Printer Friendly

System-level Key Performance Indicators (KPIs) for Building Performance Evaluation.

INTRODUCTION

Improving energy efficiency in the building sector has gained increasing attention from both research and practice worlds over the years. Efforts for designing and constructing energy-efficient buildings as well as retrofitting existing buildings for higher efficiency have accelerated. Quantifying building energy performance is essential for achieving high-efficiency goals for both new and existing buildings. For new buildings, measurable building energy performance targets are crucial for plan, design, construction, and commissioning. For existing buildings, quantifying building energy performance is centric and the basis of many fault detection and diagnostics (FDD), retro-commissioning, and measurement and verification applications.

The most common approach to assess building energy performance is at the whole building level. Through whole-building benchmarking, performance indicators such as annual site energy use intensity (EUI) of a building is compared to a benchmarking dataset of similar buildings from either real buildings or simulated results. This approach is usually simple and effective since it requires very few inputs and provides relatively accurate evaluations [Wang et al. 2012]. But given the complexity of the built environment, the whole-building level approach becomes insufficient in many circumstances when the site EUI cannot capture the uncertain factors such as system operations, mixed-use types, and dynamic occupant behaviors. Mills et al. [2008] stated the importance of system and component level metrics which allows users to identify, screen, and prioritize potential efficiency improvements. Therefore, building energy performance assessment at multiple levels [Field et al. 1997] becomes necessary. Multiple-level assessment starts from the whole building level to the system level and ends at the component level. The performance of each level is quantifiable via a set of key performance indicators (KPIs). Whole-building level KPIs such as site and source energy consumption and EUI are widely used in different scenarios including building energy benchmarking and retrofit analysis. There are also plenty of performance indicators at the component level such as EER/SEER for packaged DX equipment, HSPF for air source heat pump, COP for chillers, fan efficiency, and boiler AFUE or thermal efficiency. Those performance indicators are clearly defined and widely accepted by the building industry. Some of the applications include equipment performance rating, component-level FDD, and building standard compliance checking [ASHRAE, 2016].

On the contrary, the availability and application of performance indicators at the system level are still very limited. Figure 1 shows the gap of system-level KPIs in evaluating building performance. There is a handful of researchers trying to address this issue by defining and promoting system-level KPIs. Harris and Higgins [2012] describe New Buildings Institute's investigation of metered KPI for commercial building energy use. Their results use data from two office buildings outfitted with system-level metering to calculate KPIs. They found that calculated system-level KPIs can reveal superior or inferior performance of certain aspects of design, operations and occupant behaviors. Perez-Lombard et al. [2011] proposed a set of energy efficiency indicators for HVAC system at global, service, sub-system, and equipment levels. Liao et al. [2018] defined and showcased the whole-building load to energy ratio (LER) to cooling and heating efficiency. Deru et al. [2005] developed a procedure to measure the indoor lighting energy performance. However, the definitions of KPIs in those studies are siloed and only have limited coverage of the building systems. Also, the data for system-level performance evaluation was not typically readily available [Lazarova-Molnar and Mohamed, 2016]. In recent years, the growing availability of smart sensors and meters make it possible to monitor building systems continuously. Therefore, this study aims to develop a suite of system-level KPIs and showcase their potential applications. The KPIs cover major building energy systems, including indoor lighting, outdoor lighting, cooling, heating, ventilation, air distribution, water distribution, service hot water, and miscellaneous energy loads (MELs).

This paper first summarizes the methodology of developing a set of system-level KPIs for building performance evaluation. The paper then presents several examples of the system-level KPIs of large office buildings. Typical values of the KPIs generated from batch EnergyPlus simulations considering three ASHRAE 90.1 vintages, and five U.S. climate zones are also presented. Finally, potential applications of the KPIs in FDD and building energy benchmarking, as well as future work are discussed.

METHODOLOGY

A clear definition of building systems is the prerequisite of defining the system-level KPIs. In this paper, a system refers to an aggregation of individual equipment and distribution network (e.g., pipes and ducts) that delivers a particular building service (e.g., lighting, heating, cooling, ventilation, service hot water, and miscellaneous equipment). Instead of evaluating performance at the equipment level, the system-level KPIs aims to indicate a system's overall performance by taking into account all equipment in that system. Moreover, the system-level KPIs should reflect the system performance from different perspectives such as the amount of energy consumed, the peak demand to the grid, and the impact to the built environment by the system. Therefore, when determining the KPIs, one must consider how the KPIs can represent system performance including the following criteria:

Energy Use: Energy use related KPIs evaluate how efficient a building system is in delivering the service with a certain amount of energy consumption. The common types of energy use related KPIs are energy use intensity (EUI) and energy efficiency (EE). EUI represents the cumulative energy consumption as a function of normalizing factor (e.g., annual lighting energy consumption/building floor area). The normalizing factor could vary depending on the specific KPI. For example, it can be the total gross building area for MEL system, and conditioned floor for cooling system. EE indicates the ratio of served energy to the consumed consumption (e.g., delivered cooling energy/consumed electricity).

Power Demand: Power demand is another critical metric which has a high impact on building operations and utility structure. It is directly related to the maximum service generation and transportation capacity. The KPIs defined aim to enable the evaluation of building systems' peak demands with a higher resolution.

Responsiveness to Control: Control strategies or technologies are usually hard to evaluate via whole building or component performance. System-level KPIs provide opportunities to pinpoint control issues in individual systems. For example, the average weekday's lighting energy consumption during summer should be less than that of winter if daylighting controls work effectively since summer has more daylight than winter.

Responsiveness to Service Demand: Consumption should be correlated to real demand. System-level KPIs can help identify whether the system is functioning at reasonable efficiency. For example, cooling system total energy consumption should be correlated to outdoor air temperature. The ratio between service hot water (SHW) consumption to occupant count informs whether the SHW system works properly. The ventilation rate should correlate to occupant count if it is a demand-controlled ventilation system.

Aggregation Level: KPIs with different aggregation levels can be used for different purposes. For example, the hourly cooling system EUI can be used to track system performance change and identify control issues. And the annual cooling system EUI can be used to assess the overall cooling system efficiency.

Value Type: KPIs with different types of values can be applied in different scenarios. A single-value KPI such as annual heating system EUI indicates the overall energy performance of the heating system. On the other hand, a serial-value KPI indicates the change patterns of system performance. The monthly heating energy EUI before and after a heating system renovation could be used for measurement and verification purposes.

In addition to the criteria stated above, other important aspects such as the common issues and improvement opportunities behind an abnormal KPI value, the sensor/meter needed to calculate the KPIs, and the parameters needed to derive the KPIs in EnergyPlus, are also included. Table 1 shows the structure of the system-level KPIs tables.

SHOWCASE OF SYSTEM-LEVEL KPIS

A total of 43 KPIs for large-sized office buildings are identified which are grouped into four main system types and 11 sub-system types. Four main system types are lighting system, MELs, HVAC, and SHW. Although the KPIs defined in this project originate from the large-sized office building type, the structured table format allows further development to cover more building and system types holistically. Figure 2 and Figure 3 show the example KPIs for lighting system, cooling system, and heating system. KPIs for other systems are organized in the same format.
Figure 2. Example table of MEL system KPIs (partial)

System   Sub-system            KPI            Definition

                               W/ft2          Energy demand of the
                                              system perperson and floor
                                              area.
         Occupant-related      kWh/(ft2*yr)   Annual energy consumption
         MELs                                 perperson
MELs                           Usage Profile  The percentages of Four
                                              status - active, idle,
                                              sleep, and off.
                               w/ftz          Energy demand of the
                                              system per person and
                                              floor area.
         Non-occupantrelated   kWh/(ft2*yr)   Annual energy consumption
         MELs                                 per person
                               Usage Profile  The percentages of four
                                              status - active, idle,
                                              sleep, and off.

System  Impact        Value Type    Time Interval      Sensor/Meter
        Catrgory

        Demand |      Single Value  Multiple (hourly,  Electricity meter
        Power                       monthly, annual)   for MEL
        Energy | EUI  Single Value  Annual             Electricity meter
                                                       for MEL
MELs    Energy | EE   Distribution  Annual             Electricity meter
                                                       for MEL
        Demand |      Single Value  Multiple (hourly,  Electricity meter
        Power                       monthly, annual)   for MEL
        Energy | EUI  Single Value  Annual             Electricity meter
                                                       for MEL
        Energy | EE   Distribution  Annual             Electricity meter
                                                       for MEL

System   EnergyPlus Parameters

         Output: Variable,*,Electric
         Equipment Electric
         Power,hourly; !- Zone
         Average [W]
         Output: Meter, InteriorEqui
         pment:Electricity, hourly; !-[J]
MELs     NA
         Output: Variable,*,Electric
         Equipment Electric
         Power, hourly; !- Zone
         Average [W]
         Output: Meter, InteriorEqui
         pment:Electricity,hourly; !-[J]
         NA

Figure 3. Example table of cooling system KPIs (partial)

System   Sub-system   KPI             Definition

                      w/ft2           Cooling system demand per
                                      floor area
                      kwh/kwh         Cooling system consumption
                                      per delivered cooling energy
                      kW/ton          Cooling system power
                                      demand per delivered tooling
                                      tonnage
HVAC     Cooling      kwh/ft2         Cooling system energy use
System   System                       intensity
                      kWh/(ft2*CDD)   Cooling system energy use
                                      intensity normalized by
                                      cooling degree days
                      ton-hour/kWh    Energy efficiency of a central
                                      cooling plant, including
                                      energy use of chillers,
                                      chilled-water pumps, cooling
                                      towers, and condenser-water pumps
                                      (for water-cooled chillers).
                                      The KPI is calculated as the
                                      ratio of ton-hour of delivered
                                      cooling energy to kWh of
                                      consumed electricity of all
                                      central plant equipment

System   Impact         Value Type       Time Interval
         Catrgory

         Demand |       Serial           Multiple (daily,
         power                           weekly, seasonal,
                                         annual)
         Energy | EE    Single           Multiple (daily,
                                         weekly, seasonal,
                                         annual)
         Demand |       Single           Annual
         Power
HVAC     Energy | EUI   Single           Annual
System
         Energy | EUI   Single /Serial   Multiple (daily,
                                         weekly, seasonal,
                                         annual)
         Energy | EE    Single /Serial   Multiple (hourly,
                                         monthly, annual)

System   Sensor/Meter          EnergyPlus Parameters

         Electricity meters    Output: Variable,*,Plant
         for chiller, chiled   Supply Side Cooling
         water pumps, and      Demand Rate, hourly; !-HVAC
         cooling towers (If    Average [W]
         applicable)
         Electricity meters    Varied by system types
         for chiller, chiled
         water pumps, and
         cooling towers (If
         applicable)
         Electricity meters    Varied by system types
         for chiller, chiled
         water pumps, and
         cooling towers (If
         applicable)
HVAC     Electricity meters    Output:Meter, Cooling:Elec
System   for chiller, chiled   tricity, hourly; !- [J]
         water pumps, and
         cooling towers (If
         applicable)
         Electricity meters    Output:Meter, Cooling:Elec
         for chiller, chiled   tricity, hourly; !-[J]
         water pumps, and
         cooling towers (If
         applicable)
         Electricity meters    Output: Meter, Electricity: PI
         for chiller, chiled   ant, hourly; !- [J];
         water pumps, and      Output: Meter, Gas:Plant, ho
         cooling towers (If    urly; !- [J];
         applicable)           Output: Meter, Pumps:Elect
                               rici1y, hourly; !- [J]


Energy simulations are good virtual sources of building system sensor and meter data. Therefore, a set of building energy simulation is conducted to obtain typical values of the system-level KPIs. The energy simulation used DOE's reference large-sized office building models with EnergyPlus as the simulation engine. To investigate the KPI variations across different locations and building code versions. Five climate zones (Miami - 1A, Houston - 2A, San Francisco - 3C, Chicago - 5A, Burlington - 6A) and three ASHRAE 90.1 Vintages (90.1-2004, 90.1-2010, and 90.1-2013) are used in the batch simulations. As discussed before, KPIs can be single-value and/or serial-value, Figure 4 shows examples of single-value KPIs. Single-value KPIs indicate the overall performance of a system or sub-system and are often aggregated to monthly, seasonal, or annual level. They can be used for benchmarking at multiple levels.
Figure 4. Typical values of system-level KPIs (partial)

System            Sub-system                KPI

Lighting System   Indoor Lighting System    kWh/(ft2*yr)
Lighting System   Indoor Lighting System    kWh/(ft2*yr)
Lighting System   Indoor Lighting System    kWh/(ft2*yr)
Lighting System   Indoor Lighting System    W/ft2
Lighting System   Indoor Lighting System    W/ft2
Lighting System   Indoor Lighting System    W/ft2
Lighting System   Indoor Lighting System    kWh/(person*yr)
Lighting System   Indoor Lighting System    kWh/(person*yr)
Lighting System   Indoor Lighting System    kWh/(person*yr)
Lighting System   Indoor Lighting System    kWh/(FTE_Occupied Hours)
Lighting System   Indoor Lighting System    kWh/(FTE_Occupied Hours)
Lighting System   Indoor Lighting System    kWh/(FTE_Occupied Hours)
MEL System        -                         kwh/(ft2*yr)
MEL System        -                         kwh/(ft2*yr)
MEL System        -                         kwh/(ft2*yr)
MEL System        -                         kWh/(person*yr)
MEL System        -                         kwh/(person*yr)
MEL System        -                         kWh/(person*yr)
HVAC System       -                         kWh/ft2
HVAC System       -                         kWh/ft2
HVAC System       Heating System            ETU/(ft2*HDD)
HVAC System       Heating System            BTU/(ft2*HDD)
HVAC System       Heating System            BTU/(ft2*HDD)
HVAC System       Cooling System            kWh/ft2
HVAC System       Cooling System            kWh/ft2
HVAC System       Cooling System            kwh/ft2
HVAC System       Air Distribution System   W/cfm
HVAC System       Air Distribution System   W/cfm
HVAC System       Air Distribution System   W/cfm
HVAC System       Ventilation               cfm/ft2
HVAC System       Ventilation               cfm/ft2
HVAC System       Ventilation               cfm/ft2
SHW               -                         gallon/person
SHW               -                         gallon/person
SHW               -                         gallon/person
SHW               -                         W/gpm
SHW               -                         W/gpm
SHW               -                         W/gpm

System            Impact Category   Vintage            Miami (1A)

Lighting System   Energy | EUI      ASHRAE 90.1-2004      2.9
Lighting System   Energy | EUI      ASHRAE 90.1-2010      2.1
Lighting System   Energy | EUI      ASHRAE 90.1-2013      1.9
Lighting System   Demand | Power    ASHRAE 90.1-2004      0.28
Lighting System   Demand | Power    ASHRAE 90.1-2010      0.23
Lighting System   Demand | Power    ASHRAE 90.1-2013      0.20
Lighting System   Energy | EE       ASHRAE 90.1-2004    587.7
Lighting System   Energy | EE       ASHRAE 90.1-2010    439.2
Lighting System   Energy | EE       ASHRAE 90.1-2013    389.3
Lighting System   Energy | EE       ASHRAE 90.1-2004    584.S
Lighting System   Energy | EE       ASHRAE 90.1-2010    436.9
Lighting System   Energy | EE       ASHRAE 90.1-2013    387.2
MEL System        Energy | EUI      ASHRAE 90.1-2004     11.6
MEL System        Energy | EUI      ASHRAE 90.1-2010     11.3
MEL System        Energy | EUI      ASHRAE 90.1-2013     11.3
MEL System        Energy | EE       ASHRAE 90.1-2004   2377.9
MEL System        Energy | EE       ASHRAE 90.1-2010   2313.7
MEL System        Energy | EE       ASHRAE 90.1-2013   2316.5
HVAC System       Energy | EUI      ASHRAE 90.1-2010      3.3
HVAC System       Energy | EUI      ASHRAE 90.1-2013      3.5
HVAC System       Energy | EUI      ASHRAE 90.1-2004      4.5
HVAC System       Energy | EUI      ASHRAE 90.1-2010      1.5
HVAC System       Energy | EUI      ASHRAE 90.1-2013      0.3
HVAC System       Energy | EUI      ASHRAE 90.1-2004      5.7
HVAC System       Energy | EUI      ASHRAE 90.1-2010      4.7
HVAC System       Energy | EUI      ASHRAE 90.1-2013      4.0
HVAC System       Demand | Power    ASHRAE 90.1-2004      0.44
HVAC System       Demand | Power    ASHRAE 90.1-2010      0.42
HVAC System       Demand | Power    ASHRAE 90.1-2013      0.41
HVAC System       Air quality       ASHRAE 90.1-2004      0.12
HVAC System       Air quality       ASHRAE 90.1-2010      0.07
HVAC System       Air quality       ASHRAE 90.1-2013      0.09
SHW               Energy | EE       ASHRAE 90.1-2004      0.0022
SHW               Energy | EE       ASHRAE 90.1-2010      0.0022
SHW               Energy | EE       ASHRAE 90.1-2013      0.0022
SHW               Demand | Power    ASHRAE 90.1-2004      8.06
SHW               Demand | Power    ASHRAE 90.1-2010      8.06
SHW               Demand | Power    ASHRAE 90.1-2013      8.06

System            Houston     San         Chicago     Burlington
                  (2A)        Francisco   (5A)        (6A)
                              (3C)

Lighting System      2.9         2.9         2.9         2.9
Lighting System      2.2         2.1         2.1         2.1
Lighting System      1.9         1.9         1.9         1.9
Lighting System      0.28        0.28        0.28        0.23
Lighting System      0.23        0.23        0.23        0.23
Lighting System      0.20        0.21        0.21        0.21
Lighting System    587.7       587.7       587.7       587.7
Lighting System    442.5       436.6       434.1       435.0
Lighting System    389.8       390.8       390.1       390.7
Lighting System    584.6       584.6       584.6       584.6
Lighting System    440.2       434.4       431.8       432.7
Lighting System    387.7       388.7       388.1       383.6
MEL System          11.6        11.6        11.6        11.6
MEL System          11.3        11.3        11.3        11.3
MEL System          11.3        11.3        11.3        11.3
MEL System        2377.9      2377.9      2377.9      2377.9
MEL System        2313.7      2318.7      2318.7      2318.7
MEL System        2316.5      2316.5      2316.5      2316.5
HVAC System          3.1         2.0         3.0         3.0
HVAC System          3.1         1.9         3.0         2.9
HVAC System          5.1         2.0         4.4         4.5
HVAC System          1.3         0.2         2.5         2.6
HVAC System          1.5         0.2         2.9         3.0
HVAC System          4.6         1.9         2.6         2.3
HVAC System          3.7         0.8         1.6         1.2
HVAC System          3.2         0.8         1.5         1.1
HVAC System          0.43        0.42        0.42        0.41
HVAC System          0.41        0.42        0.41        0.41
HVAC System          0.40        0.40        0.42        0.42
HVAC System          0.11        0.14        0.09        0.09
HVAC System          0.09        0.12        0.10        0.10
HVAC System          0.08        0.10        0.10        0.10
SHW                  0.0022      0.0022      0.0022      0.0022
SHW                  0.0022      0.0022      0.0022      0.0022
SHW                  0.0022      0.0022      0.0022      0.0022
SHW                  3.06        3.06        3.06        3.06
SHW                  8.06        8.06        3.06        3.06
SHW                  3.06        3.06        3.06        3.06


In addition to single-value KPIs, serial-value KPIs show the change of system performance over a certain period. Those KPIs can be used to track system performance fluctuation, detect abnormal patterns, and compare system performance change before and after system modification/update. Depending on the application, the time intervals of serial-value KPIs can vary from sub-hourly to monthly.

POTENTIAL APPLICATIONS

System-level performance diagnostics. Traditional component-level FDD provides insights into how a specific component works, but building systems are complicated and interconnected. The operation of specific equipment may be influenced by other equipment. For instance, air handling unit (AHU) supply air fan operations are related to VAV terminal units. The lighting system, MEL system, and ventilation system performance can be all linked to the occupant-based control system. System-level KPIs provide a new perspective to evaluate the building performance, which considers the performance of the equipment in the system as a whole part. System-level KPI values help track the system performance and identify abnormal operating conditions.

System-level performance benchmarking. Another potential application is the performance benchmarking at the system level. Building energy performance benchmarking will have a higher accuracy with appropriate system-level KPIs. For example, Figure 5 shows two system-level energy performance benchmarking scenarios with different KPIs. The KPI values are derived from the simulations with DOE's reference large-sized office buildings in five different locations with three ASHRAE 90.1 vintages. Scenario 1 uses the annual cooling EUI which indicates that the building in San Francisco has the lowest cooling EUI among five locations. However, scenario 2 shows that San Francisco has the highest cooling EUI normalized by cooling degree days (CDD). The KPI in scenario 2 captures the impact of weather condition on the building's cooling system, which indicates the potentials of cooling energy savings with air economizers added to old buildings.

Engineering reference. There are a bunch of efficiency metrics for the whole building and individual equipment. Common sources are engineering handbooks, building standards, and manufacturer brochures, but there lacks such reference for system level performance. So, one potential application of the system-level KPIs would be engineering reference. The engineering reference should cover typical values or ranges of system-level KPIs in buildings under different operational conditions such as different vintages, different building types, and different climate zones.

DISCUSSION

This project starts an effort to define KPIs at the building system level. Three potential applications were discussed. The KPIs in this project are defined for office buildings. Typical KPIs are derived via energy simulations instead of real measurements. Further development and applications of system-level KPIs are of future interests:

Develop a KPI database. A database of system-level KPI values can be derived from simulation results of DOE's reference building models covering diverse use types, vintages, and climates. The KPI database can be validated and reinforced with measurement of real buildings systems.

Integrate the system-level KPIs with FDD tools. Further study is needed to explore how the system-level KPIs can be used to assist FDD of energy systems in buildings.

Customize KPIs for specific building types. The system-level KPIs are building specific. For example, in hospital buildings, system performance is closely related to the number of patient beds and room use types. In laboratory buildings, system performance is affected by the type of activities and equipment. In sports facilities and theaters, system performance is related to the types and frequencies of events. Therefore, specific KPIs are needed to describe the system performance.

Integrate the system-level KPIs with DOE's Building Performance Database (BPD). DOE's BPD is the nation's largest dataset with energy-related information for commercial and residential buildings. It provides straightforward energy data visualization and comparison functions. However, the energy-related metrics in BPD are mostly at the whole building level. Adding system-level KPIs to BPD could take advantage and expand its existing functions. An underline challenge is the availability of data to determine the system-level KPIs.

Integrate the system-level KPIs into building energy codes and standards, as well as utility incentives/rebate for new and existing buildings programs. Building energy codes and standards such as ASHRAE 90.1, ASHRAE 189.1, and California Title 24 do not have a system performance compliance path, except ASHRAE 90.1 has a tradeoff method for the building envelope performance compliance. A well-defined and validated set of system-level KPIs can be potentially used as a system performance compliance path. Utilities can also reference the system-level KPIs to design their incentives and rebates programs for existing and new buildings.

CONCLUSION

A suite of system-level KPIs are developed in this study, which covers four main end-use systems in large office buildings including lighting system, MELs system, HVAC system, and SHW system. Each main system category contains several sub-systems. This paper discussed the considerations when selecting and defining the KPIs. The KPI tables list part of a complete set of 43 KPIs, their explanations, their primary impact categories, and the sensor/meter data needed to calculate the KPIs. This study also showcases examples of some KPIs derived from energy simulations, and discussed their potential applications: system-level performance diagnosis, system-level performance benchmarking, and engineering reference. Future development is needed to expand the coverage of the system-level KPIs and promote related applications.

ACKNOWLEDGMENTS

This work was supported by the Assistant Secretary for Energy Efficiency and Renewable Energy, the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.

REFERENCES

ASHRAE, ANSI/ASHRAE/IES Standard 90.1-2016. Energy Standard for Buildings Except Low-Rise Residential Buildings. 2016, American Society of Heating, Refrigerating, and Air-Conditioning Engineers, Inc.: Atlanta, GA.

Denmark, S. (2016). Challenges in the Data Collection for Diagnostics of Smart Buildings, 376(January). https://doi.org/10.1007/978-981-10-0557-2

Deru, M., Blair, N., & Torcellini, P. (2005). Procedure to measure indoor lighting energy performance. National Renewable Energy Laboratory, (October). Retrieved from http://www.nrel.gov/docs/fy06osti/38602.pdf

Field, J. W., Soper, J., Jones, P. G., & Bordass, W. T. (1997). Energy Performance of Occupied Non Domestic Buildings: Assessment by analysing end-use consumptions. Building Services Engineering Research and Technology, 18(1), 39-46. https://doi.org/10.1177/014362449701800106.

Harris, D. (2012). Key performance indicators--field metering study and energy performance feedback. California Energy Commission PIER Project Report #500-08-049. https://newbuildings.org/wpcontent/uploads/2015/11/KPIFinalReportJuly20121.pdf.

Harris, D. and Higgins, C. (2012). Key Performance Indicators--Field Metering Study and Energy Performance Feedback. California Energy Commission, PIER Energy - Related Environmental Research Program. CEC-500-08-049.

Liao, J., & Claridge, D. E. (2018). Analysis of Whole-Building HVAC System, 124.

Mills, E., Mathew, P., Piette, M. A., Berkeley, L., Bourassa, N., Brook, M., & Commission, C. E. (2008). Action-Oriented Benchmarking: Concepts and Tools.

Perez-Lombard, L., Ortiz, J., Maestre, I. R., & Coronel, J. F. (2012). Constructing HVAC energy efficiency indicators. Energy and Buildings, 47, 619-629. https://doi.org/10.1016/j.enbuild.2011.12.039

Wang, S., Yan, C., & Xiao, F. (2012). Quantitative energy performance assessment methods for existing buildings. Energy and Buildings, 55, 873-888. https://doi.org/10.1016/j.enbuild.2012.08.037

Han Li

Associate Member ASHRAE

Tianzhen Hong, PhD, PE

Member ASHRAE

Marina Sofos, PhD

Member ASHRAE
Table 1. Structure of the System-level KPI Tables

Column                      Meaning

System                      System name
Sub-system                  Sub-system name
KPI                         KPI's name (This field can
                            be a unit or a profile type.)
Definition                  The KPI definition
Impact Category             KPI's main impact category. It can be energy
                            (energy efficiency, energy use intensity),
                            peak demand or power, water usage, air
                            quality, and thermal comfort. 'Energy | EE'
                            stands for energy efficiency, 'Energy | EUI'
                            stands for energy use intensity.
Value Type                  A KPI value can be a single value (e.g.,
                            annual EUI) or serial values (e.g., monthly
                            values, load shape or profile)
Aggregation Level           Sensor/meter reading time interval (hourly,
                            daily, monthly, annual)
Common Issues               Common system deficiency or faults
                            associated with abnormal KPI value or trend
Improvement Opportunities   Improvement opportunities corresponding to
                            the common issues
Sensor/Meter                Required sensors and meters to provide data
                            for calculating the KPI
EnergyPlus Parameters       Corresponding output meters or variables in
                            EnergyPlus to represent or calculate the
                            KPIs
COPYRIGHT 2019 American Society of Heating, Refrigerating, and Air-Conditioning Engineers, Inc. (ASHRAE)
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2019 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Author:Li, Han; Hong, Tianzhen; Sofos, Marina
Publication:ASHRAE Transactions
Date:Jan 1, 2019
Words:4280
Previous Article:Energy, Emissions and Economics (EEE) Impact Derivation and Applications for Energy Performance Calculations and Comparisons.
Next Article:Taking the (Fuel) Blinders off Energy Codes Part 2: Metrics and Mechanics in the Modern Era.
Topics:

Terms of use | Privacy policy | Copyright © 2022 Farlex, Inc. | Feedback | For webmasters |