Data and Interfaces for Advanced Building Operations and Maintenance - RP 1633 Final Report

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Data and Interfaces for Advanced Building Operations

and Maintenance - RP 1633 Final Report

Submitted to: ASHRAE

1791 Tullie Circle, N.E. Atlanta, GA 30329 Contributors: Nicholas Gayeski, PhD Sian Kleindienst, PhD Jaime Gagne, PhD Bradley Werntz Ryan Cruz Stephen Samouhos, PhD KGS Buildings, LLC

66 Union Square Suite 300 Somerville, MA 02143

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Acknowledgements

Thank you to the project monitoring subcommittee including Reinhard Seidl, Steve Taylor, Chariti Young, Jim Kelsey, and Kristin Heinemeier for their guidance, feedback, and patience throughout this research project. Thank you to all of our participants for committing time to participate in interviews, review information, and share their experiences. Thank you to the sponsoring technical committees, ASHRAE staff, and ASHRAE membership for their ongoing efforts to advance the state of the industry.

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Executive Summary

Analyzing and interpreting building performance data to inform operations and maintenance is critical to the realization of energy efficient, high performance buildings. With the advance of technology hardware and software for buildings, there is an increasing amount of available data to inform building operations, maintenance and management. However, facility management personnel have limited time and resources and need concise metrics, visualizations, and information in order to support their daily operations and decision-making. Recent works, such as ASHRAE’s Performance Measurement Protocols in Commercial Buildings, have focused attention on the metrics relevant to tracking building

performance. The research described in this report seeks to expand such investigations to consider visualization of operational metrics focused on an audience including facility managers, control

technicians, heating ventilation and air conditioning (HVAC) technicians, facilities service providers, and commissioning engineers.

The ultimate goal is to provide recommendations about data-driven metrics and interfaces so that they clearly quantify and communicate building operational performance for a diverse set of building

stakeholders. This report provides these recommendations and summarizes the activities conducted to arrive at them. These activities included: surveying relevant metrics, visualizations and software interfaces; interviewing building operations staff and supporting personnel; and creating mock up interfaces that research participants reviewed. The body of the report goes into detail about each task and how these tasks informed the recommendations. This executive summary describes the core recommendations of the research with only a brief overview of how these recommendations were arrived at through the project tasks.

Before presenting recommendations, notable resources available through this project include the following:

 A compendium of available metrics and interfaces examples is included in Appendix A. Use this database to review operational metric options and to see examples of visualizations from real applications.

 Feedback from interview participants and survey respondents are included in Appendices B, C, and E. This includes anecdotal feedback, such as anonymous comments from participants about what they want in an operational interface, and survey feedback with statistics about

interviewee preferences.

 Mock-up visualizations of metrics are available through the mock interface site,

https://sites.google.com/a/kgsbuildings.com/rp1633/, and screen shots are available in Appendix D.

For the reader interested in scanning the example interfaces reviewed in this research, we recommend jumping ahead to Section 5, Appendix A, or the website listed above.

Advanced Operations and Maintenance Interface Recommendations

The old adage attributed to Henry Ford, “If I had asked people what they wanted, they would have said faster horses” applies to this research in that building operations and maintenance personnel do not

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4 necessarily know what to ask for to get better metrics and visualizations through which to manage, operate and maintain buildings. We have condensed the preferences expressed by interviewees and best practices found in the industry into a set of recommendations that reflect the predominant needs underlying the expressed preferences. Specifying engineers, product designers, and facilities personnel may consider these recommendations as they specify, design or adopt operations and maintenance interfaces.

The feedback we collected was from a diverse set of stakeholders which was at the same time broad - in that we talked to many different people, in different roles, and in different types of facilities - and limited in that the stakeholders represent only a tiny portion of the industry. We recognize that the feedback we gathered does not constitute a statistically significant sample from which to claim, definitively, that these recommendations are precisely what every operations and maintenance stakeholder wants in an advanced interface. With such caveats in mind, our recommendations are presented below.

At the most basic level, we recommend the ability to view and drill down into different scales of information because facility management and operations personnel need the ability to assess performance at multiple scales. These scales include the following:

 Enterprise or portfolio scale, presenting performance of multiple facilities,  Building scale, presenting overall building performance information,

 System scale, at which systems like heating, cooling, ventilation, lighting, generation and others may be drilled into and assessed from a systems perspective, and at

 Equipment and Zone scale, at which specific equipment like an air handler, pump, boiler, Fan Coil Unit, VAV box, or others may be assessed, and finally

 Project scale, at which the performance of the building or systems related to specific projects, such as a re-commissioning project or a chiller replacement, can be assessed. Many research participants indicated a strong need to be able to view information at this scale in order to assess the effectiveness of their investments and initiatives.

We recommend including certain types of information across all scales, including the following:  Cost information, such as how much energy cost a building or equipment consuming.

 Utility information, such as how much electric, gas or water a building or equipment consuming, their carbon equivalents, progress related to utility consumptions goals.

 Operating characteristics, such as visualizations and graphics of how buildings, systems or equipment are performing now or over time. This can include characteristics such as runtimes, expected occupied hours, average operating temperatures, pressures, flows or other

characteristics indicative of performance.

 Diagnostic information, such as automated fault detection and diagnostic outputs which can detect when buildings, systems or equipment have faults or opportunities for higher efficiency. This might include, for example, when mechanical or control faults such as valves leaking by, but also opportunities for more efficient operation such as installing variable speed drives, cleaning heat exchangers, programming reset schedules, or optimizing a chilled water loop.

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5  Data visualization tools. This spans all scales and reflects an underlying need for the ability to

create charts, scatter plots, and other views in multiple formats using any data from any scale. It also presumes data is gathered and stored for later use.

We recommend software interfaces allow users to navigate from each of these scales into each of these types of information, with associated metrics and visualizations for each category. The specific design, user experience, or workflows within these interfaces is a product design and user experience challenge outside the scope of this research. Within each of these scales certain metrics or visualizations stand out based on the interviewees responses, and these are listed below.

Enterprise scale  Metrics

o Daily and monthly operating costs for utilities, like energy and water

o Daily, monthly and real time consumption for utilities, like energy and water o Utility peak demand use and time

o Greenhouse gas emissions in carbon equivalents

o Current and recent whole building operating modes for heating, cooling, or both o Diagnostic metrics including number of faults, rise or fall in fault counts, avoidable cost

associated with faults and opportunities, and savings achieved o Normalization of all metrics by building area and weather conditions  Visualizations

o Maps allowing users to compare and select buildings for deeper investigation, with multiple layers to display the metrics listed above

o Line charts to view portfolio performance over time o Bar charts to compare buildings, benchmarks, and goals

o Pie charts to show building or utility contributions to overall use o Tabular views of buildings, sortable by the metrics above Building scale

 Metrics

o All of the metrics at the enterprise scale listed above, but for each specific building

o End-use breakdowns presented both by utility type, for example by electric, gas, steam, and chilled water consumption, as well as by end use type, for example by cooling, heating, ventilation, lighting, plug loads, and other uses

o For demand response applications, projected future consumption and the timing of demand response events

o Operating characteristics such as building expected occupancy, measured occupancy, and whole building comfort indices

o Major system and equipment operating characteristics such as major equipment run time hours or overall plant performance

o Major system and equipment diagnostic metrics rollups such as total avoidable cost associated with faults, impact of faults on occupant comfort, and fault severity  Visualizations

o All visualizations listed at the enterprise scale

o Calendar plots or time series overlays to compare performance under similar conditions or day types over time

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6 o Tabular views of operating characteristics and diagnostic information

System scale  Metrics

o Current operating conditions for key variables, such as supply temperatures or pressures relative to setpoints for major systems, temperature differences on major hydronic loops, statistics on valve positions served by loops or damper positions served by ventilation systems

o Run-time hours for major systems and equipment

o Fault indicators showing system-level faults such as simultaneous heating and cooling or competing systems or suboptimal controls like lack of staging or suboptimal start/stop o Fault metrics for each system such as the number of faults, the avoidable cost associated

with faults, and the impact of faults on occupant comfort conditions.  Visualizations

o Time series plots of conditions for each system with representations of allowable operating ranges and setpoints

o Tables showing statistics about major equipment, such as run time hours, current operating conditions, fault counts, fault impacts, and cost impact.

o Color coded graphics illustrating systems deviating from expected performance, in alarm, or with diagnostic faults, with multiple layers of information overlayed in a systems diagram. Layers may include, for example, deviations from setpoints, alarms, and fault severity measured by cost impact, comfort impact, or equipment maintenance priority

o

Drill down capabilities into textual and graphical information about a fault describing and illustrating the nature of the fault, root causes of the fault, suggested resolution, and impact of the fault on operating cost, energy consumption, occupant comfort, or equipment lifetime

o Co-presented graphs of supply side and load side conditions, such as a time series of hydronic loop temperature differences over time relative to the mean, minimum and maximum hydronic loop load side valve positions over time. Similar visualizations can be created for ventilation system dampers and air handler supply air conditions.

o Histograms for major point compliance deviations (e.g. number of hours deviating from setpoint by one, two, or three degrees) or damper and valve positions (e.g. number of hours during each valves or dampers were positioned at 10%, 20%, 30%, etc. open)

Equipment and zone scale  Metrics

o Equipment and zone deviations from setpoints or thermal comfort conditions, related to for example temperature, humidity, carbon dioxide, and light levels

o Fault information such as equipment operating off schedule, stuck dampers, leak-by on valves, simultaneous heating and cooling, or suboptimal equipment controls

o Fault metrics such as the number of faults, the avoidable cost associated with each fault, the impact of faults on zone comfort conditions or equipment lifetime, and duration of faults  Visualizations

o Time series plots of conditions for each equipment with representations of allowable operating ranges and setpoints

o Color coded equipment graphics illustrating equipment deviating from expected

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7 faults, or fault severity measured by cost impact, comfort impact, or equipment

maintenance priority

o Color-coded floorplans with multiple layers representing metrics, such as deviation from comfort or supply conditions, and faults, such as zones or components with specific faults and their fault metrics above

o Animations of floorplans or equipment graphics illustrating performance metrics over time o Floorplans illustrating groups of zones served by common plant or ventilation systems o Drill down capabilities into textual and graphical information about a fault describing and

illustrating the nature of the fault, root causes of the fault, suggested resolution, and impact of the fault on cost, energy, comfort, and equipment lifetime

Project scale  Metrics

o Expected project cost

o Expected energy and cost savings and projected payback o Actual project cost

o Achieved energy and cost savings and payback  Visualizations

o Time series, such as line or bar charts, of project-related utility consumption with an indication of project start date and completion date

o Tabular views of all projects, with the ability to sort projects by the metrics listed above Here are a few considerations for consulting engineers:

 Many of the metrics and visualizations above presume the underlying data is available from sensors and systems in the building, that the building automation and metering systems’ capabilities are sufficient to collect this data, and that the data is trended somewhere in a scalable database.

 Many of the metrics and visualizations demonstrate the need to be able to represent data in many ways, such as time series, bar charts, or scatter plots and with the flexibility to allow users to create their own views of the data. Do not specify a fixed set of graphics or metrics, but rather the ability to represent data and metrics at different scales and for different purpose and stakeholders. This requires flexible tools and configurability of components or interfaces for different stakeholders.

 Anticipate the need to integrate building data with other data sets and systems by specifying integration capabilities such as webservices. Common systems with other relevant data include maintenance management systems, integrated workplace management systems, complaints software, space management software, and accounting tools.

 For graphical system representations, where they exist, enforce accurate representations of systems, e.g. heating plants, air handlers, in automation system graphics or other

representations

 It is unlikely that a single software package will provide all of the recommended functionality, because the metrics and visualizations contain data and information that cut across different types of software applications and building systems. Therefore, interoperability of software packages through technologies like webservices and single-sign-on authentication becomes important to fulfill the requirements through multiple software packages. Customers with requirements for a ‘single pane of glass’ type interface presenting all of the metrics and visualizations may require a higher level of integration, typically at a higher cost.

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8 From this research it is clear that concisely presenting information for operations and maintenance personnel is critical to managing building performance, and will be accomplished as much by good design of user interfaces as by presenting specific metrics and visualizations. In summary, interfaces should present information at multiple scales, across an enterprise, for specific buildings, within building zones or for specific systems and equipment, and for facility projects with clear indicators from metrics and visualizations representing overall performance and where to drill down. When drilling down, interfaces should provide sufficient information to indicate not just current conditions, but whether those conditions are within appropriate ranges, how those conditions compare to past performance, how those conditions relate to other system components, and whether those conditions represent faulty or suboptimal performance. Lastly, interfaces should provide flexibility in viewing data in many formats, with different charting types, allowing users to switch between views, and to easily overlay data or switch to related data sets.

Research Tasks

The research was conducted in six major tasks. These began with a scoping and review phase, in which we conducted a review of available technologies and an initial set of scoping interviews. Based on this initial research, we developed a stakeholder interview questionnaire to focus on specific metrics and graphics and conducted a second set of interviews. Then, interactive dashboard prototypes embedded in a web-based survey were created for participants to test the interfaces and communicate interface preferences. Finally, recommendations for advanced building operations and maintenance interfaces were developed based on the results of all of these tasks.

Review of Metrics and Interfaces

Section three of this report includes a literature review of previous research, a compilation of existing tools, and a summary of existing types of data, metrics, and graphical methods of representation used to assess building performance. Relevant research and publications are reviewed including the

Performance Measurement Protocols in Commercial Buildings, the Performance Metrics Project through the U.S. Dept. of Energy’s Commercial Building Initiative, ASHRAE’s Building Energy Quotient, and ASHRAE Guideline 13, Specifying Building Automation Systems. These catalogue many relevant metrics, such as basic building energy use intensity (EUI), which are widely used and a foundation for assessing building performance.

A database of metrics and graphics used to evaluate building performance and aid in operational and financial decision-making is available as Appendix E. The metrics database provides an overview of the types of data, metrics, and other information that is or could be made available in building automation systems, energy dashboards, and other analytics systems. The graphics database summarizes the types of graphical representations that can be used to present these metrics and information to the user from within an interface or dashboard. The graphics database includes examples of various types of visual

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Interviewees had widely varying

views on the most useful

metrics and visualizations, and it

was clear that an inflexible, fixed

set of metrics and visualizations

would not serve the needs of all

stakeholders.

representation for data that are currently found in commercial tools such as calendar plots, floor plan views, rating system visualizations, and equipment graphic overlays.

Participant Interviews

Section four of this report describes the results of interviews with project participants, which solicited their preferences for data, metrics and visualizations for operations and maintenance. The interview questionnaires were structured into a set of 7 focused categories spanning an enterprise portfolio, building and equipment or system level, and covering topics such as consumption, cost, emissions for various utilities, and operating characteristics and

diagnostics about equipment and systems.

Interviewees were presented with example metrics and visualizations across these categories and asked whether they found them useful or not. Notably, interviewees had widely varying views on the most useful metrics and visualizations, and it was clear that an inflexible, fixed set of metrics and visualizations would not serve the needs of all stakeholders. Instead, widely varying needs demand flexible interfaces, which allow for different metrics to be presented in a variety of visualizations and configurations for each stakeholder. Some participant preferences were

heavily influenced by negative past experiences, including inaccurate data, unintuitive metrics, and non-transparent dashboards. Such experiences erode trust in more complex system outputs, such as fault diagnostics and avoidable costs. Many participants, especially those with engineering knowledge, preferred simple, verifiable information such as time-series graphs of key performance data and the ability to plot data from different systems on the same charts. These desires seem to be an immediate response to current pain points with existing building automation systems that have limited trending and graphing capabilities, or lack of trust in existing diagnostic information.

The types of information that participants most frequently indicated were useful included metrics related to equipment fault detection, potential for LEED or other certification, system or equipment efficiency metrics, and benchmarks comparing the building performance to an ideal or simulated model. The most commonly preferred graphic visualizations included equipment and system level graphics, floor plans, and graphs showing live or historical time series data. Although participants were provided examples of the various types of graphics, it is possible that participants chose those graphics they were already most comfortable with as the most useful. Next most frequently preferred graphics included graphs showing performance data overlaid with weather data, heat maps of performance (such as zone temperature deviations) overlaid on a floor plan, energy end use icons or graphics, and performance over time overlaid on a clock or calendar.

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10 Less useful types of representation included performance equivalents (for example, energy use

represented using numbers of light bulbs), temporal maps (heat maps of performance over time), and report cards. The least popular visualizations among those who manage and operate buildings were the gauge and the scatterplot, but for different reasons. Many operational staff felt that a gauge was flashy but without substance, and many participants did not seem comfortable with the scatterplots. Two of the most popular visualization types for both portfolio and building-level management were the

benchmark (visually comparing current values with historical performance or goals) and the time series. For cross-building information, participants liked color-coded portfolio or campus maps as a way to communicate high-level information only if they allowed away to drill down to detailed information. Bar charts or time series graphs of utility consumption, comparisons to past performance, and pie charts of end use breakdown over selected periods of time were predictably highly ranked. Portfolio and financial decision-makers generally had little interest in or understanding of detailed operational information, but instead preferred common financial metrics such as spending, budgets, and project or maintenance ROI. Utility consumption presented as a time-series graph, with benchmarking against goals or historical values, was a highly ranked way of viewing building performance. Facility managers generally gave high rankings to energy consumption time series, energy breakdown pie charts and time series, and energy comparison benchmarking (% different from benchmark). Understanding energy breakdowns by end use, building, tenant, or other metric was routinely ranked high by managerial stakeholders, however many were skeptical about the cost effectiveness of using metering and sub-metering to produce the breakdowns or other advanced metrics.

Operations and engineering personnel, such as technicians, building engineers, and commissioning agents, preferred to have detailed information on equipment operation and data. Some of these technical stakeholders complained of the lack of trending and graphing capability (or flexibility) in their current systems, and they expressed a desire to see time series of operational data and simple operating state graphics condensed into one screen. Many desired to view raw data from different BAS and metering systems in one interface and to have options to view any data using multiple visualization methods. Presenting this data and related calculations on system graphics, equipment graphics, or zone graphics was well-received.

Many technical stakeholders expressed a need for the ability to drill down from high level building performance metrics into system operations and diagnostics. Most participants gave high ranking to basic operating information such as current operating conditions, recent trends in operations,

equipment runtimes, and setpoint compliance. Participants did express interest in diagnostic findings, which would illustrate which equipment and systems were underperforming or had faults causing performance issues, such as a leaking air handler valve causing simultaneous heating and cooling. On the other hand, many of the same participants expressed skepticism that these diagnostics could be accurate in either accurately finding faults or the projecting the energy costs of these issues. Example Data, Metrics and Visualizations for Advanced Operations

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11 Based on feedback from the interview participants, examples of advanced operations and maintenance interfaces were created and are available to the general public at the following location:

https://sites.google.com/a/kgsbuildings.com/rp1633/.

This interface includes the most commonly identified ‘useful’ metrics and visualizations from the participant interviews, and some additional ones beyond interviewee preferences. Interview participants were asked to survey the mock interface and to rank each metric and visualization on a scale of one to five, from least to most useful. Participation in this follow up survey has been very limited, with only 16.5% of participants responding to this final survey, but it is still open to participants and to the general public. The results of these surveys are presented in section five of this report. Common metrics ranked highly. These included basic information such as a simple cost table of building expenditures and building energy use intensities plotted over time and relative to other buildings in the portfolio or established benchmarks. Participants regularly expressed a preference for visualizations that clearly indicated what aspect of building operations to attend to whether in time, location, or within a system. For example this might include: a campus map showing color coded buildings based on deviations from expected performance or operations; a system graphic showing the component

exhibiting a fault and the nature of the fault; a table of projects or equipment prioritized by potential for savings; or calendar plots and time series indicating the points in time when issues worth investigating occurred.

Participants were also asked to provide additional feedback following the ranked survey responses. Managers expressed a consistent preference for summary information about the success of energy projects. For example, one participant said “The most useful section would be tracking of energy and cost savings projects.” This may reflect the role of most participants, as facility managers, and their need to communicate the effectiveness of facility investments. Many participants responded that the operations and diagnostics sections are important for day-to-day operations, and often missing from available interfaces today. For example, one participant stated that “the diagnostics portion of this survey would be the most useful area to identify quickly issues in the field and get them corrected. This is lacking in the industry and is now becoming the best method for continuous commissioning,” while another added that it would be “Even better if this [interface] is overlaid on BAS user interface.” Providing clear indications of equipment operational characteristics, and importantly equipment deviating from normal or outliers, was also important. For example, one participant noted, “For zone operations, would be very useful to know which zone is the worst (especially in worst-zone control schemes.”

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Tables

Table 1 Tools in Existing Tools Database

Table 2 Data visualizations in Graphics Database

Figures

Figure 1 Scoping interviews – Building types visited

Figure 2 Scoping interviews – Range of building sizes visited Figure 3 Scoping interviews - Types of stakeholders interviewed Figure 4 Financial decision-making processes

Figure 5 Participant sources of information about building performance Figure 6 Frequency of participant use of control systems and dashboards Figure 7 Data and information available from participant tools

Figure 8 Functionalities available in participant tools

Figure 9 Tasks performed by participants using control systems and dashboards Figure 10 Participant utilization of control systems and dashboards

Figure 11 Participant satisfaction with existing control system and dashboards Figure 12 Rated usefulness of new metrics and information

Figure 13 Rated usefulness of new graphical information

Figure 14 Sample calendar plot page from Interface Component interviews Figure 15 Participant profile for Interface Component interviews

Figure 16 Percent of participant approval of specific visualizations

Figure 17 Energy metrics preferences for portfolio and financial managers in Questionnaire 1

Figure 18 Benchmarking options preferences for portfolio and financial managers in Questionnaire 1 Figure 19 Visualization options preferred by managerial stakeholders for specific categories in

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15 Figure 20 Visualizations preferred by operations stakeholders for all categories in Questionnaires 5, 6,

and 7

Figure 21 Early prototype example interface design Figure 22 Example interface section organization

Figure 23 Typical example interface page organization and navigation Figure 24 Example interface main homepage

Figure 25 Example interface Costs homepage Figure 26 Example graphics from Costs page Figure 27 Example interface Utilities homepage Figure 28 Example graphics from Utilities page Figure 29 Example interface Operations homepage Figure 30 Example graphics from the Operations page Figure 31 Example interface Diagnostics homepage Figure 32 Example graphics from the Diagnostics page Figure 33 Example content from the Data page

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1. Project Objectives

Analyzing and interpreting building performance data to inform operations and maintenance is critical to the proliferation, retrofit and success of higher performance buildings [1] [2]9/11/2015 1:04:00 AM. Despite the growing ease in collecting building data [3], and increasing attention to performance measurement in buildings [4], there has been little research of metrics and interfaces that best serve building operations and maintenance stakeholders. There now exists a significant amount of guidance and standards on measuring the performance of buildings, primarily for bulk energy information, but with limited depth on metrics and visualizations to inform daily aspects of building operation or the unique needs of various building types [5] [6].

Recent ASHRAE research on Performance Measurement Protocols in Commercial Buildings [4] [7] has focused attention on the metrics relevant to tracking building performance. The research described in this report seeks to expand such investigations to consider graphical visualization of operational metrics and their arrangement within interfaces. This research seeks to focus attention on operations and maintenance stakeholders, including control technicians, heating ventilation and air conditioning (HVAC) technicians, service providers, commissioning agents, and facility managers. The goal of this project was to create guidance about data-driven metrics and visualization that clearly quantify and communicate building operational performance to a diverse set of building stakeholders.

The objective of the first part of this research was to obtain an understanding of the current state of the technology by evaluating building automation and control systems, energy dashboards, and other analytics systems that are available in buildings today. This study included a review of relevant research, creating a compendium of known building performance metrics, and a summary of existing commercial interfaces. In addition, interviews were conducted with over 80 stakeholders with various roles

responsible for managing hundreds of buildings across the U.S. During these interviews, we reviewed the types of systems and interfaces currently available to the participants, the types of data, metrics, and graphics presented in these systems, and how (or if) this information is being used. We also

assessed which performance metrics and graphical representations would be most relevant to each type of participant based on their reactions to a series of example visualizations. Based on the interview responses, we determined what types of metrics and graphical methods of presentation are most useful for building operation and financial decision-making for different types of buildings and by stakeholders with different sets of needs.

During the second part of this project, we used the sets of metrics and graphical visualizations selected in the first half of the project to create example interfaces. These interfaces were customized to meet the needs of several main types of stakeholders, including those with operational, energy, and financial interests. The dashboards were made available online so they could be surveyed and ranked by a group of volunteers from the original participants. This stage of the research moved beyond the static

graphical examples used in the original interviews by providing participants with an interactive environment that emulated a working building performance or operations interface.

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17 This report concludes with recommendations for the data, metrics and visualizations for interfaces that best serve the needs of advanced operations and maintenance in buildings. These recommendations are made based on the results of the state of the technology review, the initial sets of stakeholder interviews, and responses to the example interface survey.

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2. Project Tasks and Report Structure

This research project was conducted in six major tasks. These began with a scoping and review phase, in which we completed a review of the state of the technology and an initial set of scoping interviews. Based on our initial results, we then revised the stakeholder interview questionnaire to focus on more specific metrics and graphics and conducted a second set of interviews using the revised protocol. We then developed a series of wireframes for example interfaces based on the results of the interviews and the state of the technology review. These later evolved to become interactive dashboard prototypes, embedded in a web-based survey. We concluded the project by recruiting participants to review these interfaces and complete the survey, and by finalizing a list of recommendations based on the results of all six tasks.

During Task 1 of this research project, we began gathering information about the current state of the technology, including an assessment of the type of information, data driven metrics, and dashboard interfaces currently used in building monitoring and control systems. To obtain this information, we conducted a literature review of previous research as well as a review of existing tools, including Energy Information Systems (EIS), building automation systems (BAS), energy management and control systems (EMCS), energy monitoring dashboards, and other analytics products. We also developed a database of known building performance metrics and a library of example graphics from existing tools. Once we had established the state of the technology, we used this information to develop an initial interview

questionnaire and protocol for the stakeholder interviews. During Task 1, we aimed to complete roughly one half of the proposed total set of stakeholder interviews. For the first set of interviews, we met with 39 participants who worked in or managed a combined total of 23 different buildings, located primarily in the Northeast. This first round of interviews was more general in nature than later rounds and helped establish a baseline for the types of tools and metrics that participants currently had access to. The results of Task 1 are presented in Section 3: State of the Technology, and Section 4.1: Scoping Interviews.

In Task 2 of the project, we developed a more detailed questionnaire and a compendium of graphics to present specific types of metrics and example visualizations to interviewees. With the project

monitoring subcommittee’s guidance, these were reduced to a minimal set in order to facilitate 2 hour interviews. In Task 3, interviews with a second set of 40 stakeholders were conducted using the new questionnaire to collect preferences and ideas for example interfaces. The second set of interviews took place across the U.S. and again included stakeholders in a variety of roles in building operations and decision-making. The results of Tasks 2 and 3 are presented in Section 4.2: Interface Component Interviews.

During Task 4, the results of Tasks 1 through 3 were compiled and used to inform the development of interactive example interfaces with metrics and visualizations. These example interfaces were made available to participants on an online site, in which surveys were embedded to rank and collect subjective information about user preferences. The interfaces were divided into sections on costs, utilities, operations, diagnostics, and data visualization, and subdivided into portfolio, building, plant,

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19 ventilation, and zone scale information. The goal of this project was not to determine the optimal user experience or interface design for operations and maintenance, but rather to assess which specific metrics and visualizations were useful to operations and maintenance personnel, facilities managers, and financial stakeholders. In Task 5, these interfaces were made available to participants who were requested to complete a one hour survey to provide feedback on these interfaces. The results of Tasks 4 and 5 are presented in Section 5: Data, Metrics and Visualizations for Operations and Maintenance. The final task of this project is to report on the findings of the research. This research report

summarizes the work performed and resources created. It also provides recommendations from across this work on data, metrics, and visualizations considered useful specifically from an operations and maintenance perspective.

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3. State of the Technology

Because energy and building performance systems and dashboards are a rapidly growing and changing aspect of the building understanding, assessing the current state of available technology was critical to this research project. It is important to note, however, that this review only represents a snapshot in time for a fast-changing technology. For this study, we focused on three main areas: metrics and

information for building performance, graphical representation and visualization of this information, and the use of building automation and controls systems, energy monitoring systems, and other types of dashboards for building maintenance and operations.

As many of the advancements in this area are occurring directly in the marketplace, it was necessary to gather information about the tools and dashboards that are available in buildings today as well as to examine previous research. This section includes a literature review of previous research, a compilation of existing tools (initially conducted in 2012 and updated in fall 2014), and a summary of existing types of data, metrics, and graphical methods of representation used to assess building performance.

3.1 Literature Review

There have been a variety of previous studies that have examined data and interfaces for building operation, particularly in the areas of metrics for measuring building performance and dashboards for visualizing performance. This section includes a summary of this research.

3.1.1 Data, Metrics, and Information for Building Performance

As buildings become more complex and technology improves, building stakeholders have access to an increasing amount of data and information directly from the building itself. Information about a building’s performance may be available as data, metrics, or ratings. For this study, we consider “data” to be numerical, Boolean, or multi-state values that are obtained directly from a meter, sensor, or control system. Examples of data include room temperature, valve position, supply air flow, chiller power consumption, and whole building electricity consumption. Data may be available from a wide variety of meters and sensors located throughout the building, and an individual building may have thousands of available data points. Data may be accessed in numerous ways, including direct readings from meters or sensors on individual pieces of equipment, through building automation and control systems, through on-site workstations and kiosks, and through web-based and remote interfaces. We also consider “information” about a building useful in characterizing performance for operators, such as building floor area, mechanical system types, heating degree days or other climate data, and mechanical schedule information.

Performance metrics, also called performance indicators, differ from data and information in that they are generally not directly available from a sensor or meter but are instead calculated using combinations of data and other building information. Examples of metrics include Energy Use Intensity (EUI, or energy per building area), chiller kW/ton, photovoltaic cell efficiency, and occupant complaints per day.

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21 project, using quantitative criteria, in a dynamic, structured format.” Hitchcock lists a variety of

objectives that may be considered using metrics, including: energy efficiency; environmental impact; life-cycle economics; occupant health, comfort and productivity; and building functionality, adaptability, durability, and sustainability. As a part of ASHRAE Special Project 115: Performance Monitoring

Protocols, MacNeill et al. [9] completed a comprehensive review of literature relevant to building performance measurements. They identified the most relevant methods for quantifying building performance in several areas, including energy performance, indoor air quality, thermal comfort, acoustics and vibration, and lighting quality. They also developed an “Evaluation Matrix” that categorizes over 200 documents related to building performance measurements.

Although a wide range of metrics exists, it is clear from MacNeill et al’s research that there is currently no consensus on which metrics or sets of metrics should be used to define building performance. However, there is an ongoing effort to develop frameworks of standardized metrics, particularly for energy-related performance. The Performance Metrics Project through the U.S. Dept. of Energy’s Commercial Building Initiative, the National Renewable Energy Laboratory (NREL), and Pacific Northwest National Lab (PNNL) has defined a set of performance metrics with the goal of standardizing the

“measurement and characterization of building energy performance” [10] [11] [12]. Such metrics are highly specific and clearly defined, as the researchers involved in this study believed that reducing the possible levels of interpretation would thereby reduce the disparity among assessment results. The metrics are also organized by tier, which correspond roughly to stakeholder interest: Tier 1 includes a smaller number of more general metrics such as Net Facility Energy Use which are of interest to building owners or rating system sponsors, while Tier 2 metrics include a larger number of more specific metrics such as DHW System Efficiency, which are of interest to stakeholders involved in daily building

operations. In total, the metrics were divided into six categories (energy, water, operations/

maintenance, purchasing/waste/recycling, indoor environmental quality, transportation), and 4 levels of standard performance metrics are listed with increasing granularity. For example, the metrics for energy range from monthly total building energy use and cost (and total per square foot) at level 1 to monthly individual equipment energy per square foot and per occupant at level 4. The recommended operations and maintenance metrics revolve around total annual expenditures at level 1 and move to an accounting of work orders and individual procedural costs at level 4, and the indoor environmental quality metrics similarly revolve around space temperatures, CO2, and occupant satisfaction reports. Through this project, a set of procedures was also defined to outline how to set up the scope of a project, how to select metrics to be measured, how to identify the data and equipment required to obtain each metric, and how to analyze the metrics over time [11].

Around the time that the DOE Performance Metrics Project results were released, ASHRAE published a book on Performance Measurement Protocols, or PMP (the end result of Special Project 115 referenced above), in an effort to standardize building performance claims and measurement practices [4]. The earlier book identifies the metrics and appropriate measurement practices for building performance for six types of building information (energy, water, thermal comfort, indoor air quality, lighting, acoustics) from basic to advanced levels. At all levels, the energy metrics recommended include energy

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22 occupancy. Intermediate and advanced performance metrics are characterized by higher frequency and more granular data, although these recommendations are accompanied by the caveat that they might be cost prohibitive for the owner. The advanced level recommendations include self-referential energy use benchmark models, such as calibrated simulations or multi-parameter regression models, and a system-level granularity of energy consumption sub-metering at hourly or daily frequencies. A second book, published in 2012, acts as a best practices implementation guide for managing and improving the performance of buildings [7]. Although the basic level recommendations can be completed without reference to the BAS, the intermediate and advanced level recommendations require a moderate to complex BAS or EIS and a certain level of utility and other sub-metering.

Several other studies have considered the use of metrics for building performance assessment. Lee and Norford considered the use of energy performance metrics to benchmark a set of 49 schools in a school district in California [13]. Hitchcock’s research involved the development of a model for building performance metrics that is consistent with the Industry Foundation Classes (IFC), for use across a building’s life cycle [8]. O’Sullivan et al. [14] used an IFC-based model of a building at University College Cork as a case study for a building energy monitoring, analyzing and controlling (BEMAC) framework for life cycle building performance assessment, and Morrissey et al. [15] proposed a Building Information Model (BIM) to support this BEMAC framework. Neumann and Jacob defined the performance metrics that would be required for different steps or levels of continuous commissioning, including

benchmarking (operational rating), certification (asset rating), optimization, standard analysis, and regular inspection [16].

Building performance rating systems provide an additional way of assessing building performance. Unlike most available data and metrics, rating systems are generally used to rate or rank performance on a whole building level. Performance can be assessed as an aggregate of multiple categories of sustainability (such as with the LEED system) or it can be considered in only one category. Energy consumption or efficiency performance systems are probably the most common types of rating system. Given the many ways in which building performance is communicated, the US Department of Energy has adopted the Building Energy Data Exchange Specification (BEDES) which helps to facilitate exchange of building characteristics and consumption through a common dictionary of terms, definitions and field formats for use by software tools and or rating systems.

At present, there exist several different approaches to producing a rating or score for a building. Glazer [17] evaluated a wide variety of energy rating systems and identified three broad categories of

protocols: statistical (the building is rated based on where it falls in a statistical distribution of actual buildings), points (the building is rated based on how many points it gets in a long list of criteria), and prototypical (the building is rated based on comparison with good conceptual buildings, using simulations). Similarly, Olofsson et al. [18] describe three approaches for generating ratings: the simulated data approach (SDA) which compares real energy consumption to an ideal simulated version of the same building, the aggregated statistics approach (ASA) which looks at a wide population of buildings, and the expert knowledge approach (EKA) which is based on “expert surveys of

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23 and labeling as the three different types of ratings classifications, where labeling is defined as the equivalent to assigning percentile intervals to energy classes (ratings), i.e. buildings get ranked A, B, C, etc. based on where their energy performance falls [19].

One of the most popular statistical benchmarking rating systems is the ENERGY STAR Label for Buildings [20], which allows building owners and managers to compare the energy consumption in their building to that of similar buildings across the United States on a 100 point scale. To earn the Energy Star, a building must earn an Energy Star rating of 75 or higher, which indicates that it outperforms at least 75% of similar buildings. LBNL’s Cal-Arch system is a similar benchmarking system that is only applicable to buildings in California [21]. The EnergyIQ tool is an updated version of Cal-Arch which provides “action-oriented benchmarking”, providing guidance about the potential energy impact of a set of suggested actions (for example “install EMS lighting controls”) which have been generated based on the benchmarking results [22]. Although statistical benchmarking systems may be more commonly used than prototypical or simulation-based systems, the statistical databases used for such ratings may not be available for specialized buildings types such as laboratories. Labs21 is an example of a rating system that uses a simulation-based benchmarking approach to overcome this challenge [23].

Points systems are also common among rating systems, and include high-profile programs such as LEED [24] and BREEAM [25]. In the United States, LEED is possibly the most well-known rating system,

although other systems include BOMA 360 [26], Green Globes [27], and CHPS [28]. Ratings systems such as LEED generally assess building performance in multiple categories to determine overall performance, and in each category, credits or points are awarded based on fulfillment of various strategies for energy efficiency or sustainability. A rating or certification is then awarded to the building based on the number of points that the building is able to achieve. For example, the LEED system has four levels of certification (Certified, Silver, Gold, and Platinum) with Platinum requiring a building to achieve at least 80% of the possible credits. For this research project, the LEED rating system was found to be important in two ways. LEED EBOM (Existing Building Operations and Management) is of particular relevance to this study, as credits are available to a building which has a building automation system, energy meters, and/or more advanced building energy management systems. Additionally, during our review of existing tools, we found that new dashboard products are being offered which track LEED points for a building attempting to achieve or maintain a LEED certification (see section 3.2). The LEED rating system may ultimately be greatly influential to the use and development of control systems and dashboards. In addition to statistical benchmarking and points-based systems, labeling systems are gaining

popularity. These types of systems tend to use simple schemes to denote performance, such as report card letter grades. For example, ASHRAE’s Building Energy Quotient or bEQ [29] is a letter-based

grading system based on the actual and/or designed building EUI vs the median EUI for similar buildings. In additional to operational ratings, labeling systems may also be used to rate building assets, i.e. the energy potential of a building, such as that which is currently being developed for the state of Massachusetts [30].

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3.1.2 Visualizing Building Performance Data and Information

While building data, metrics, and ratings all provide extremely valuable information about a building’s operations and performance, the way in which this information is provided to a building stakeholder may be equally important. A building with a modern control system may have hundreds or thousands of data points that are updated at frequent intervals, and it would be difficult or impossible for a building operator or manager to process that much raw data in a useful or efficient way. While building performance metrics and rating systems offer ways in which raw data can be processed into more condensed non-graphical forms, display of both raw data and metrics in graphical formats such as scatter plots and daily or weekly profiles can help a building stakeholder view and analyze large amounts of building data very efficiently [31]. Graphical display of data in plots and graphs can also be helpful for diagnosing building equipment faults [32].

One important consideration for the visualization of building information is the target audience of the tool. Marini et al. [33] conducted a study in which a dashboard was installed in a federal building. Five different user categories were considered, with different granularity of information available to the different user groups. Some of the lessons learned included: information should match the user, dashboards should transform data to information, and dashboards can help knowledge lead to action. While most control system interfaces are geared towards building operators and engineers, other types of dashboards have emerged which are aimed towards different stakeholders, such as regional

managers and financial stakeholders. Additionally, the term “eco-visualization” has been used to describe visual displays aimed at promoting sustainable behavior in building occupants. These have been proposed as public displays of information and may exist in two forms: pragmatic, which use formal elements from scientific visualization; and artistic, which may use more ambiguous imagery [34]. An example of an artistic representation is found in [35], in which visualizations of trees are used to represent carbon emissions. In existing tools today, a wide variety of plots and graphs may be used for visualizing building data and metrics (discussed further in section 3.3).

3.1.3 Interfaces and Dashboards for Building Operations, Monitoring, and Controls

Interfaces and dashboards provide interactive settings in which data, metrics, and graphical information about a building may all be displayed to a building stakeholder. Building automation and controls systems (or similarly energy management and control systems, building automation systems, energy management systems, and other names) represent one of the more common types of systems that building operators, engineers, and managers may interact with regularly in buildings today. However, a variety of other systems, such as energy monitoring dashboards, enterprise energy management

systems, energy information systems (EIS), advanced analytics or fault detection and diagnostic systems, and other types of tools have emerged in recent years. The tools that are currently available will be discussed further in section 3.2.

In 2014, ASHRAE released an updated version of Guideline 13, Specifying Building Automation Systems [36]. This guideline is meant to help someone construct an effective specification for a Building Automation system, and it promotes capabilities such as open protocols, system interoperability,

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25 custom reporting, data trending and trend visualization (both time series and scatterplot), remote or portable terminals, and applications like demand limiting, energy calculations, and anti-short cycling, as well as more traditional BAS features. In Annex D, Guideline 13 also points out the management and energy saving benefits of building performance monitoring on both the building and equipment levels, either as part of the BAS, or as a separate EIS . It identifies three levels of performance monitoring, from simple data trending to sophisticated diagnostics of equipment faults, operational issues, and power quality, and calls fault detection “a natural enhancement to monitoring the performance of an HVAC system.” Annex D references the recent ASHRAE Performance Measurement Protocols for Commercial Buildings: Best Practices Guide [7].

In a recent cost-benefit analysis of 26 EIS case studies (23 of which were in-depth), Granderson et al. found that 21 of 23 in-depth cases attributed significant savings to the installation of an EIS [37]. Among the factors associated with greater energy savings were pre-EIS site EUI (how wasteful the building was before the EIS), length of time since EIS installation, higher-granularity instrumentation, consumption benchmarking, regular load profiling, and consumption anomaly detection. Also, on the list of

operational efficiency best-practices were the use of time series visualizations to study load profiles and the use of x-y scatterplots to asses load vs outdoor temperature.

Much of the past research that has been done in the area of building systems and interfaces has focused on EIS, which typically include building automation and control systems in addition to tools with related functionalities such as demand response management and enterprise energy management. Granderson et al. [38] created a framework to characterize and classify EIS tools. From an overview of existing tools, they found that visualization and analytical features are distinguished by their flexibility, and that rigorous energy analyses (baselining, forecasting, anomaly detection) are not universal. They also conducted a small number of case studies in which the use of EIS tools in real buildings was evaluated. Some of the conclusions from the case studies were that data quality has significant impact on EIS usability and that while EIS may offer a wide range of features, actual use of those features may be limited. Other case studies of EIS use in real buildings include Motegi et al. [37] and Kircher et al. [39]. In addition to EISs, energy monitoring dashboards are a growing trend. Lehrer and Vasudev [40] interviewed building managers and design professionals and found that such tools are currently being used in similar ways to BAS/EMCSs. The authors found that some of the users’ key needs were: High-level overview with drill-down capabilities, integration of energy visualization features with data analysis, and compatibility with existing BASs. We will discuss the results of our stakeholder interviews, in which both BAS/EMCS and dashboard systems were evaluated, in section 4.

3.2 Existing Tools

A significant aspect of this research was to identify and compile a list of existing tools for building operations, maintenance, and decision-making. These tools included general building automation and control systems, energy or resource monitoring systems, enterprise energy management systems, and

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26 systems with more advanced analytics, such as optimization, fault detection, or demand response functionalities.

The current database contains information about 70 different tools, compiled between December 2011 and November 2014 (Table 1). These tools were identified using previous studies [34] [35] [36] [37], recommendations by the PMS and others in industry, internet searches, building visits, and stakeholder interviews.

For each existing tool, the database entry includes a short summary, categorization by intended audience, categorization by content or functionality, a link to a folder of example interface graphics (if available), and a website link. The database is constructed in Microsoft Excel. The excel file must reside in the same main folder as the folder of example graphics for the links to function properly. Tools were categorized based on publicly available information, some of which consisted only of marketing

material, or based on feedback gathered in stakeholder interviews. All attempts were made to correctly categorize each tool; although in some cases it was not possible to fully determine what functionalities were available based on the available information.

Because we were interested in the variety of tools available to different stakeholders, it was important to try to understand the audience(s) towards which each tool was targeted. The possible categories for intended audience that we considered were: financial or enterprise manager, facilities manager, field personnel, and occupants or general public. We found that most tools were relevant for facilities managers (94%), with many tools available for financial or enterprise managers (76%) and field personnel (64%). Tools for occupants and the general public were the least common (15%). In addition to intended audience, we also attempted to categorize each existing tool by content or functionality (if such information was available). The categories we considered were: educational content or public display (such as energy monitoring kiosks), enterprise or campus level views (data or information over multiple buildings available at once), energy or utilities monitoring, ENERGY STAR or LEED information, real-time equipment data (such as that typically available in a building controls system), optimization features, equipment fault detection and diagnosis (FDD), demand response (DR), and retrofit recommendations or calculated ROI.

We found that the most common feature in the tools and dashboards we considered was energy or utilities monitoring (90%). While such systems are typically found only in high performance buildings today, it remains to be seen if such tools will eventually become commonplace for building operations. Other common features offered by existing tools were real-time equipment data (57%), and enterprise or campus level information (56%). The least common features were educational/public content and retrofits or ROI (both 14%), followed by FDD and DR (both 17%).

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Table 1 Tools in Existing Tools Database

Vendor Product Name(s)

Agilewaves (now SeriousEnergy) Building Optimization System and Resource Monitor

AirAdvice BuildingAdvice, Energy Kiosk

Apogee Interactive Progress Insights

AtSite InSite

Automated Building Systems Energy Dashboard

Automated Logic WebCTRL

BCM Controls BAS and Energy Dashboards

BuildingIQ BuildingIQ

C3 Energy Resource Management C3 Enterprise Energy Management Platform

Carrier Building Control Systems with iVu

Chevron Energy Solutions UtilityVision

Cimetrics Energy Kiosks and Displays, Analytika

CISCO Building Network Mediator

Computrols Computrols Building Automation System (CBAS)

CopperTree Analytics Kaizen

Di Mi Di Mi Speaks

DEXMA DEXCell Energy Manager

EcoDomus EcoDomus Facilities Management (FM)

Ecova Building Monitoring and Alerting, Continuous Building Optimization

ELUTIONS ELUTIONS Energy Management

EnergyICT EIServer and EIDashboard

EnergyPrint EnergyPrint

EnerNOC DemandSMART, EfficiencySMART Insight

EnVINTA One2Five Energy, Energy Callenger, EnterprizeEM(?)

Envizi Envizi

ESI Building Performance Manager (powered by SkyFoundry)

eSight eSight Energy

Ezenics Ezenics

Facilities Dynamics PACRAT

FactoryIQ EnergyPoint

Field Diagnostics Synergy

FirstFuel (formerly iBLogix) FirstFuel Rapid Building Assessment platform

GridPoint GridPoint

HARA EEM EEM Suite: Discover, Plan, Act, Innovate

Honeywell Energy Management Solutions, Enterprise Buildings Integrator, Attune

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IBM TRIRIGA

Iconics Facility Analytix, Energy Analytix

Intelligent Energy Solutions Eniscope

IFCS Corp. and NRCan DABO

Integrated Building Systems Intelligent Building Interface System (IBIS) Interval Data Systems EnergyWitness

Johnson Controls (EnergyConnect) GridConnect

Johnson Controls Metasys and Sustainability Manager

Johnson Controls Panoptix

KGSBuildings Clockworks

LBNL EnergyIQ

Lucid Design Building Dashboard Network & Building Dashboard Kiosk, BuildingOS

NorthWrite/Energy WorkSite/Onset Energy WorkSite

Novar Opus Energy Management System

Noveda Monitors, Facilimetrix, Portfolio Operator's Portal

NStar EnergyLink

Opendiem (by Building Clouds) Opendiem Energy Manager Panoramic Power Energy Management Solutions Periscope (ActiveLogix) Periscope Dashboard

PNNL/Honeywell/Univ. Colorado Whole Building Diagnostician (WBD)

Powerit Solutions Spara EMS, Demand Control, Demand Response, and Price Response

Pulse Energy (now EnerNOC) Pulse Energy Dashboard

QA Graphics Energy Efficiency Education Dashboard Quality Attributes Software (QAS) IBBuilding, IBCampus, IBEnterprise Apps

Retroficiency Retroficiency Dashboard

SAIC Enterprise Energy Dashboard (E2D)

Selex ES DiBoss

Schneider Electric Struxureware, Resource Advisor, Energy Operations, Vista and Continuum

SCIenergy (formerly Scientific Conservation ) EnergyScape

Serious Energy Serious Energy Manager

Siemens APOGEE and TALON products, Siemens Advantage

Navigator

SkyFoundry SkySpark

Teletrol (Phillips) eBuilding

Trane Light Commercial System Controls, Tracer Building Management Controls

Tridium Vykon Energy Suite (VESAX)

Verisae vxCONSERVE, vxMAINTAIN

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Wegowise Wegowise

It is important to note that, at the time of writing the initial list, the industry was changing rapidly, and this list of tools grew and changed as this final version was actively updated. During 2011 to 2014 while this project was underway, several new systems were introduced into the market and a few companies merged their products. More new tools emerged as interest and demand in energy management tools with dashboards and interfaces for different stakeholders grew. This list has been updated and included in this final version of the report. Even so, this updated list of tools serves to illustrate the wide variety of products that are currently available to buildings today and the general trend towards energy and performance monitoring that has emerged over the past decade.

3.3 Existing Metrics and Graphics

In addition to identifying existing tools, we developed databases of metrics and graphics used to evaluate building performance and aid in operational and financial decision-making. The metrics database attempts to provide a comprehensive overview of the types of data, metrics, and other information that is or could be made available in building automation systems, energy dashboards, and other analytics systems. The graphics database summarizes the types of graphical representations that can be used to present these metrics and information to the user from within an interface or dashboard.

3.3.1 Metrics Database

The existing metrics database includes ten different categories of performance data, metrics, and information. These categories include

 General weather or temperature  Whole facility (including utilities)  Renewable energy systems  Energy end use or system

 Cooling system components and equipment  Heating system components and equipment  Ventilation system components and equipment  Lights and plug load components and equipment  Benchmarking and standards

 Facilities and maintenance

Within each category, different types of metrics were identified based on previous research in building performance metrics [8] [10] as well as the information available about existing tools.

Each metric identified was categorized based on type (for ex., raw data, normalized, or calculated metric), relevant measurement interval(s), relevant unit(s), possible normalizations, and context (site, source, or cost). For each metric, example units were also given. For example, Energy Intensity (total building energy consumption) can exist as raw data or as a normalized metric, can be collected at intervals such as daily, weekly, monthly, or annually, can be presented in units of energy or cost, can be

Data and Interfaces for Advanced Building Operations and Maintenance - RP 1633 Final Report