	384,082.56	70%	47.56% (31.40–52.31%)
Overnight loads
Total	86,215.38	16%	20.73% (15.30–25.58%)
Others
Total	3410.37	0.6%
Total consumption	545,136.40	100%	100%

¹ The data between parentheses is the variation range according to the different building’s characteristics. The number before the parentheses is the average consumption rate for the “all buildings” category.

reviews the case study’s combined energy consumption for its end uses, and the comparison to CBECS 2012. As the table indicates, the overconsumption by the HVAC system distorted the comparative analyses with the CBECS data. Additionally, the photo and occupancy sensors for the lighting system, and extensive amount of natural light let into the building, decrease the energy consumed by lighting. Since most of the MELs are Energy Star certified, and the majority of these were turned off after working hours, the phantom load and the period on standby mode were relatively insignificant. The occupants were found to be extremely environmentally conscious, as most of them worked for a sustainability consulting firm, and such behavior might not be exhibited in other buildings.

The sum of the energy consumed across end uses should be equivalent to the energy consumption recorded by the utility company. Any gap between the energy documented by the utility company and the actual end-use would require additional investigation to identify the sources of such a gap. Some of the differences could be due to the use of other energy sources, such as batteries, and on-site renewables.

4.8. Step 8: Use a Retrofit Toolkit to Analyze Potential Savings

The final step is to combine Steps 1 through 7 to analyze the energy savings potential of various packages of ECMs. This is done leveraging an MS Excel sheet that generates an ECM-based output using the data collected from Steps 1 to 6. The sheet also uses selective CBES data, such as building type, area and year built, and investment criteria (e.g., maximizing cost savings, minimizing payback period, costs less than the maximum budget), regional energy price, and monthly energy data. CBES also incorporates choices for a preliminary or detailed retrofit analysis. More detailed analysis would require additional data, such as building geometry, façade materials, internal energy loads, and HVAC system specifications. The collection of this extensive amount of data would require additional effort (and perhaps support from energy professionals), financial resources, and time commitment. The authors opted to create and use a simplistic retrofit analysis sheet based on simpler data so SMCB owners can use the MS Excel sheet with a limited investment of time.

Note that CBES was exclusively developed for California climate zones. However, the case study building is located in the same climate zone as the city of El Centro (CA), so CBES is applicable and provides useful information for the comparison of retrofit options. The El Centro zip code is used to match with the CBES’s buildings so that similar buildings in the same region would be identified for the project. The authors found 10 ECMs from CBES for the project as shown in

Table 12. CBES analysis results for the low-investment retrofit of the case study building. The IEQ impact explanations are quoted from the CBES results.

ECM	IEQ Impact	Energy Savings (MWh)	Investment (USD)	Payback (yrs)
#12 Add HVAC economizer	Adding an economizer will increase time-average outside air ventilation, often by more than a factor of two. This can improve indoor air quality. In office settings, studies found that more outside air can reduce sick building syndrome (SBS) symptoms and improve work performance. Similar benefits may also apply to retail and mixed-use buildings.	155.23	21,924	0.9
#17 Apply roof coating with reflectivity	-	4.64	20,768	19.0
#18 Efficiency upgrade of the gas storage water heater	-	7.53	3246	12.7
#28 Apply top floor ceiling insulation (R30)	Better insulation may improve thermal comfort. But note that adding insulation can disturb existing building materials that may contain asbestos. Contact a trained and accredited asbestos professional to determine if this is a concern.	9.38	46,122	50.8
#30 Add programmable thermostat	Heating and cooling setpoints should be within the thermal comfort range. Research studies found that the optimum temperature for office work performance is between 68 and 74 °F.	87.47	0	N/A
#31 Install daylighting sensors	Achieving good daylight control can be challenging because of the vast difference in lighting level preference among people. Automatic lighting controls need to be simple to use and allow occupants to manually override to ensure satisfaction.	220.49	1784	0.1
#33 Replace air filter	Select low-pressure air filters that have the same or better particle removal efficiency because there are substantial health benefits by reducing indoor particle levels.	170.52	0	N/A
#34 Improve water tank insulation	-	1.18	86	2.1
#35 Upgrade to gas instantaneous water heater	If the current gas water heater is located indoors and is natural draft, then changing to a high efficiency equipment with forced exhaust venting will reduce the risk of combustion pollutants leakage to indoors.	5.17	0	N/A
#36 Add ceiling fans	The use of ceiling fans can maintain thermal comfort at higher temperature (28 °C or 81 °F, assuming 50% relative humidity). If there is no existing air conditioning, ceiling fans can help improve thermal comfort.	486.40	19,717	0.3

. CBES also generated an Energy Star benchmarking score of 8 based on the case study’s EUI (out of a possible score of 100, where a higher score indicates a more efficient building). The minimum score for a building to qualify for an Energy Star certification is 75. The Energy Star score of 8 shows that the case study is extremely inefficient.

CBES provides data on energy and cost savings, estimated investment cost, and payback periods for each ECM (other than the description and indoor environmental quality—IEQ). However, CBES does not itemize the costs so it should only be used for reference. Some ECMs do not require cost, e.g., replacing the air filter. Some of the extremely low-cost solutions could save over 7% of the total energy consumed annually. Small and medium sized building owners can choose from combinations of ECMs that they can afford and that will maximize their financial benefits. The authors used ECMs 12, 31, and 33 to measure the potential energy savings, and found that the total energy savings was estimated to be 545 MWh (20%) annually, with an estimated initial investment cost of $23,700.

Other than the ECMs recommended by CBES, the authors also calculated the energy saving opportunities that the building owner was already aware of.

Table 13. Summary of the energy retrofit opportunities and savings for case study building.

	Estimated Expected Savings
	MWh (2014)	% Energy	% Electricity	US Dollars
Energy Retrofit Opportunity
ECMs 12, 31, 33 (CBES)	545	20.72%	28.24%	$54,500.00
Repairs in the HVAC	700	26.62%	36.27%	$70,000.00
Weekend HVAC schedule	80	3.04%	4.15%	$8000.00
Thermal isolation	30	1.14%	1.55%	$3000.00
Power management (30%)	25	0.95%	1.30%	$2500.00
Total	1337	52.47%	71.50%	$133,700.00

shows the estimated savings for the various ECMs identified using the 8-step process: (1) Incorporate ECMs 12, 31, and 33 from CBES; (2) Perform technical repairs and maintenance on the HVAC system; (3) Reduce the weekend operational hours from the HVAC operational schedule; (4) Invest in thermal isolation solutions in order to reduce the HVAC system operational hours, and (5) Implement power management strategies to reduce 30% of the overnight or phantom energy load. The percentage of energy saved was benchmarked using the 2014 consumption data. The calculation found that by combining the five measures, the building could potentially realize an estimated 50% reduction in energy consumption. The authors did not estimate the potential cost savings, as reliable cost data is difficult to obtain and highly variable.

5. Discussion

While there are many online energy retrofit toolkits for commercial buildings, prior studies showed that their reliability is questionable if the building owners do not engage the help of a professional energy consultant. The framework presented in this paper allows small- and medium- sized building owners to identify opportunities for substantial building energy consumption reduction through a simpler, but thorough, energy use analysis. More importantly, the process and MS-Excel based analysis (Step 8) require a reasonable amount of data that does not require the owners to make large financial or time commitments to collect and enter. Owners are then empowered to use the results from this framework to select packages of ECMs to implement with an investment that aligns with their current energy performance and available capital.

In general, larger commercial buildings have more resources to invest in energy efficiency than smaller commercial buildings. Increasing the size of buildings but maintaining the same investment could yield greater financial benefits. For example, a $20,000 investment on an equipment would reduce more energy consumption of a large commercial building than a small one, as a large building consumes more energy than a small one. Thus, the benefit yielded from the same investment differs significantly between a small and a large building. Therefore, oftentimes the larger commercial buildings can afford the more sophisticated building systems, as the larger energy footprints would justify the cost of such systems. Owners of small and medium size commercial buildings would fare better with the simpler solutions.

6. Conclusions

This paper presents a simple framework and MS-Excel based analysis for small and medium size commercial buildings to select energy reduction solutions without breaking their banks. While this framework would not replace a competent energy auditor, it can help provide guidance for less upfront cost. This low-investment methodology also aims to help small and medium commercial building owners to identify opportunities for energy savings before committing to a larger investment. The framework supports the decision-making process for selecting different energy efficiency retrofits. This bottom-up approach combined field data collection, a spreadsheet, and the use of a publicly available web-based retrofit toolkit (CBES) that costs little to use. This framework offers opportunities for novice building owners to identity and assess savings opportunities in their buildings. The development of a framework that is appropriate for small and medium commercial buildings, leverages publicly available data, and facilitates energy analysis for novice owners constitutes the authors’ contribution to knowledge. The authors illustrate the effectiveness of this approach for the case study building that would potentially reduce its energy consumption by 50%. More research is needed to further enhance this framework and to assure its use becomes more reliable, covers more climates, and includes reliable cost information.

6.1. Limitations of This Study

The authors acknowledge several limitations associated with this study. First, this study leverages a building on a university campus. Given the climate commitments made by the university, the data available for this building may not be indicative of the types or volume of energy and electricity data that is typically available for a SMCB. While data availability may limit the potential to apply the framework presented herein to other SMCBs, the authors made every effort to suggest alternative data sources for each step in the framework if the required data was not directly available. The authors further acknowledge that implementation of this framework is time consuming, so it may not be feasible for all SMCB owners or operators. However, owner-operators of buildings have a clear economic incentive for completing energy retrofits in their buildings, so they may be willing to spend the time. Finally, the authors acknowledge that this framework uses MS Excel for data analysis, rather than a more sophisticated data analysis package. While tools like MATLAB and Python may be more computationally efficient, the analysis required for this framework is not so burdensome that the computational performance of Excel becomes a barrier to implementation. Thus, the authors opted to leverage MS Excel due to its ubiquitous nature and ease of use, and accepted the computational shortcomings. In summary, while the study clearly has limitations, the authors argue that the framework presented herein still provides value to SMCB owner-operators looking to improve their energy efficiency without hiring external auditors or consultants.

6.2. Potential Future Work

The authors envision three potential extensions of this work that could make the framework easier to replicate:

• Automate data collection to reduce time required to implement the framework, particularly Steps 5 (Analyze MELs Energy Consumption) and 7 (Summarize the Total Electricity Consumption by End Uses)
• Add sensor recommendations to the framework to support more accurate data collection and leverage new technologies, e.g., HEAT-meter [35], that can further reduce the time and expertise required to implement the framework
• Develop a web-based platform to make framework implementation easy and intuitive for SMCB owner-operators.