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Presented at the Cool Sense National Integrated
Chiller Retrofit Forum, Sept. 23 - 24, 1997, San Francisco,
California, LBNL 40512 rev.2
Large commercial, institutional and government buildings generally
do not operate at economically achievable levels of energy efficiency.
Performance monitoring projects across the US have documented
the potential to conserve 15 to 30% of energy use through improved
operation and maintenance practices. Corresponding energy and
capacity reductions for large office buildings in California are
estimated to be about 42 * 1012 Btu (source) in existing
buildings, which includes 24 BkWh (site electricity) and 32 BkBtu
(gas) for large office buildings. The objective of this multi-year
project is to develop and apply state-of-the-art continuous building
performance measurement and supporting information processing
and data visualization technologies. These technologies will
diagnose problems in the performance of building energy systems
and provide owners and managers with reliable, decision-oriented
information. The project's goal is to assist building owners
and property managers in effectively reducing energy use through
improving O&M practices and implementing opportunities for
cost-effective investments in improved building energy systems.
The project was conducted by an interdisciplinary team to assess
the current state of technology, develop an appropriate information
and diagnosis capability, and test it in real buildings. The
research team designed a multi-level building diagnostic system,
including sensors, computer-based communications, data archival/retrieval,
diagnostic information processing, data visualization and other
components that would meet the needs expressed by building owners
and property managers. The research team developed diagnostic
and information visualization algorithms at these three levels:
the whole building (Level I), the overall building cooling system
(Level II), and the chiller and cooling tower subsystems (Level
III).
Buildings generally do not perform as well in practice as anticipated
during the design stage. There are many reasons for this, including
improper equipment selection and installation errors, the lack
of rigorous commissioning and proper maintenance, and poor feedback
on ongoing performance, including energy performance. These problems
are prevalent in most building systems, and frequently found in
dynamic systems such as heating, ventilation, cooling, and lighting
controls.
This project was conceived to develop and introduce state-of-the-art
information technology in buildings in order to substantially
enhance building energy performance by continuously improving
operation and maintenance (O&M). The project is being conducted
by an interdisciplinary team to assess the current state of technology,
develop an appropriate information and diagnosis capability, and
test it in real buildings.
This paper summarizes the key results from the first phase of project (Sebald and Piette, 1997). The scope of the Phase 1 effort included:
Early in the project we decided to focus on large Class A commercial
office buildings. These buildings are managed by large property
management companies and we think there are potential "early
adopters" among these companies. The Phase 1 market assessment
activities included in-depth interviews with six technical managers
who had been identified as the most sophisticated in California.
These interviews included a review of their perceptions of operations
and maintenance problems with all major building systems, including
controls. We found it difficult to identify a single system or
component that was most problematic. Rather, there are systemic
problems associated with the lack of feedback available from current
Energy Management and Control Systems (EMCS). Today's EMCSs are
designed for control, with extremely limited capabilities in sensing,
archiving, data analysis, diagnostics, and data visualization.
In the first phase of the project we concluded that significant
benefits could be derived by large commercial and institutional
building owners1 and operators
from developing and demonstrating
the use of a continuous performance tracking and diagnostic system.
We produced a conceptual design for a diagnostic system that
would provide continual feedback on building performance and assist
operators in diagnosing operational problems. The performance
data and data visualization techniques developed were designed
to report on common failure modes for specific building systems
discussed below. The data visualization techniques were designed
to be effective computer-based graphics that display the most
important metrics to describe system performance. These graphics
were developed from basic engineering principles for the whole
building and the water side of the cooling system. The system
also includes automated data collection and analysis software
that prepare the data for the visualization system. Together,
the data collection, analysis, and visualization systems should
serve as a robust and reliable platform for an integrated and
automated diagnostic system. The evolution toward automating
the diagnoses will be include in the Phase 2 research efforts,
which began in mid 1997. The diagnostic system should be useful
allocating operations and maintenance (O&M) resources over
the short run and capital investments over the long run.
The proposed diagnostic system differs from previously developed
systems in several important ways. First, it is specifically targeted
toward sophisticated building operators and engineers. Most related
research efforts or related techniques are targeted toward a remote
expert user. Second, the proposed system is designed to be permanently
installed. Related approaches that are known for being easy to
use are built around short-term rather than continuous monitoring
systems. Third, the monitoring system is based on laboratory-quality
sensors that are far more reliable than sensors found in most
commercial building systems. Fourth, the proposed system continuously
archives data on approximately 80 points once each minute. Most
current systems do so every 15 minutes or longer. Fifth, the
diagnostic system has a top-down design that logically flows from
the general whole-building analysis to system and component diagnostics.
This is in contrast to bottom-up approaches that attempt to detect
performance failures associated with specific individual devices.
These five features, (1) sophisticated building operators and
engineers as users, (2) permanent installation, (3)
laboratory-quality sensing, (4) short-interval data archives,
and (5) top-down design, are critical to our proposed system.
Much of this report is devoted to explaining how these departures
from previous approaches increase the probability that the proposed
system will perform well. As an initial test of the response
to our ideas, we visited additional buildings and held two technical
seminars with the managers to assess their reaction to the proposed
diagnostic system. Approximately one dozen building managers have
formed the core of our contact base; they have all expressed strong
support for our approach and expressed an interest in participating
in demonstration projects.
There are difficult trade-offs between advancing the automation
of the diagnostic systems versus designing the system for optimal
human-based diagnostics. The development of automated diagnostics
can be justified by the recognition that building systems are
becoming more complex over time and are difficult for the average
operator to understand (Hyvarinen, and Karki, 1996). We have
chosen to work with the most sophisticated operators we can find.
But, we will explore how to automate some of the "expert
diagnosis" so that the system could be developed for a broader
set of users. Automated diagnostics consist of methods to detect
faults and may include identification of fault sources. Most
diagnostic systems include a general user interface to aid the
human user in getting information about the detection of a fault.
Automated diagnostic systems generally include model-based (e.g.,
simple functions, physical, or black-box) fault detection and
classifiers (knowledge or association based).
The demonstration in Phase 2 is oriented toward ensuring that
the diagnostic system is cost-effective. Average reductions in
whole-building energy savings of 15% have been demonstrated by
compiling and assessing whole-building and major energy end-use
data (Herzog and Lavine, 1992). Savings from 20 to 40% have
been demonstrated in some cases (Claridge et al, 1994). With
average whole-building energy costs (gas and electricity) of about
$2/ft2-year, a 15% reduction translates to a savings of
$0.30/ft2-year. This amounts to $150,000/year for a 0.5
million ft2 building,
which is the size building the demonstration projects are oriented
toward. We anticipate that mature market costs for the diagnostic
system will be under $75,000 in hardware and software. Thus,
the proposed system will be designed to be cost-effective for
large buildings. It is worthwhile mentioning that by many accounts,
these savings estimates are conservative.
Looking statewide, the target technical potential energy savings
is a fifteen percent reduction in large office building energy
use for California. Large office buildings (those greater than
50,000 sqft) in California consume about 24 BkWh (site electricity)
and 32 B kBtu (gas). Fifteen percent savings in office energy
use translates into 41.8 *1012 Btu
(source)2. There
are about 11,000 large offices in California, which at about 100,000
sqft each, total 1.1 Bsqft of office space. With average energy
intensities of about 22 kWh/sqft-year for electricity and 31 kBtu/sqft-year
for gas requiring about $240,000 per year, a fifteen percent savings
translates into about $35,000/year (Akbari et al, 1993 and Akbari
et al, 1991).
Additional peak demand savings are likely, and not included here.
Furthermore, it is likely that with the use of a diagnostic system
like that described below, additional HVAC savings are possible.
These additional savings are likely to be achieved from improved
equipment sizing given reliable data on actual cooling loads.
Some examples of this are presented. Finally, greater statewide
savings are also possible from extending these technologies to
other building types.
One of the project sponsors, the
California Institute for Energy Efficiency,
asked the project team to focus on large Class A office
buildings. Class A office buildings represent the most prestigious
buildings in the office market3.
They have above average rents,
high quality standard finishes, state-of-the-art building systems,
exceptional accessibility, and a strong market presence. Class
A building owners and operators are among the most sophisticated
in the commercial sector. They are considered innovative, and
early adopters of emerging technologies. Several building owners
and operators have expressed a strong interest in collaborating
with the research team on a field demonstration of the prototype
diagnostics system. One benefit of performing the field test
demonstrations with Class A building operators is that the property
management companies who operate the buildings tend to have large
real-estate portfolios. They indicate that they are eager to
utilize lessons learned during the demonstrations in their other
buildings, thus the approaches we explore could propagate rapidly
throughout similar buildings. Additional factors regarding the
selection and advantages of collaborating with this market segment
are described below.
One of the important activities in Phase 1 was to identify major
O&M problems in commercial buildings. We assumed a broad
definition of the concept of an O&M problem: an O&M issue
that caused an increase in energy use beyond the expected performance
of a building system. For example, a cooling tower that operates
when the chillers are off causes unnecessary energy use. The
expected performance is for the tower schedule to be coordinated
with the chiller schedule, but such problems are common in commercial
buildings.
Our initial plan was to develop a list of the major classes of
O&M problems along with their severity and frequency. This
plan had to be modified due to the broad nature of O&M problems
found in commercial buildings, as further discussed below. The
two primary sources of information use in this task were: review
of published literature and analysis of existing case study data,
and interviews with building engineers and operators, and industry
O&M workshops. The review of published literature included
evaluating conference proceedings and journal articles discussing
the relationship between O&M and energy use in commercial
buildings. Research results from demonstration projects and programs
such as Texas A&M University's
LoanSTAR program (Haberl et
al, 1996),
PG&E's
Advanced Customer Technology Test (Hernandez and Brohard, 1994),
Bonneville Power Administration's
Energy Edge Program
(Piette et al, 1994), and
Pacificorp's
Energy FinAnswer Commissioning program
(Piette et al., 1996, Yoder and Kaplan, 1992) were considered.
The literature review suggests that virtually all buildings have
some sort of O&M problem, and the vast majority of buildings
are not carefully commissioned. Previous published case studies
indicated that careful review of hourly end-use and whole-building
energy performance data can result in savings equivalent to about
15 percent of annual operating costs (Herzog and Lavine, 1992,
and Claridge et al, 1994). These savings are much greater (up
to 40 percent) in some cases.
The second source of information we drew upon was the results
of detailed, personal in-depth interviews and feedback from building
owners and operators. These interviews were based on an extensive,
50-page, questionnaire designed to tabulate O&M problems and
characterize building owners' and operators' experiences with
diagnostic and control technologies. The questionnaire contained
detailed sections asking about commissioning practices, organization
of maintenance activities, the use of preventative maintenance
diagnostic technologies (e.g., vibration analysis, thermography,
etc.), Energy Management and Control System (EMCS) sensor maintenance
and calibration. The research team toured fifteen facilities to
obtain first-hand experience with current control technologies
and the way operators interact with them.
The on-site interviews were based on an O&M questionnaire
designed to structure information collected from building personnel.
The idea was to identify their most important problems in O&M,
after which we would tabulate and focus on the most common ones.
Instead of generating these kinds of seemingly straightforward
results, the underlying problem turned out to be much more complex.
We expected, but did not generate a list of common specific problems
for which we could generate a clean set of diagnostics. The difficulty
with identifying all common O&M problems is that reports of
these problems tend to be anecdotal rather than statistically
significant. Instead of identifying a detailed set problems,
we found a more critical and diverse set of structural problems
that need to be addressed by any successful diagnostic approach.
The key problem we identified is that building operators lack good information on major building systems we identified is that the information tools currently in use in these buildings severely limit a building managers' ability to assess their own O&M practices in a comprehensive manner. As mentioned, the questionnaire had sections asking about continuous information systems such as EMCSs, as well as one-time and short-term diagnostics such as vibration analysis and thermography. The most significant conclusion from the surveys and literature search is that building managers have little information on the energy performance of their major building systems, such as the cooling plant, lighting, and ventilation. They therefore have very limited capabilities to:
The general problem of lack of information on major building systems
comprises a series of specific problems. For example, the most
sophisticated building managers reported problems in keeping sensors
properly calibrated. Thus, the information directly available
from the EMCS is questionable. Temperature, humidity, and flow
sensors were all reported as problematic, with the most concern
over humidity and flow sensors. The following five constraints
were also found to be significant in evaluating opportunities
for a continuous diagnostic system.
Another important element of Phase 1 was the analysis and application of technology innovation and adoption theory Figure 1). We selected Class A building operators because of their role in the commercial building market as "early adopters" of advanced technologies. We purposefully worked with the Building Owners and Managers Association to identify the most sophisticated and innovative building engineers and operators in California. The analysis is based on the classic work by Rogers (1983) who suggested that technology adoption can be described by five categories: innovators, early market, early majority, late majority, and laggards. As an example of how the categories differ, "innovators" pursue technology and sometimes make a purchase simply for the pleasure of exploring a new idea or device, while "early adopters" are interested in new technology for its own sake and are quick to understand and appreciate the benefits of new products.
Figure 1. Stages in the Technology Innovation-Decision Process (Rogers, 1983).
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The analysis is based on the classic work by Rogers (1983) who
suggested that technology adoption can be described by five categories:
innovators, early market, early majority, late majority, and
laggards. As an example of how the categories differ, "innovators"
pursue technology and sometimes make a purchase simply for the
pleasure of exploring a new idea or device, while "early
adopters" are interested in new technology for its own sake
and are quick to understand and appreciate the benefits of new
products.
The companies selected for the O&M surveys had the characteristics
that Rogers deemed important. First, they had some organizational
"slack" to pursue new ideas and had developed a method
to analyze innovations utilizing this slack time. Second, they
had made someone in their organization responsible for the technology
strategy. Although they do not have formal R&D departments,
they have identified a role of technology evaluator. Finally,
they had demonstrated by past performance that they could think
creatively and would act on new information in previous innovations
we evaluated.
After identifying the innovative operators we sought to identify
the process of innovation utilized in the past by evaluating the
process used to adopt related characteristic innovations. These
"scouting" studies resulted in an understanding of the
business and technical constraints and incentives for innovations.
Specifically, we found that the technical managers responsible
for innovation frequently conducted pilot studies with their own
operating budgets. Furthermore, we found that the technical managers
responsible for innovations were limited to evaluating simple
components and were unable to undertake large scale studies of
potential "system-wide" technologies because they could
not justify the cost and time for such studies.
Another important task in Phase 1 was to investigate and evaluate diagnostic methods, tools, and techniques for inclusion in the current project. We conducted a broad review of possible approaches for diagnostics and determine the degree of technical maturity with which each has been applied to building problems. We defined a set of criteria and then evaluated options in terms of these criteria. Our analysis considered issues such as required sensor and communications technology, bottom-up versus top-down diagnostics architecture, and the design of temporary versus permanent systems. We also examined the status of techniques from the field of intelligent systems (e.g., artificial intelligence, fuzzy logic, neural networks) and diagnostics used in process control industries. A diagnostic system comprises the components depicted in Figure 2.
Figure 2. A depiction of the components of a diagnostic system
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There are many approaches for each of the components, so one must define a set of criteria and evaluate the suitability of the approaches. We used the following six criteria:
The overall architecture of diagnostic systems includes:
We concluded that for that for the target Class A office buildings, a top-down architecture is promising because it is integrates better and costs less to design than bottom-up systems. Bottom up systems detection of performance failures associated with specific individual devices assuming a fixed range of operating conditions (in the face of the great diversity of conditions found in real-world applications) (Hyvarinen and Kohonen, 1993, Hyvarinen, and Karki, 1996). Similarly, human assistance in fault detection appears more promising in the near term. Increased automation is a viable longer term strategy as data collection is improved and automated techniques will be easier to build. Automated techniques require training statistical models with data sets that are limited by current building monitoring systems. After building the models one must test their ability to detect various categories of faults.
Scope
The proposed system requires sensing of energy, weather and water
side variables (temperatures, pressures and flows). Sensors commonly
used in buildings are typically not adequate due to durability
(frequent failures or falling out of calibration) and accuracy
problems (e.g. measuring flows accurately is crucial, but typical
systems either do not measure flow or do so with inadequate accuracy).
Less accuracy is needed for day-to-day control than is needed
for diagnostics and evaluation of equipment performance. Typical
EMCS data are not used for precise measurement of cooling tons
or energy consumption. Rather, these data are used for relative
control. However, if one approaches a building with an interest
in characterizing control strategies, schedules, loads, and efficiency
levels, current EMCS data are often not reliable.
Another way to think about the value of high quality data for
large cooling plants is to consider cooling energy as liquid money
(Houghton, 1996). A 3,000 ton chiller plant represents $300/hour
of operating costs for 1 kW/ton and 10 cents/kWh. Therefore,
the cost of a $6000 magnetic flow meter (first costs plus installation)
is equivalent to only 20 hours of operation. With 2,000 hours
of full-load operation, a five-percent error in measurement could
reach $30,000/year.
The proposed system consists of 85 monitoring points including
high-grade thermistors, power meters, magnetic flow meters, aspirated
psychrometers, and a variety of similar monitoring systems, with
data archived every minute. The monitoring equipment is listed
in Table 1.
Table 1: Systems and Sensors of the Diagnostic System.
| Whole Building | Power | +/- 0.25% |
| Chillers | Differential Pressure (water)
Water Temperatures Flows (water) Power (to chillers) | +/- 0.25%
+/- 0.25% +/- 1.00% +/- 0.25% |
| Pumps | Differential Pressure (water)
Power | +/- 0.25%
+/- 0.25% |
| Cooling Tower | Dry Bulb Temperature
Aspirated Psychrometer Water Temperatures Power Flow | +/- 0.25%
on-site calibration +/- 0.25% +/- 0.25% +/- 1.00% |
| Local Micro-Climate | Dry Bulb Temperature
Aspirated Psychrometer | +/- 0.25%
on-site calibration |
The three levels and associated systems chosen for the diagnostic system are the following (as shown in Figure 3):
Figure 3: Three Levels of the Diagnostic System
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The rationale for the selection of these systems is as follows.
First, the selection of whole-building diagnostics is the starting
point of the proposed diagnostic system. Whole-building data
contain the basic yard-sticks by which a building operator can
get an overall set of metrics to evaluate building performance.
The rationale for the selection of the cooling system is related
to the benefits of working with this system, plus the difficulties
related with the competing systems of lighting or ventilation
systems. First, we found that there can be a major improvement
in cooling plant analysis with the addition of improved metering
equipment, such as thermistors and magnetic flow meters. With
the addition of these systems, we will characterize the energy
efficiency of the cooling plant. Chillers are the largest single
energy-using components in large office buildings, and are thus
one of the most logical items to evaluate. Evaluating the entire
cooling plant will allow us to understand the overall system performance,
which is more important than examining a component in isolation
from the system.
For comparison, the measurement issues associated with ventilation
and lighting are more distributed; literally distributed throughout
a building. Measuring air flows is particularly problematic.
A similar confounding issue with ventilation systems is that
ventilation requirements in individual zones vary because of duct
configurations and thermal variations. The result is that ventilation
systems are difficult to evaluate, and are complex because they
are coupled to tenant comfort and indoor air quality issues.
These were determined not to be good candidates for the initial
demonstration, but are suitable for future research. Our initial
diagnostic system, by contrast, is restricted to monitoring cooling
plant equipment that is located either in the central plant or
on the rooftop.
The components selected for the analysis are chillers and cooling
towers. Both of these components were targets of complaints from
building managers as subjects of poor sizing. Chillers are often
oversized, thus they require more power per ton than optimal because
they are less efficient at low partial load. Cooling towers are
often undersized. Larger towers allow the chiller to operate
at cooler condensing temperatures. The diagnostic system will
explore major failure modes for these components.
The IMDS will be a permanently installed and continuously active
system. This is necessary because buildings continuously change.
For example, some problems reoccur, such as those from modifications
to schedules to handle special events. These modifications often
lead to equipment being left on when not needed. The diagnostic
system is designed to operate in parallel with any existing EMCS,
rather than expanding or modifying the EMCS. This decision was
made because current EMCSs do not have the necessary capabilities
in sensing, network capacity, frequency of sensing, data archives,
data processing and visualization.
Knowledge Base and Failure Modes
The Phase 1 study included a detailed analysis of performance
metrics and benchmark data to characterize the fundamental principles
of how building, system, and component performance can be characterized.
We developed a series of standard graphics that will allow the
metrics to be displayed in a manner that assists in the diagnosis.
These graphs were analyzed to determine benchmark signatures
for good performance, such as where measured values should fall
on a given analysis plot, or what the curve shape should look
like if the system or component is performing properly. We developed
a series of measurements and sensing requirements to evaluate
the systems and components. We also listed common modes of failure
that one can diagnose with the given metrics and graphics based
on case study data and related literature. The discussion of failure
modes is not an entirely exhaustive list of failures, but covers
common and critical modes of failure. In most of these categories,
we have used the following relationship to search for efficiency
increases (i.e. detect problems). There are three categories
of deficiencies: efficiency degradation, scheduling, and load
issues. For example, in a chiller system, these three categories
can be summarized by:
For each level in the hierarchy, a step by step diagnostic procedure
was developed. The proposed knowledge base is designed to be
modular, with a set of standard graphs and standard information.
These graphs also serve as a tutorial that is designed to orient
the building operator on how best to understand the system or
component's energy performance.
As mentioned, whole-building data are critical elements of a top-down system, allowing an operator or building owner to evaluate the annual energy use and annual energy costs. For comparison with other buildings, these data are typically normalized by floor area, to produce an energy-use intensity (EUI). As examples, we present two sample diagnostic plots at the whole building level. In Figure 4, benchmark whole building EUIs are plotted for comparison with a given actual building. Data sources for this plot include: EIA, 1995, Akbari et al, 1993 and 1989, Piette et al, 1986, and Energy User News, 1995. One compares these values with those of the current building to determine whether large differences exist. Independently, or as an adjunct to results from Figure 4, one can detect and diagnose time of day usage problems with three-dimensional, hourly whole-building graphics. Excessive off hour use can be easily detected with such plots.
Figure 4: Office Building Energy Use Intensities for Comparison
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Examples of key failures that can commonly be identified with whole building hourly data include:
The entire cooling system efficiency can be evaluated using the
efficiency versus load analysis (kW/ton vs. cooling tons) as an
important element of the Knowledge Based System (KBS). The total
cooling system performance in kW/ton is affected by the kW/ton
for each component. It is convenient to view the entire system
performance as a series of plots for each component, each showing
the kW/ton vs. percent load. The shape of the efficiency versus
percent load curve is dominated by the chiller, so the entire
cooling system kW/ton curve tends to look like the chiller curve.
Examples of cooling plant failure modes are the following:
One common example of chiller diagnostics is the evaluation of
efficiency (kW/ton) versus load (tons). Chillers should ideally
operate near their rated efficiency (purchase point). Various
problems (oversizing, improper scheduling, control problems etc.)
exhibit signatures on this type of plot. The failure modes for
chillers are similar to those listed for cooling systems. Cooling
tower failure modes were also developed in Phase 1.
The chiller monitoring will capture key parameters in the chiller
operation such as water flows and temperatures, pressure drop,
and power. These data will allow determination of chiller efficiency
and loads. We will also measure the pressure drop across the
chiller heat exchangers to determine the extent of fouling. The
cooling tower monitoring will also include water temperatures
and flows, plus local outdoor air data and cooling tower fan power.
The local outdoor air data are an important factor in assessing
the performance of cooling towers. A temperature measurement
station including an aspirated psychrometer will be installed
on the top of the building as far away from the cooling towers
as possible. Data from this psychrometer will be used to evaluate
"nano-climate" effects. Cooling tower intake conditions
will be compared with outdoor air conditions to evaluate recirculation
of cooling tower exhaust.
Database and Visualization
The data base is needed to correctly archive and retrieve desired
information from the large amount of data. The advanced graphics
and powerful computing environment are needed in a prototype system
like the one proposed to implement desirable data computation
and visualization without constraints imposed by the computing
platform. This is both a hardware and a software issue. We intend
to use UNIX graphics workstations4
and a modified form of the Electric
Eye building data visualization software.
The purpose of the Phase 2 demonstration is to build, deploy, and evaluate the IMDS described in the previous sections. Our approach is to build the system for an actual building using a flexible system architecture, and embellishing it as warranted by feedback from building owners and operators. The specific objectives are:
The demonstration consists of the following elements. The first
task is to finalize an agreement with a building engineer and
property manager to allow the system to be installed. The final
custom specification will be developed and the equipment will
be purchased. The equipment will then be installed and commissioned,
plus the operators will be trained to download the data into the
diagnostics system. Once the system is installed we will carefully
document how it is used and evaluate the data collected on site.
Baseline energy performance data will be collected for use in
evaluating energy savings from changes in operations that result
from using the diagnostic system. This evaluation requires recording
changes made in operations and control.
The research team will develop a preliminary functional specification
documenting rules and algorithms to describe the most important
faults detected with the diagnostic system. The specification
will include an electronic document to (a) describe rules and
variables used for performance assessment and diagnosis, (b) identify
degradation and failure modes and conditions associated with each
mode, and (c) identify all ranges of variables used to categorize
performance. As mentioned above, the demonstration effort will
also explore methods to automate the diagnostics. The increased
intelligence will take two forms: (1) more automated diagnoses
and (2) the beginning of a capability of the system to be self
learning (learn from experience).
Another important aspect of the demonstration is the evaluation
of human factors and the ease of use of the diagnostic system.
This evaluation will consider questions such as: how is the system
being used, and are there major issues with the interface that
inhibit the optimal use of the system? These same questions
and findings will be reviewed with the peer operators and owners
to obtain additional feedback on these questions from similar
potential users. A series of recommendations will be implemented
if possible.
Since high quality sensors are a critical element of the diagnostic
system design, the demonstration will include an evaluation of
the costs and benefits of data accuracy and relative value of
each data point. This activity will include categorizing the
data in the test system to determine which data fields were most
useful and important, plus evaluate accuracy requirements for
each diagnostic technique used and performance failure identified.
This task will also include evaluating the life-cycle costs (first
costs and maintenance costs) of high-quality, high-end sensors
versus alternative, more common sensors. The demonstration will
also include comparisons of the EMCS data with the diagnostics
system data.
The primary objective of the diagnostic system is to introduce
state-of-the-art building monitoring and diagnostic information
systems into Class A buildings for use by sophisticated building
operators. This objective is based on our background research,
which suggests that the proposed system meets the needs of operators
and that they support the system we've designed. The concept
is to deploy a permanent system to assist in continuous improvements
in O&M to reduce energy use and operating costs. Our overall
goal is to work with building owners and property managers in
demonstrating the cost effectiveness of the proposed diagnostic
system, thereby creating a market demand for such technology.
We hope to demonstrate that the system could be cost effective
when commercialized by the private sector.
The Phase 2 demonstration is oriented toward deploying the basic
infrastructure for an advanced information system, including field
tests of initial applications. These demonstrations will allow
the controls industry to evaluate the value of such systems that
greatly exceed today's current EMCS technology. Such a system
is the starting point for more advanced, automated diagnostics,
such as those based on fuzzy logic or neural networks. Our approach
is to design the system as a rich, interactive information tool
that will allow the operator to inquire about how well the building,
system, or component is performing.
The diagnostic system will include metering various building systems
and components to provide feedback on building performance. The
users of the system will be building operators and property managers.
The suppliers could be electric utilities, other third-party
experts such as ESCOs, or control companies. The service would
ideally be paid through savings in the operating budget. This
technology gives the owners and managers a quantum leap in improving
management in their buildings. It could reduce operating costs
and make their spaces potentially more comfortable. It also gives
them the choice of local or remote building diagnosis. The system
to be demonstrated is an example of an entire wave of information
based technology. It gives customers a direct entree into this
entire new field.
The participants in the interviews were selected by their peers
as the best in the field. The projects for the demonstrations
will represent prestigious Class A buildings, chosen to attract
the attention of the competitive building management industry.
The managers of the pilot projects are willing to have tours
through their sites for their competitors and industry associations.
The research team for
the CIEE Project on Diagnostics for Building
Commissioning and Operation is grateful to the large number of
contributors who assisted in Phase 1. The Phase 1 team also included
Kristin Heinemeier (formerly with LBNL,
now with Honeywell,
Prof. Dale Seborg (UCSB),
Prof. S. Haddad (Univ. Santa Clara),
and Charlie Huizenga (UC Berkeley).
We are grateful to Mike Brambley
(Pacific Northwest National Laboratory)
and Steve Blanc (Pacific Gas and Electric Company)
who met with the research team several times
over the course of Phase 1. Valuable feedback and review was
also provided by Mark Bailey and Dennis Clough
(U.S. Department of Energy).
We appreciate the continuing support from CIEE Director Jim Cole,
as well as CIEE's Karl Brown and Carl Blumstein. The
project also benefited from early conversations with Jeff Haberl
(Texas A&M University) and
Christie Kjellman (Southern California
Edison Company). This work was supported by the
California Institute for Energy Efficiency
and the Assistant Secretary for Energy Efficiency
and Renewable Energy, Office of Building Technology, State and
Community Programs of the
U.S. Department of Energy
under Contract No. DE-AC03-76SF00098. CIEE is a research unit of the
University of California.
Publication of research results does not imply
CIEE endorsement of or agreement with these findings, nor that
of any CIEE sponsor.
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Nordman, and L.I. Rainer. January 1989. "Integrated Estimation
of Commercial Sector End-Use Load Shapes and Energy Use Intensities."
LBNL Report No. 27512.
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of Commercial Sector End-Use Load Shapes and Energy Use Intensities,
Phase II," Final Report submitted to the California Energy
Commission, Jan. 1991, LBNL Report No. 30401.
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and Rainer, L., "Integrated Estimation of Commercial Sector
End-Use Load Shapes and Energy Use Intensities in the PG&E
Service Area," Final Report submitted to the California Energy
Commission, 1993, LBNL Report No. 34263.
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the ACT2 Project Pilot Demonstration," Proceedings of
the ACEEE 1994 Summer Study on Energy Efficiency in Buildings,
Vol. 5, American Council for an Energy-Efficient Economy, Washington
D.C., August 1992.
Claridge, D.E., Haberl, J., Liu, M., Houcek, J., and Athar, A.,
"Can You Achieve 150% of Predicted Retrofit Savings? Is It
Time for Recommissioning?, " Proceedings of the ACEEE
1994 Summer Study on Energy Efficiency in Buildings, Vol.
5, American Council for an Energy-Efficient Economy, Washington
D.C., August 1994.
Energy Information Administration, U.S. Department of Energy,Commercial
Buildings Energy Consumption and Expenditures 1992 , Commercial
Buildings Energy Consumption Survey, 1995.
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Co., Radnor PA.
Fryer, L., "Electric Chiller Buyer's Guide: Water-Cooled
Centrifugal and Screw Chillers," Esource Technical Update,
Feb. 1995, TU-91-1, Esource, Inc., Boulder, CO.
J.S. Haberl, T.A. Reddy, D.E. Claridge, W.D. Turner, D.L. O'Neal,
and W.M. Huffington, Measuring Energy-Saving Retrofits: Experiences
from the Texas LoanSTAR Program, Texas A&M University,
Oak Ridge National Laboratory Report, ORNL/Sub/93-SP090/1, July
1995.
Herzog, P. and LaVine, L. "Identification and Quantification of the Impact of Improper
Operation of Midsize Minnesota Office Buildings on Energy Use:
A Seven Building Case Study," Proceedings of the ACEEE
1992 Summer Study on Energy Efficiency in Buildings, Vol.
3, American Council for an Energy-Efficient Economy, Washington
D.C., August 1992.
Houghton, D., A Market Survey of Liquid Flow Meters, ESOURCE
Strategic Memo, Boulder Colorado, Feb. 1996.
Hyvarinen, J., and R. Kohonen, Editors, "Building Optimisation
and Fault Diagnosis System Concept", IEA Annex 25: Real
Time Simulation of HVAC systems for Building Optimization, Fault
Detection and Diagnosis, Technical Research Centre of Finland,
Laboratory of Heating and Ventilation, BO Box 206 02150 ESPOO
FINLAND, October 1993.
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and Fault Diagnosis System Source Book", IEA Annex 25:
Real Time Simulation of HVAC systems for Building Optimization,
Fault detection and Diagnosis, Technical Research Centre of
Finland, Laboratory of Heating and Ventilation, BO Box 1804 02044
VTT, ESPOO FINLAND, August, 1996.
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Heinemeier, K., and Janda, K. Final Report on the Energy Edge
Impact Evaluation of 28 New, Low-Energy Commercial Buildings,
Report to the Bonneville Power Administration, LBL-33708, February
1994.
Piette, M.A. and Riley, R., "Energy and Power Performance
of New Commercial Buildings: Results and Data Issues from the
BECA-CN Data Compilation, Proceedings of the 13th
Annual Energy Technology Conference, Washington D.C., March
1986, LBL Report No. 20896.
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Free Press, 1983.
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Commissioning and Operations", prepared for DOE and the California
Institute for Energy Efficiency, LBNL 40512, July 1997.
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Resource Acquisition Programs," Proceedings of the ACEEE
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2
0.15 * 24 BkWh elec. * 10240 source Btu/kWh + 0.15 * 32 BkWh gas =
(36.9 + 4.8) * 1012 Btu (source) = 42 * 1012 Btu (source).
3
The Building Owners and Managers Association classifies office buildings
as follows:
4
Made by Silicon Graphics Inc.
Class A buildings :
The most prestigious buildings in the particular market that have above
average rents, high quality standard finishes, state of the art systems,
exceptional accessibility, and a definite market presence.
Class B buildings :
Buildings that compete for a wide range of tenants with rents in the
average range. Building finishes are fair to good and systems are adequate,
but cannot compete with Class A at the same rent level.
Class C buildings : Buildings seeking tenants requiring functional
spaces at rents below the average for the market area.
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