Diagonstic Tools for Large Commercial HVAC Systems

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DISCLAIMER

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This report was prepared as a result of work sponsored by the California Energy Commission(Commission). It does not necessarily represent the views of the Commission, its employees, or theState of California. The Commission, the State of California, its employees, contractors, andsubcontractors make no warranty, express or implied, and assume no legal liability for the informationin this report; nor does any party represent that the use of this information will not infringe uponprivately owned rights. This report has not been approved or disapproved by the Commission nor hasthe Commission passed upon the accuracy or adequacy of the information in this report.

E RNEST O RLANDO L AWRENCE BERKELEY NATIONAL L ABORATORYIS AN EQUAL OPPORTUNITY EMPLOYER .


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This report describes results from our task with the High Performance Commercial Buildings Systems Program. Forinformation about other reports and other tasks, please visit buildings.lbl.gov/CEC/

This work was supported by the California Energy Commission, Public Interest Energy Research Program, underContract No. 400-99-012 and by the Assistant Secretary for Energy Efficiency and Renewable Energy, Office of Building Technology, State and Community Programs, Office of Building Research and Standards of the U.S.Department of Energy under Contract No. DE-AC03-76SF00098.

Comparative Guide to Emerging Diagnostic Toolsfor Large Commercial HVAC Systems

High Performance Commercial Building Systems ProgramElement 5 Integrated Commissioning and DiagnosticsTask 2.2.1 Fault Detection and Diagnosis Procedures

Hannah Friedman and Mary Ann PietteCommercial Building Systems

Building Technologies DepartmentEnvironmental Energy Technologies Division

Ernest Orlando Lawrence Berkeley National LaboratoryUniversity of California

1 Cyclotron RoadBerkeley, California 94720

MAY 2001


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TABLE OF CONTENTS

Executive Summary v

1.0 Introduction 1

1.1 Purp ose and Benefits of Diagnostic Tools ...............................................................11.2 Tools and Selection Criteria ......................................................................................21.3 Energy Management Control System Data .............................................................41.4 Techniques for Building Performance Assessment ................................................51.5 Steps to Diagnostic Tool App lication .......................................................................71.6 Project Objective an d Scop e .......................................................................................71.7 Report Organ ization ....................................................................................................8

2.0 Literature Review 9

2.1 Diagn ostic Tool Literature ..........................................................................................92.2 California Stud ies Related to Com missioning and Diagnostics .........................102.3 Diagnostic Tool Chara cteriza tion Fram ework ......................................................11

3.0 Study Methods 13

4.0 Tool Overview 15

4.1 Tool Scope ..................................................................................................................154.2 Intended Use ..............................................................................................................174.3 Tool Backgrou nd........................................................................................................194.4 Market Issues..............................................................................................................20

5.0 Comparison of Operational Use 23

5.1 Data Acquisition .......................................................................................................235.2 Data Archiving and Pre-processing........................................................................27

5.3 Diagnostics .................................................................................................................28

5.3.1 Sum mary of Problems Detected .................................................................285.3.2 Raw Data Visu aliza tion ...............................................................................315.3.3 Diagnostics Background ..............................................................................355.3.4 Manual Diagnostic Methods.......................................................................365.3.5 Automated Diagnostic Methods ...............................................................405.3.6 Trea tm ent of Un certain ty ............................................................................43


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5.4 Tool Dev elop ment Plans ...........................................................................................435.5 Sum mary of Au tom ation ..........................................................................................44

6.0 Tool Evaluation 45

6.1 Econom izer Diagnostic .............................................................................................456.2 General Strengths and Limitations .........................................................................506.3 Challenges in Diagnostic Tool Use..........................................................................52

7.0 Conclusions 55

7.1 Curr ent State of Diagnostic Tools ...........................................................................557.2 Tool Research and Developm ent Perspectives......................................................557.3 Future Work to Assess and Improve Tools ...........................................................57

7.4 Future for Diagnostic Tools......................................................................................58

Acknowledgements 59

Sources Cited 61

Appendices

Appendix A Abbrevia tion s .............................................................................................................67

Appendix B Supplementary Figures.............................................................................................69

Figure B1 Sample ASCII input file for the ENFORMA software Retrieve from file utilityFigure B2 Electric Eye time-series data visualizationFigure B3 EEM Suite, Single point time-series trending with historical dataFigure B4 WBD Outdoor Air Economizer Diagnosis screenFigure B5 Universal Translator economizer module data graph

Appendix C Tool Sum mary Sheets................................................................................................73


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List of Tables and Figures

Table 1 Building Performance Assessment Techniques ....................................................... 6Table 2 D iagnos tic Tool Background ...................................................................................... 19Table 3 Tool Marketing Issues ................................................................................................. 20

Table 4 D ata Inputs into Tools ................................................................................................. 24Table 5 Problems Detected through Manual and Automated Diagnostics ..................... 29Table 6 Master List of Problems Detected by the Diagnostic Tools ................................. 30Table 7 Raw Data Visualization Techniques ........................................................................ 31Table 8 Manual and Automated Di agnostic Methods ......................................................... 36Table 9 Tool Automation Summary ........................................................................................ 44

Figure 1 Tool Scope ...................................................................................................................... 15Figure 2 EEM Suite 24-hour Line Plot, average hourly load profile ................................... 32Figure 3 UC Berkeley Fan System Analysis Tools, motor load factor plot ....................... 33Figure 4 PACRAT Tree View .................................................................................................... 34Figure 5 ENFORMA Software Economizer plot with example reference plot ................. 37Figure 6 UT Performance Graph, Economi zer diagnostic module ..................................... 38Figure 7 PACRAT Chil le r Performance p lo t ........................................................................... 39Figure 8 PACRAT Anomaly form ............................................................................................. 40Figure 9 WBDs Outdoor air economizer module problem state notification .................. 41Figure 10 WBDs Whole building energy module energy consumption index ................. 42Figure 11 Ai r-side Economi zer ..................................................................................................... 46Figure 12 UC Berkeley Fan Tool Economizer Diagnostic Plot, MAT vs. OAT ................... 49


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Executive Summary

Major opp ortun ities for d ecreased energy consump tion exist for comm ercial buildings throughimp roving the performan ce of existing systems. Previous case studies have found that tun ingup a n existing buildings heating, ventilating, an d air conditioning (HVAC) systems, referred

to as retro -commissioning , results in an average sav ings of 5%-15% of total energyconsum ption. These energy saving opp ortun ities have not been not realized by a largepercentage of comm ercial buildings.

This guide compar es emerging d iagnostic softwa re tools that aid detection and diagnosis of operational problems for large HVAC systems. We have evaluated six tools for use w ith energyma nagem ent control system (EMCS) or other monitoring data. The diagnostic tools summ arizerelevant performan ce metrics, display plots for m anu al analysis, and p erform au tomateddiagnostic procedu res. There are two main pu rposes in writing this guide: 1) to help potentialtool users gain an und erstanding of key diagnostic capa bilities that could affect toolimp lementation with EMCS data, and 2) to provide tool d evelopers w ith feedback by

identifying imp ortant features and needs for future research.

The six d iagnostic tools includ ed in ou r ana lysis are:

ENFORMA Portable D iagnostic Solutions , Architectural Energy Corp oration Analysis Tools for Built-up Fan Systems (UCB Tools) , Univ. of California, Berkeley Universal Translator (UT), Pacific Gas and Electric Whole Building D iagnostician (WBD) outdoor air economizer modu le (OA/ E) and

wh ole building energy mod ule (WBE), Pacific North west National Laboratory Performance And Continuous Recommissioning Analysi s Tool (PACRAT),

Facility Dynam ics Engineering Enterprise Energy Management Suite (EEM Suite

TM

), Silicon Energy Cor p.

We sum mar ize the existing diagnostic tool literature and present the backgroun d w ork thatguided ou r comp arison framew ork. Interviews and d emonstrations with tool developers andinpu tting data into some of the tools gave us a d etailed u nd erstanding of tool features an dperform ance in the following categories:

Tool intent and scope Developm ent history Market issues: cost, intend ed u sers, nu mber of curr ent users Data need ed for d iagnostics Data acquisition procedu res Archiving an d pre-processing

Problems d etected Raw d ata visualization techniques Methods for manual and automated diagnostics Treatment of un certainty Development plans


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1.0 Introduction

1.1 Purpose and Benefits of Diagnostic Tools

Comm ercial buildings consum e one-third of primary en ergy and two-thirds of electricity in theU.S. each year (EIA, 1995). Major opp ortu nities for decreased energy consu mption exist forcommercial buildings through impr oving the performan ce of existing systems. Case studieshave foun d that tu ning u p an existing bu ildings heating, ventilating, and air cond itioning(HVAC) systems, referred to as retro-commissioning, resu lts in an a verage savings of 5%-15% of the whole building energy use (Gregerson, 1997). At thirty-four bu ildings on the Texas A&Mcamp us, the Energy Systems Laboratory has d emonstrated an average chilled w ater savings of 28%, heating savings of 54%, and electr icity savings of 2%-20% (Clarid ge et al., 2000). Theseenergy saving op portu nities have not been n ot realized by a large percentage of comm ercialbu ildings, as less than 0.03% of existing bu ildings are estima ted to be comm issioned ann ua lly(Dod ds et al., 1998).

If these energy savings are clearly available, wh at p revents building ow ners and man agers fromretro-commissioning or continuously assessing building per forma nce? The d ifficulty indetecting an d diagnosing opera tional p roblems is a main factor. EMCS typically have limitedembed ded diagnostic capabilities. As a result, EMCS data are u nd erutilized for d iagnostics.Given the limited tools available to man age the large am ounts of d ata from an EMCS, buildingoperators rar ely have the time or tr aining required to collect and an alyze trend logs forperforma nce problems. Building operators and energy engineers can u se diagnostic tools tostreamline and guide th e use of EMCS da ta.

To add ress these needs an d issues, new diagnostic softwar e tools are becoming av ailable that

facilitate the detection and diagnosis of energy and other p erformance problems in comm ercialbuildings. Diagnostics has been d efined as the process of detecting incorrect operation an ddiagnosing the cause of the pr oblem (Ha ves, 1999). Diagnostic tools can be used to a ssessexisting building op erations for retro-comm issioning as a single event or continuou sly overtime.

For over twelve years, researchers have developed au tomated diagnostic method s for H VACsystems. Autom ated diagnostics research has prod uced m odel-based m ethods for detectingdeviations from n ormal operation and ru le-based expert systems to detect and diagnosepr oblems (Anderson et al. 1989, Haberl et al. 1989, Culp 1989, Nor ford et al. 1990, Kreider andWan g 1991, Benou arets et al. 1994). With varying d egrees, the emer ging comm ercial tools havedraw n upon these methods.

Detecting and d iagnosing p roblems with building operations can lead to impr oved bu ildingcontrol and occup ant comfort, energy savings, increased equ ipm ent life, and decreasedmaintenance costs. Beyond th eir diagnostic capabilities, these comm ercial tools pr ovide a datama nagem ent framework by organ izing information from volum es of un deru tilized time-seriesda ta. These data are collected th rough energy m anagem ent control systems (EMCS), utilitydem and metering, and d edicated m onitoring systems. Using these data, diagnostic tools cansum mar ize relevant perform ance metrics, display plots for m anu al analysis, and p erform


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autom ated d iagnostic procedu res. A diagnostic tool creates value by summ arizing andorganizing large am oun ts of data an d efficiently extracting useful information. By combiningnew er EMCS logging capabilities with ad vances in information technology, there is greatpotential to assess building performan ce.

Autom ating the process of detection and diagnosis saves time in reviewing raw d ata. The useof automated diagnostics can imp rove d ata analysis, detecting problems that go u nnoticed forlong periods of time. For examp le, these tools help u ncover energy wa ste that does not affectoccup ant comfort. Autom ated tools can be used to continu ously review d ata, imp roving theprobability that performance problems are detected comp ared to man ual techniques. Whileexpert engineers may initially be needed to configure the tool, less experienced users canperform high-level performance assessmen t u sing the tools embed ded expertise.

Although there hav e been research efforts to d evelop diagnostic methodologies for bu ildingHVAC systems for over a d ecade, only recently hav e comm ercial software tools becomeava ilable. Consequ ently, there has been little detailed characterization of the tools and a limitedaw aren ess of their differences and capabilities by poten tial user s. This stud y assessesdiagnostic tools for u se with large commercial building EMCS da ta, comparing the features of the tools for tw o aud iences. First, a side-by-side tool comp arison and evaluation attemp ts togive potential users (building op erators, energy m anagers, engineers, energy service compan ies,and commissioning agen ts) an un derstand ing of each tools capabilities to help a user betterassess options for implem entat ion. Secondly, as feedback for ongoing diagnostic tooldevelopm ent, this work identifies importan t tool features and prop oses direction for futu rewor k to assess and im prove th e tools. Tremendou s potential exists for the u tilization of datavisualization and diagnostics techniques, and each tool offers valuable capabilities fordiagnostic analysis.

1.2 Tools and Selection Criteria

This section introd uces the tools selected in ou r compa rison. Our stud y focuses on theimp lementation of third-party software tools with existing EMCS data, thou gh som e tools areintended mainly for short-term u se with data loggers.

Manual and Automated D iagnostics Defined

The distinction between man ual and autom ated diagnostic tools is not straightforward , sincetools have various levels of automation in their d ata collection p rocedures, man agement an dprocessing of data, and diagnostic use. We d efine manu al diagnostic tools as aids to d iagnostics

that help extract inform ation from r aw d ata. Manu al diagnostic tools require a know ledgeableuser to identify problems using plots and information au tomatically generated by the tool. Incontrast, autom ated d iagnostics redu ce or eliminate the need for hu ma n reasoning in detectionand diagnosis of problems by au tomating the p rocess of analyzing data (Bram bley and Pratt,2000). Au tomated d iagnostic tools use a combina tion of models, statistical method s, andph ysical and expert ru les to detect operational problems. Physical rules are derived from firstprinciples, for examp le, from the laws of thermod ynam ics. Expert ru les are ru les of thum b


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We briefly discuss the Information Monitoring and Diagnostic System (IMDS) developed bySup ersymm etry and d emonstrated by Lawren ce Berkeley National Laboratory ( LBNL) andpar tners throu gh a CIEE project 1 (Piette et al., 2000). The IMDS consists of a ded icated dataacquisition system, separate from the control system, and a robu st platform (high qualitysensors, web-based remote a ccess, high frequ ency archive) to w hich autom ated diagnostics canbe implemen ted in the future. The IMDS does not incorporate pr e-defined d iagnostics, andtherefore is not includ ed a s a diagnostic tool in ou r stud y. Electric Eye, advanced d atavisualization softwar e utilized by the IMDS, is included in our comparison as an example of ahigh quality visualization tool.

The ACRx tool, developed by Rossi and Brau n (1997), is a statistical, rule-based diagn ostic toolfor rooftop air conditioners. This tool is not covered by this assessment since it does notevaluate built-up air hand lers.

These diagnostic tools are relevant for all clima tes and th us, are of nat ional interest. In theircurrent ph ase of developm ent, the tools focus on air hand ler diagnostics, specifically, theoutdoor-air econom izer. Since the mild climate in California is conducive to econom izercooling, the too ls have d irect relevance to California.

1.3 Ene rgy Management Co ntrol System D ata

A goal of this repor t is to describe the curren t ability of software tools to u tilize EMCS data.Each tool selected has th e ability to use EMCS data, but this d oes not need to be their intend edda ta source. For examp le, the tools meant for short term mon itoring are bu ilt for use with d ataloggers, but h ave the capability to inpu t EMCS data. While inp utting EMCS data into theseshort-term tools is a less streamlined p rocess than with th e continu ous m onitoring tools, usingthe short-term tools to continuously d etect perform ance problems can be valuable.

Newer EMCS have ability to extensively monitor a building using digital contro ls. There isgreat p otential to u se EMCS trend logs to assess building performan ce, but th e use of this da tafor diagn ostics mu st be evaluated on a site-by-site basis du e to differences in EMCS trend ingand archiving capabilities (Heinem eier, 1994). Typically, even the new EMCS do not haveembed ded autom ated d iagnostics, and it is often time consum ing to export d ata from the EMCSinto another software progr am (i.e., spread sheets) for an alysis.

This paper focuses on how to use EMCS da ta with d iagnostic tools since these data p oints areoften ava ilable for continu ous trend ing. A d ata collection m ethod like data loggers or a

ded icated d ata acquisition system could be u sed to pr ovide data for the specific pu rpose of diagnostics, although these method s may h ave a high initial cost depend ing on the qua lity of the system.

1 California Institute for Energy Efficiency, Diagn ostics for Building Comm issioning and Op erationProject. Collaborators includ e Lawrence Berkeley National Laboratory (LBNL), Sup ersymm etry, A.Sebald, and C. Shockman. See http:/ / eetd.lbl.gov/ ea/ iit/ diag/ index.html


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While EMCS data ar e wid ely available for d iagnostic use at n o ad ditional hardw are cost, theda ta can be pron e to problems w ith reliability, accuracy, and storage. EMCSs often rep ortmissing data, wh ich can be caused by control system bottlenecks if too man y points are tren dedat a h igh frequency. The EMCS generally provide only limited storage space for d ata archives.

EMCS are d esigned to control the bu ilding, not to p rovide d ata for diagnostics. Therefore, theEMCS may n ot have all the points required by a d iagnostic tool. Add itional points may need tobe add ed, such as chilled w ater flow rate and w hole building pow er. In this report, the adequacyof EMCS data for d iagnostic analysis is not ad dr essed (the PG&E measur ement stud y begins toexamine these issues, see Section 2.2). The data ar e assum ed to be reliable enou gh for use withdiagnostic tools. Instead, we d escribe how the tools convert EMCS data into useful inform ation.

1.4 Techniques for Building Performance Assessment

Before the tool comp arison is presented, it is importan t to show how diagnostic tools fit withinthe spectrum of building p erform ance assessment tools available. In the progression fromma croscopic to m icroscopic assessment, bu ilding a ssessment tools can be orga nized into threecategories: benchm arking, energ y tracking, and diagn ostics. The placement of each tool in thisspectrum can extend into mu ltiple categories. For exam ple, diagnostic tools may p rovidebenchm arking as well as diagn ostics, bu t tools generally focus on one of the three areas. Table 1sum mar izes this performan ce assessment hierarchy of techniqu es.


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Table 1 Building Performance Assessment Techniques

Level Data Result Example Examples ofSoftware

Benchmarking Utility bills andbasic buildin gcharacteristics,location (weath er)

Building metrics forcomp arison overtime or against otherbuildings

kWh/ sq ft-yr,kBtu/ sq ft-yr

EPAs Energ y Star

Portfolio Man ager1

UC Berkeley FanBenchmar king Tool 2

LBNL Arch web-basedbenchmarking:research p rototype 3

Energy Tracking

Utility bills anddemand metering,EMCS end -use data,subm etered circuits

Understand p atterns of energy u se, majorenergy end -uses.Perform u tility rateanalysis

% HVAC,% lighting,% plug loads

Web-based: ABB Energy

Interactive EEM Suite

0 D FU R

0L FU R

Diagnostics

EMCS or short-term

time-series da ta:temps, power,air/ water flow rates,valve positions

Classes of energy waste

EMCS program ming Equipment

performance System interactions

energy waste

fromsimultaneousheating andcooling

In-house spreadsheets,ENFORMA, U T, WBD,PACRAT, EEM Suite,UCB Tools

1 URL: ww w.energystar.gov/ benchmark 2 UC Berkeleys Fan Benchmarking Tool is part of th e set of analysis tools for built-up fan systems.

(Webster et al., 1999a and b)3 http:/ / poet.lbl.gov/ arch/ (Piette et al., 2001b).

Benchmarking is a m acroscopic level of performance assessment, wh ere m etrics are used tomeasu re performa nce relative to others. Buildings are typically benchm arked using coarseda ta, often from u tility bills, and some p rocedure for norm alization for variables such asweath er and floor area. One w idely used tool is the Energy Star Buildings Label PortfolioManager. This tool focuses on whole building energy benchm arking and calculates thebu ildings per centile ran k compa red to a pop ulation of similar buildings. LBNL is alsodeveloping a w eb-based benchmarking tool for California bu ildings called Arch, w hichcomp ares ener gy use intensity to a database of buildings (Piette et al., 2001b). In an effort tomove benchmarking to a comp onent-level, the UCB Fan Tools include fan p erformancebenchmar king using one-time m easurem ents. This fan benchm arking is briefly discussed inSection 4.2 on Tool Intent.

Tracking energy p erformance over time is a logical enha ncement of one-time benchmarking.Energy tracking can result in an overall und erstanding of load shap es. Subm etering to

disaggregate w hole building pow er m easurem ents can help focus d iagnostic efforts on m ajorenergy end-uses.

After these simp le app roaches to performan ce assessment, perform ing diagnostics is the nextstep for a better un derstand ing of HVAC system p erformance. Diagnostics involve theevaluation of specific system and componen t operations. Although the d ata needed fordiagnostics are more extensive than for energy tracking, this jump in complexity is essential toobtain the information needed to aid in correcting p roblems. Benchmarking an d energy


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tracking ar e useful in identifying inefficiency at the whole bu ild ing level and focusing effortstowar d large energy end -uses, wh ile diagnostics allows d etection of specific problems and helpstarget the causes of these problems.

1.5 Steps to D iagnostic Tool Application

This compara tive guide is organized aroun d the p rogression of steps app lied w hen u singdiagnostic tools. These steps are carried ou t in order, although th e app roaches that the toolstake to each step ranges from man ual to automated .

Steps to diagnostics:

1. Data acquisition2. Data m anagement and pre-processing3. Problem detection4. Problem d iagnosis

1.6 Project Objective and Scope

The ma in objective of this report is to provide a clear comp arison amon g curren t diagn ostictools, outlining their key features and assessing their cap abilities. We limit the assessment totools that can use EMCS data to d iagnose HVAC systems typically found in large comm ercialbuildings (built-up air han dlers and central plants). The comp arison is intend ed for twoaud iences. First, potential users can gain an un derstan ding of the key capabilities of each tool.Second , this work gives tool developers feedback by id entifying imp ortant features an d nextsteps for tool research.

It is imp ortant to specify what our stu dy d oes and d oes not assess. This report focuses on thecompar ison of diagnostic tool features, capabilities, and method s for u se in existing buildings.Add itionally, based on our experience operating the tools and observations of detaileddem onstrations, we evalua te the tools and br iefly identify barriers to imp lementation. It is notour intent to assess the market for these tools, nor stud y p otential user acceptance of thefeatures comp ared . A cost-benefit analysis for imp lementing the tools and a compreh ensiveanalysis of case stud ies are also beyond our scope.

To make the information in this rep ort available to a w ide au dience, sections of the report havebeen pu blished in th e proceedings of two conferences. For the 9 th National Conference onBuilding Com missioning in May 2001, we presented a comp arison of diagnostic tool features(Friedm an an d Piette, 2001a). We focused on the economizer d iagnostic evaluation an d ageneral tool evaluation in our pap er for the International Conference for Enhancing BuildingOp erations in July 2001 (Friedm an and Piette, 2001b).


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1.7 Report Organization

This report defines a comp rehensive set of characteristics and comp ares them across a num berof emerging diagn ostic tools. Section 2 review s the existing d iagnostic tool literatu re, as well asliterature th at formed the starting point for our characterization framewor k. Section 3 sets forth

the m ethods u sed to assess the tools, and Section 4 presents an overview and background foreach tool, focusing on the scope, intent, and ma rketing. A d etailed explanation of toold iagnostic featur es is presen ted in Section 5. While Section 4 and 5 char acterize and com parethe tools, Section 6 extends into an eva luation o f each tool, includ ing strengths, limitations, andchallenges to use. A comm on tool diagnostic, the outd oor air econom izer, is discussed in detail.Conclusions about the current state of diagnostic tools and persp ectives on futu re research anddevelopm ent are set forth in Section 7.


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2.0 Literature Review

The literature r eview begins with a backgroun d of the informa tion cur rently available abouteach tool. Next, we review d iagnostic tool inform ation from two recent repor ts oncomm issioning and d iagnostics in California. Last, d iagnostic tool characterization literatu re is

introdu ced, which p rovided guidan ce for the comp arison categories chosen in this report.Through this literature review, we id entify the lack of comp arative assessment of em ergingdiagnostic tools.

2.1 Diagnos tic Tool Literature

While the need for and benefits of comm issioning are beginning to be u nd erstood, less is knownabout tools to assist commissioning and diagnostics. Each of the tools evaluated have beendescribed in pr evious literature, but few formal case stud ies or comp arative analyses have beenperform ed. The tool installations that have been d ocumented do n ot give detailed cost

inform ation, as they focus alm ost exclusively on the benefits of using th e tools. The toolliterature and case stud ies are sum ma rized below.

The WBD has been w ell described, with an exten sive website that contains case stud ies fromfield test sites. The WBD outd oor air econom izer mod ules basic function has been pr esented(Bram bley et al., 1998), with an overview of the logic tree u sed to d eterm ine economizer states(Katipamula et al., 1999). One case stud y at a large hotel in San Fran cisco reported tha t theoutd oor air economizer m odu le identified a total of $6000/ year in energy waste for tw o 10,000cfm air han d lers (Pratt et al., 2000).

Potential interest in im plementing an d challenges in m arketing the WBD w ere assessed th rougha focus grou p in 1998 (Heinemeier, 1999). The marketab ility assessmen t of the WBD wa s aprod uct of a partn ership between researchers at Pacific Northw est National Laboratories andHon eywell. The focus group participan ts had som e reservations about the ability of the tool tosave energy cost and time, and indicated th at dem onstration projects should be d one todecrease un certainty abou t the tools effectiveness.

Descriptions of the ENFORMA software w ere initially pu blished in 1994 (Waterbu ry et al.,Arney an d Frey). Add itional pu blications give furth er tool details and describe the results of acase stud y (Frey 1999, Arn ey and Frey 1999). One office build ing tha t utilized ENFORMAdiagnostic tools identified poten tial savings of over 17% of month ly energy costs. Thedevelopers h ave also developed brief case histories for u se in their m arketing ma terials, whichshow simple payback p eriods of less than one year w ith about 10% energy savings achievedusing the ENFORMA software (ENFORMA, 1999). These case stud ies use the ENFORMAsoftwar e as an aid to detection, but r equire ad ditional HVAC expertise for the identification an ddiagnosis of problems.

The EEM Suite has sales literatu re that includ es little deta il about sp ecific tool features. SiliconEnergy p rovides p otential custom ers one-page Success Stories that includ e qu antitative andqu alitative benefits to imp lementation of EEM Suite mod ules (Silicon Energy, 2001). Noconference or related papers are available for this tool.


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A pap er reporting on PACRAT describes the m ain features an d on e case study in limited detail(Santos and Brightbill, 2000). This case stud y gives an overv iew of the energy w aste of equivalent to $250,000/ year identified throu gh th e imp lementation of PACRAT at a N ationalSecurity Agency building. These field results do not includ e supp orting docum entation of

specific pr oblems detected or diagn osed. Details abou t PACRAT installations exist on apassword protected section of Facility Dynamics Engineerings website.

Pacific North west N ational Laboratory (PNN L) scientists have written tw o p apers th at includ einform ation on diagnostic tools in ad dition to the WBD. They describe the WBDs outd oor aireconom izer mod ule, with brief reference to other commissioning tools, including theENFORMA software, ACRx, the IMDS, and EMCS trend ing (Bram bley et al., 1999). This work presents a pa ragrap h w ith the major features of each tool. In add ition, Bram bley and Pratt(2000) have explained th e use of autom ated d iagnostic tools in the context of energy servicebusiness opp ortun ities. In this paper, they provide definitions for autom ated and m anu aldiagnostics an d list benefits to au tomating diagnostics.

2.2 California Studies Related to Commissioning and Diagnostics

Recently, two commissioning-related stu d ies were completed in Californ ia. Each of theseprojects includ ed elements related to the u se of tools for commissioning and diagnostics incommercial buildings.

In 1999, Pacific Gas and Electric (PG&E) pu blished a r epor t for the California EnergyComm ission that detailed a method for determining the measu remen t accuracy and storagefrequency necessary for da ta acquisition in HVAC systems (Fromberg et al., 1999). A cost-benefit an alysis based on energ y simu lations (spread sheet analysis) of buildings in two climatezones was p erformed to assess potential energy w aste du e to sensor inaccuracies. Add itionally,the stud y determ ined ap prop riate data storage frequencies based on an assessment of exampleplots. As a pa rt of the HVAC system background for the assessment, plots for use in chiller andair hand ler system diagnostics are also presented. This report points to and begins to add ressthe need for a rigorous assessment of data qu ality requiremen ts for diagnostics.

PG&E also fund ed a California comm issioning m arket characterization p roject, prep ared byPortland Energy Conserva tion, Inc. (PECI, 2000). Sur veys of comm issioning p rovid ers andbuilding own ers showed th at tools were desired to aid comm issioning. A literature review forcommissioning p resented a brief background of comm issioning tools, includ ing the WBD, theENFORMA software, the IMDS, and the Virtua l Mechanic (ACRx).


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2.3 Diagnostic Tool Characterization Framework

The fram ework for our d iagnostic tool comp arison has draw n u pon tw o previous efforts tocharacterize diagnostic tools. To iden tify a fram ework for assessing the value and usab ility of adiagnostic tool, mar ketability requirem ents for comm ercial building diagnostic tools were

developed (H einemeier, 1998). Heinemeier evaluated eight categories for tool marketability:

Intent of system: short-term or long-term, level and scope of diagnostics System value: systems diagnosed, pr obability and consequences of problem, detection

alternatives Action to be taken: iden tify action with d iagnostic Required system reliability: pr obability of false alarm and correct failure d etection Notification: means of notifying the u ser of a fault. User: Building operator, on-site expert, remote expert System Cost: Hardware, software, services Market: building size and type

These categories influen ced our meth odology for characterizing and comparing the tools, butwe h ave organized ou r tool assessmen t differently. First, we p rovide an overv iew of keyfeatures and characteristics such as tool intent, users, market, and system cost. Then, wepresent an operational assessment, which is organized arou nd our v iew of the progression of steps to d iagnostics (see Section 1.5 for the list of steps). Each of H einemeiers categories iscovered in this rep ort w ithin th is alternative organization.

The second body of w ork that influenced the comp arative framework for this report is the CIEEProject on Diagnostics for Build ing Com missioning an d O per ation (Piette et al., 2000). TheCIEE project studied the attributes of diagnostic systems an d developed continu ous

performa nce measurem ent techniques for field-testing in a pr ototype system, referred to as theInform ation Monitoring and Diagnostic System (IMDS). The CIEE Phase I work assessed th estate of diagn ostics technology and d efined the focus of the IMDS (Sebald and Piette, 1997). Thediagnostic system architecture (top-down/ bottom-up, autom atic/ manu al,temporary/ continuou s m onitoring) was presented as w ell as important performance m etricsand plots for m anu ally d iagnosing faults in chillers and cooling towers. The architecturerepr esentation and the d iagnostic techniques highlighted (i.e., perform ance m etrics, loadshap es, load d uration cur ves) influenced the d esign of our characterization framework.

The need for a m ore comp rehensive analysis of diagnostic tools is app arent, since no d etailedcomparison of the tools has been performed . The individu al pap ers and su pp orting produ ctma rketing literature d o not p rovide d etail on tool features. This lack of information is du e inpar t to the relatively recent d evelopment of the tools. The recent m arket characterization stud yfor PG&E found that m any p otential user s do not r ealize that the tools exist (PECI, 2000). Anevaluation can pr ovide this informa tion.


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3.0 Study Methods

Our analysis of diagnostic tools utilizes three m ethods to ga in an u nd erstanding of the tools andform a fram ework for characterization and compar ison. First, where p ossible, the tools wereacquired by LBNL for our ow n use. We attempted to inp ut d ata into the tools in order to

evaluate their features. Next, the tool developers were extensively interviewed regard ing toolbackground , implementation, features, un derlying methods, and future developm ent. Last,throu ghou t the information gathering process, a framew ork for characterization w as developedto focus on im portan t tool features. In this report, the comp arative analysis is sum marized in aseries of tables.

Our initial strategy was to enter a dataset w ith known faults into each tool to test its diagnosticaccuracy and ease of use. We were not able to comp lete this task for several reasons. First, onlyfour of the six tools were obtained. PACRAT and the EEM Suite were not pu rchased because of their high price. Second , we were not able to inpu t data into the WBD, which required PNN Lassistance for imp lementation. Therefore, the initial method s were m odified to focus on u sing

the tools where available, and testing was not d one in a formal sense. We were able to enterdata into the UT, the ENFORMA software, and the UCB Tools. A comp arison of the ease of useor formal d iagnostic testing has been left for futu re w ork.

With varying levels of completion an d commercialization, ma ny tools do n ot have d etaileddocum entation or man uals, so the pr ocess of using and un derstan ding the tools was difficult.Interviews w ith the tool developers and dem onstration and tra ining sessions provid ed theneeded assistance in und erstanding the tools. We attended a day of training on PACRAT, andalso received a tw o-hour d emonstra tion of PACRATs featur es. Silicon Energy alsodem onstrated their EEM Suite mod ules. Tool developer interviews and su bsequent follow-upcommu nication w ere p erformed du ring w inter 2000-2001.

Defining th e categories to characterize the tools required a d etailed un derstan ding of both tooloperations and imp ortant features for different users. A compr ehensive set of features has beencompar ed, includ ing tool backgroun d, costs, data acquisition, data p re-processing andma nagem ent, and m anu al and au tomated d iagnostic method s. It has been left up to the readerto recognize what characteristics are imp ortant for their particular app lication. Thecharacterization categories wer e loosely based on H einemeiers work (1998, 1999). The CIEEPhase I w ork w as also draw n u pon for its diagnostic technology assessment criteria (Sebald an dPiette 1997).

A parallel compa rison of these d iagnostic tools is difficult since the d iagnostic tools areintended for different users and systems, with differing levels of autom ation. Even though thetools have man y scenarios for imp lementation, all tools have a comm on focus on impr ovingbuilding operations.

The compar ison fram ework required explicit d efinitions for terms that are often u sed loosely,such as benchm arking, diagnostics, man ual diagnostics, and autom ated d iagnostics. Futurecomp arisons of diagn ostic tools will be facilitated by ou r term inology definitions andcomparison structure.


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4.0 Tool Overview

Section 4 presents an overview of the scope, intent, backgroun d, and current m arket status of each tool. Since these tools hav e different levels of comm ercialization and d ifferent d iagnosticpu rposes, it is imp ortant to app roach the analysis of the diagn ostic tools with an u nd erstanding

of these overview issues. This overv iew sets the context for the comparisons of oper ational usein Chap ter 5.

4.1 Tool Scope

One p urp ose for comp aring d iagnostic tools is to present the spectrum of tool capabilities andplace each tool within that spectru m. The following grap hic describes the differen t pieces of adiagnostic system. The EMCS provides the d ata for the tools stud ied and , therefore, the toolscope begins with the acquisition of this data from EMCS control points. To place the IMDSproject in th is context requires a wid ening of the d iagnostic system scope to include sensors andad ditional control points separate from th e EMCS. Some tools can au tomatically acquire d ata

from the EMCS and use da tabases for archiving this data. Pre-processing is used to p repare th eda ta for analysis throu gh synchronization, averaging, and filtering for erroneou s and missingda ta. Most tools then allow various raw da ta visualization techniques for manu al diagnostics.Add itional manu al and au tomated d iagnostic procedu res help d etect and d iagnose problems.Figur e 1 d epicts how th e tools fit into this representation of system architecture.

Sensors ControlpointsAuto dataacquisition Archive

Pre-process

DataVisualize Detect Diagnose

,0'6

((0 6XLWH

3$&5$7

:%' Limited

87 Limited

(1)250$

8&% 7RROV

Figure 1 Tool Scope

As indicated in Figure 1, the EEM Suite, PACRAT, and the WBD hav e au tomated dataacqu isition and ar chiving for continuous analysis. By contrast, the UT, the ENFORMAsoftwar e, and UCB Tools require manu al data acquisition and do n ot have archivingcapabilities.

The IMDS includes high quality sensors and advanced visualization using the Electric Eyesoftwar e. Since the IMDS does not have p re-defined d iagnostic plots, we d o not includ e it inour analysis except in the comp arison of data visualization techniques.


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The shad ed region for the WBD represents their limited d ata visualization capa bilitiescompar ed to the other tools. To focus on the needs of operators, the WBD has been d esigned forcommu nication of diagnostic results, withou t requiring interpretation of diagnostic plots. TheUTs treatm ent of diagnosis is mu ch less comp lex than P ACRAT or the WBD, and th erefore this

area is also shaded. PACRAT and the WBD focus on p roviding autom ated d etection anddiagnosis. Even though most tools do not diagnose pr oblems, they are still considereddiagnostic tools since they p rovide au tomation to p roblem d etection.

Ov erview of Major sy stems serv ed

Since the focus of this stud y is large comm ercial bu ildings, the comp arison focuses on thediagnostics that ap ply to the typ ical systems found in these buildings: built-up air hand lers,central cooling p lants, and d istribution system s. The following list is an overview of the majorsystems served by the tools. The da ta required, problems detected, and m ethods used to d etectthe pr oblems are detailed in Cha pter 5 on operational use.

The ENFORMA software is used for short-term analysis to aid diagnostics in m any systemtypes, includ ing air h and lers, cooling tow ers, chillers, heating p lants, and zone d istributionsystems. The tool processes da ta and requ ires the user to manu ally comp are data to pre-defined reference p lots.

The UTs prim ary strength is in synchronization of multiple data sou rces such as EMCSdata an d d ata loggers. The tool also has diagnostic modu les for outd oor air economizersand equ ipmen t run-time and cycling.

The UCB Fan Tools have u nique cap abilities to benchmar k fans using on e-timemeasu remen ts, but since we have limited ou r analysis to time-series data, we d o not reviewthis featu re. We focus on UC Berkeleys five spread sheet mod ules for time-series data thatinclud e the analysis of fan power, system temp eratures, zone temp eratures, reheat, andstatic pressure.

The WBD is an au tomated tool for continuous an alysis of economizers (outd oor aireconom izer modu le OA/ E) and w hole building or central plant energy consump tion(whole building energy mod ule - WBE).

PACRAT provid es continuou s analysis and is both broad and in-dep th in its autom atedd iagnostic capab ilities. PACRATs au tom ated d iagnostics ad dress the air hand lers, chillers,hyd ronic system, whole building energy, and zone d istribution.

The EEM Suite uses a w eb-interface for continuou s display an d man ipulation of utility,EMCS, and related time-series data connected through gateways. The tool provid es datavisua lization, bu t there are no p re-defined d iagnostic plots. The EEM Suite consists of tenmod ules. The mod ules of interest to diagnostics are the Energy Analyst, Data Analyst,Enterprise Navigator, Alarm Manager, an d Universal Calculation Engine.


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Top-Dow n or Botto m-Up Diagnostics

The scope of each tool is related to its diagnostic app roach, which can be d escribed as top -dow n or bottom-up . A top-down app roach starts with high-level da ta (i.e., wh ole buildingelectric consump tion) to reason abou t causes of equipm ent-level problems. A bottom-upapp roach uses lower level perform ance data to d etect problems (i.e., temp eratures, pressur es,valve position) and th en ma y app roxima te an aggregated effect for the wh ole building (Hou se,2000). Tools that use both ap proaches pr ovide m ore options for users to investigate problems.

The WBDs w hole building energy (WBE) modu le and PACRAT utilize a top-dow n app roachby creating a baseline for historical energy u se for wh ole building energy an d other subm eteredcomp onen ts. The WBE and PACRAT create baselines for systems and ind ividu al pieces of equipm ent, which can be though t of as intermediate level top-dow n diagnostics.

EEM Suites strength is in top -down analysis as an energ y tracking tool, but the tool has thecapab ility to collect EMCS data for man ua l bottom-up analysis. The WBDs outd oor aireconomizer m odu le and the p rime strength of PACRATs d iagnostic capabilities focus onbottom-up app roaches to pinpoint system p roblems. The ENFORMA software, the UCB Tools,and the UT are bottom-up tools, relying exclusively on componen t-level data from an EMCS ordataloggers.

Softw are Platform

The tools use a variety of softwar e platform s. PACRAT, WBD, and th e UT utilize SQLdataba ses (Microsoft Access) to store da ta. PACRAT also has extensive visualization featuresfrom a third-party grap hing tool. The ENFORMA software stores data in a p roprietary binaryformat. The UCB Tools utilize spread sheets, but p lan to move to a d atabase forma t in thefuture.

The EEM Suites SQL database is located on a r emote server, giving am ple ha rd -disk space forlong-term data collection. The EEM Suite uses an OLAP (on-line analytical pr ocessing)technology that w orks with the d atabase to roll data into hou rly, daily, and m onthly bins toallow for faster calculations. The front end of the EEM Suite u tilizes a grap hing tool as well as aweb-browser.

4.2 Intended Use

The scope of each diagn ostic tool is directly related to its intend ed u se. As described in the

introd uction in Section 1.3, some of the tools were not dev eloped for utilization with EMCSda ta, but they all have the capa bility to analyze such data. The short-term tools (ENFORMA,UT, and the UCB Tools) are intend ed for commissioning or retrofit analysis using d ata loggers,but th ese tools can also imp ort formatted da ta files from any sou rce, including EMCS data. Incontrast, the WBD, PACRAT, and EEM Suite were d esigned to continu ously evalu ate EMCSda ta. EEM Suites web-based p latform allows comp arisons across campu ses of buildingsaccessed remotely. The WBD and PACRAT are able to assess data from mu ltiple buildings


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connected to a netw ork. PACRAT and EEM Suite can d irectly comp are d iagnostic results formu ltiple buildings.

The diagnostic tools have different levels of automation, which are directly related to theirintended function. If the tool is intend ed for short-term u se, autom ated features may n ot be as

crucial, since data are n ot analyzed in d aily operations. If tool users are experienced in energyanalysis or if the tool is used for one-time comm issioning or retrofit projects, there m ay be lessneed for autom ated m ethods of diagnosis. For continuou s data analysis, fully autom atedd iagnostic proced ur es are valuable for both efficient detection an d to enable less technicallyknow ledgeable users to perform d iagnostics. For both short-term and continu ous analysistools, automation can greatly redu ce the time need ed to acquire data an d exam ine the data forproblems.

Addit ional Features

Many of the tools perform more an alyses than a re includ ed in ou r d efinition of diagnostics.Examp les of app lications beyond diagnostics includ e competitive energy pr ocurement,mon itoring and verification for performan ce contracting, and retrofit analysis. This pa perfocuses on the features of the tools as they relate to diagnostics for operations, and we d o notevaluate these ad ditional features. These features are beneficial to certain u sers and give moreversatility to the tools, but ar e outside the scope of this study. For comp leteness, the ad ditionalfeatures are described briefly below.

Monitoring and verification (M&V): Many of the tools have the capability for use inmon itoring energy savings for perform ance contracting p urp oses, limited by the requ iredlevel of soph istication of the M&V. PACRAT and th e WBD are the m ost extensive M&Vtools since their m odels can be u sed to d efine baseline consum ption. The secondENFORMA m odu le, the Lighting Evaluation System, estimates an nu al savings fromlighting retrofits using short-term m onitored or estimated data.

Retrofit analysis: In add ition to the ver ification of retrofit savings, some tools havemod ules that p rovide retrofit screening ana lysis. The UT has m odu les that calculate costsavings from installing occup ancy sensors for efficient control of lighting and plu gload s.The ENFORMA Lighting Evaluation tool outp uts lighting load shapes an d calculates energyconservation potential from schedu ling changes and retrofits, including changes in coolingand heating energy consumption.

Energy procurement: One of EEM Suites strength s lies in its use a s an ener gy accoun tingtool, with cap abilities in ut ility bill ana lysis, rate analysis, load forecasting, and load

curtailment commu nications to aid bu ilding participation in u tility load-shedd ingprograms.

Control capabil ity : Silicon Energys gateways have th e cap ability for two-way control withthe existing EMCS. This allows users to imp lement limited control from a rem ote locationthrough a w eb browser.


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Fan Benchmarking: One of UC Berkeleys Fan Tools assesses fan p erform ance u sing one-time pow er, flow, pressure, and fan speed m easurem ents for benchmarking against fans of similar type. The Fan Perform ance Analysis tool comp ares speed -corrected fan metr ics formeasu red and design d ata to calculate fan efficiency, motor load factor, system opera tingpoint, flow density (cfm/ sq ft), specific fan p ower (watts/ cfm), and fan efficiency ratio

(measu red efficiency/ design efficiency). This application of comp onen t-level benchm ark ingis un ique.

De sign decisions: PACRAT developers suggest that archived m etrics from theperform ance characterization m odu le can be used to inform futu re d esign d ecisions (i.e.,measu ring actual cooling load/ sq ft and d own sizing futur e cooling p lants).

4.3 Tool Background

A sum ma ry of the status of the tools is presen ted in Table 2. The commercialization stage isgiven her e for reference, with d etail discussed in Section 4.4 on m arket issues. All tools arerelatively new and are continu ing to be enhan ced.

Table 2 D iagnos tic Tool Background

Developer Stage Funding

ENFORMA A rch itect ur al En ergy C or p or atio n C om m er cia liz ed in 1996 EPRI* cost-shareand private

UCB ToolsCenter for Environm ental DesignResearch, UC Berkeley

Prototype in 1999 CIEE, CEC**

UTPacific Energy Center , Pacific Gas &Electric Company (PG&E) Beta testing in 2000

California p ublicgoods charges

WBDPacific North west N ational Laboratory(PNNL), Honeyw ell, and Un iversity of Colorado

Prototype with pilotpr ojects in 1998

U.S. Dep t. of Energy

PACRAT Facility Dynam ics Engineering Com mercialized in 1999 Private

EEM Suite Silicon Energy Commercialized in 1998 Private

IMDS Supersymmetry, LBNL, En-Wise,C. Sho ckman , A. SebaldPrototypes in1998 an d 2000

CIEE, U.S. Dept. of Energy, CEC

* Electric Pow er Research In stitute** California Institu te for Energ y Efficiency, California Energ y Com mission

The ENFORMA software was one of the first diagnostic tools commercially available and h asbeen u tilized for commissioning an d r etrofits using the AEC MicroDataLogger produ ct. TheUCB Tools are a part of a research p roject to develop m easurem ent and analysis pr otocols fordetecting p roblems in built-up air hand ling systems. The UT is intended for use with dataloggers, wh ich are available to PG&E custom ers through the Tool Lend ing Librar y at PG&EsPacific Energy Center. The WBD developm ent wa s led by researchers at PNN L who saw theneed for autom ated d iagnostic tools for use with EMCS data. PACRAT was d eveloped byHVAC controls engineers to stream line th e p rocess of pr oviding retro-comm issioning services


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and is now available for use as a stand -alone tool. Silicon Energy has d eveloped the EEM Suitema inly for rem ote web-based energ y tracking and energy rate structure an alysis at largebuildings or grou ps of buildings. The IMDS has been dem onstrated in an office building in SanFrancisco (Piette et al., 2000), and is cu rren tly being imp lemented a t a second building inSacramento.

Four of the six tools (the UCB Tools, the UT, the WBD, and the IMDS) hav e had govern mentfund ing for research and developm ent. The ENFORMA tool, though not pu blicly funded , wasfund ed thr ough EPRI for use by u tilities and u tility comm issioning program s. It is imp ortant tonote that all the tools stud ied are leaders in the em erging field of diagnostic tools.

4.4 Market Issues

Now that the basic scope an d intent of the tools have been d iscussed, it is importan t toun derstan d the current u ser base and the imp lementation cost of the diagnostic tools. Again,the tools offer a wid e range of capabilities beyond diagnostics and have h ad different levels of public funding.

Table 3 Tool Marketing Issues 1

Stage Users Softwarecost

Config. &training

cost

Softwareupgrades &

support

ENFORMA commercialized1996

internal u se: ~300 buildings Over 50 software licenses HVAC service provid ers,

energy managers,engineering consultants

$495 p erlicense (twoinstallations)

$800/ day +travel 3

Phone supportwith software.Consultingservicesavailable for

fee.

UCB Tools prototyp e 1999

Plans to popu late databaseby demonstration projects

Potential: ESCO, energymanagers

free not available not available

UT beta testing 2000 Over 50 users Engineering consultants,

energy managersfree not available not available

WBDprototype withpilot projects1998

~10 sites, 200 building s Opera tors, HVAC service

providersn ot av aila ble n ot av aila ble n ot av aila ble

PACRAT commercialized1999

~10 sites, 50 buildings Operators, energy managers,

facility p lanners an ddesigners.

$10K 30K(includes

training)

$400-800 persystem

not av ailable

EEM Suite commercialized1998

1000 sites and 2500 buildings 25 utilities and energy

service provid ers and 15 endusers

Basic 2:$40-50Kadditionalmodules:$10-25K

App rox. 35%of softwar ecost

18% of software cost

1 All costs and user figures are app roximate.2 Basic includ es one mod ule, server, and gatew ay for 250 points.3 ENFORMA softwar e training is optional.


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Tool Users

Few tools have been imp lemented at a large nu mber of site to date, as the development p rocesscontinu es in custom insta llations. The futu re utilization of these tools may includ e differentversions tailored to m any types of users, includ ing engineers, energy m anagers, operators,commissioning agents, and energy service comp anies.

The intended users for the sh ort-term monitoring focus of the ENFORMA softwa re, the UCBTools, and the UT are consulting engineers and facility man agers that wish to retro-comm issiontheir building. The ENFORMA software h as been used extensively throu gh the d evelopersconsu lting services. Several HVAC techn ical service pr oviders and other engineer ingconsultants have also used the ENFORMA software. The UCB Tools are a research p rototype,currently withou t external users. The UT is furth er along in testing than the p rototype tools,with appr oximately 50 active beta testers. The UT is being licensed to the Pacific Energy Centerand will be mad e available to the pu blic. The tool is targeted to engineers and energy m anagersusing multiple data collection devices (data loggers and EMCS).

The long-term m onitoring tools (WBD, PACRAT, EEM Suite) are currently used by oper atorsand energy m anagers at large buildings or campu ses with central d irect digital control systems.PACRAT developers an ticipate a w ide variety of users, includ ing building op erators, energyma nagers, facility plann ers and designers, and en ergy service compan ies. While PACRAT isthe m ost adva nced in its scope and autom ated diagnostic capabilities, it is also a comp lex toolthat r equires significant involvement from the d evelopers to app ly it. PACRAT has beenimp lemented mainly at u niversities and governm ent facilities.

The WBD w as installed throu gh p ilot pr ojects at PN NLs laboratory camp us, as w ell as at alarge hotel, large office building, and at 90 Department of Energy buildings at the Han ford,Washington site. A m un icipal district heating and cooling plant has also implemented the WBDWBE mod ule, serving aroun d 100 buildings.

The EEM Suite has been implemen ted at large camp uses or comp anies with bu ildings located atmu ltiple sites, referred to as an enterp rise. In add ition, utilities pu rchase EEM Suite modu lesto distribute to their customers, mainly for energy an d load redu ction tracking.

Tool Stage and Cost

All of the tools are un dergoing constant developm ent. Data acquisition algorithms an ddiagnostic mod ules are up da ted and add ed as implementation at the initial sites occur. TheENFORMA software and EEM Suite are curren tly comm ercially available. PACRAT developers

provid e custom installation and progra m ad ditional featur es based on user needs. Theprototyp e WBD is intend ed to be comm ercialized th rough a third-par ty and is currentlyavailable for licensing for comm ercialization. Throu gh anoth er California Energy Comm issionPu blic Interest Energy Research p roject (CEC PIER), the WBD will soon be d emon strated inthree mod es of use: by opera tors of a large office building (Sym ph ony Tower s in San Diego), bythe m anager of m ultiple buildings from a central site (Alamed a Coun ty, California), and by anHVAC service provid er. The UT is pu blic doma in software and will remain free of charge after


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licensing to the Pacific Energ y Center occurs. The UCB Tools are an ea rly pro totyp e, with intentfor an external comm ercialization p ath.

The cost categor ies are broken down in Table 3 by software license cost, configura tion andtraining, and up grad es and sup port. It is difficult to comp are p rices across tools, since each toolhas different capabilities. PACRAT is the most advanced d iagnostic tool comm erciallyavailable and EEM Suite is the m ost extensive data acquisition an d man agement tool, andther efore, these tools cost more. The WBD is not yet comm ercialized , so its cost is marked notava ilable in Table 3.

With the p urchase of some tools, the u ser also receives the HVAC expertise of the d evelopers inthe configura tion process. This service aspect is especially true with PACRAT, as thedevelopers gu ide the configura tion and initial use of the tool. The EEM Suite developersprovid e installation supp ort, but diagnostics supp ort is not includ ed. The ENFORMA softwareprov ides HVAC consulting w ith the initial tool configuration and training. The WBDdevelopers hav e had extensive involvement w ith installation and the interpretation of d iagnostic results at each pilot site.


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Table 4 D ata for Input into D iagnostic Tools

AHU

Economizer OtherCentral plant &

distribution ZonesWhole

buildingDat

frequen

ENFORMA

SAT, OAT, RAT,MAT, RH(ambient andreturn)

SF/ RF amps,

Supp ly air: SP,VP, RH, cfm

Chiller amps, CHWST,CHWRT, CHW pu mpamp s, CT sum p T,CWRT, CT fan amp s,HWST, HWRT, HWpu mp am ps, OAT, RH

ZAT, coilSAT, zone

supplyVP 2-3 min

UCB Tools SAT, OAT, RAT,MATSF kW or am ps,OAT, fan SP

ZAT,HWST,HWRT,OAT

15 min

UT SAT, OAT, RAT,MAT SF kW or amps 5-15 min

WBEWhole building gas,HVAC (other thanchiller) electric, andchiller electric, OAT, RH

Wholebuildingelectric,OAT, RH

At least haverages sum s, 5-1recomme

WBDOA/E

SAT, OAT, RAT,MAT, RH, SFon/ off, HW valvestatus, CHWvalve status

At least haverages1-5 minrecomme

PACRAT

SAT, OAT, RAT,MAT, SF status,SF cfm or % VSD,HW valve, CHWvalve, OA damperOp tional: RH

SF CFM or %VSD, SF on/ off,OA damper, HWvalve, CHWvalveOptional: OAcfm, OA CO2,RA CO2, RA RH

Primary and secondaryloops: CH WST, CH WRT,CHW flow. Chiller am psor kW, CWST, CWRT,chiller status, pum pstatus, chiller comman d,valve outpu ts, loop DP

ZAT,Optional:ZRH,setpoints,ZCO2

Wholebuildingelectric + upto 3additionalnormalizingvariables

user defindeveloperecommeminu te tohour

EEM Suite Benchmark / trend any points.Whole

buildingelectric,OAT

User d efi

SAT Supply air tempOAT Outd oor air tempMAT Mixed air tem pRAT Return air tempZAT Zone air temp

RH Relative humid itySF Supply FanRF Return FanSP Static pressureVP Velocity pressure

CHW Chilled waterCW Condenser waterST Supply tempRT Return tempHW Hot water


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How is t he tool configured?

Moving time-series data into a d iagnostic tool is a critical step tha t may r equ ire techn icalassistance for new u sers. The shor t-term tools impor t text files, while the continu ous ana lysistools rely on au tomated data acquisition thr ough links to the EMCS da ta.

The ENFORMA softwar e requ ires specifically formatted h ead ers in ASCII text files that sp ecifythe collection frequency and time range. The ENFORMA softwar e assumes that the d ata aresequential (see samp le input file in App end ix B1). The UT has an interface that helps imp ortda ta from text files so the d ate and time are not required in a pred efined configuration. Unlikethe ENFORMA software, the UT retains the timestam p w ith each p oint. To inp ut d ata into theUCB Tools, the user simply p astes data into the inp ut screen in the spr eadsheet.

EEM Suite, PACRAT, and the WBD can acquire d ata by d irectly polling th e EMCS for each datapoint, or by u sing th e EMCS to create trend logs of selected d ata points, and grab this d atafrom the tren d files and into the tool. These tools have routines to capture EMCS data,progr amm ed separ ately for each controls manu facturer. The tools add to the list of controlsystems to w hich they are able to link as the software gateways an d interfaces to help access theEMCS da ta are developed.

Although the WBD can access trended data from EMCS trend logs, the tool typically makesdirect connections to bring the d ata into the WBD database as it becomes available rather thanusing the EMCS trend ing capability. To use a direct conn ection to the data , the EMCS datapolling netw ork mu st be robust enough to withstand networ k traffic from the p olling, wh ichcan potentially disrup t the core control fun ctions of the EMCS. EEM Suite also polls the d atafrom the EMCS directly throu gh the gatew ays. In contrast, PACRAT uses only data that hasbeen trend ed by th e EMCS.

PACRAT has extensive help files tha t describe configurat ion, but since the p rocess is qu iteinvolved, a Facility Dynamics engineer typically perform s the configuration. Trends m ust becreated w ithin the EMCS and then the engineer assigns each p oint to the appr opriate d atabasetables and inp uts operational thresholds for problem reporting. The user can run the PACRATanalysis on an comp uter on-site, or the tool developers can d own load the d ata from an filetransfer protocol (ftp) site, process it, and send the results back to the user. For this method, theuser is equipp ed w ith a viewer version of PACRAT, which perm its diagnostic outpu t viewingand visualization, but excludes th e pr ocessing step.

PN NL scientists configure the WBD at each pilot facility, linking th e control system d ata to thetool. For the p ilot facilities, the WBD is run off-line at PN NL for the initial analysis, after which

the tool is installed on site for the oper ators to use in real-time. The Architectural EnergyCorpora tion typically trains u sers and configures the ENFORMA software, includ ing assistancewith initial analysis. The Pacific Energy Cen ter sup ports beta testing qu estions abou t the UTand occasionally has free training seminars. The EEM Suite software includ es gatew ayinstallation, consulting on wh ich p oints to include, an d training on a pp lications, with ad ditionalconsulting available.


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The tools vary in the time an d effort required for configuration. Since curr ent tool users werenot surv eyed, this question was not stud ied in detail.

5.2 Data Archiving and Pre-Processing

Once the tools access the data, the data are archived, and pre-processed for use in d iagnostics.Since EMCS data ha ve the p otential for m issing and erroneous values, some tools include timestamp synchronization and data validation procedures.

Archiving

To streamline the an alysis of historical data and create a data m anagem ent structure, some toolsarchive data. In a database, PACRAT archives all raw d ata, as well as sum mar y data su ch asperforma nce metrics and diagnostic results. On a r emote server, the EEM Suite archives rawda ta and the alarm states in a da tabase. The WBD archives hourly average data and en ergyconsum ption in its database.

The archiving capa bilities of the short-term tools are limited to the am oun t of data run in eachproject and the mem ory limitations of the softwa re platform. The ENFORMA software an d th eUCB Tools save per iods of data as separate pr oject files. The data are not stored in a singlelocation within these tools. Similarly, the UT does not store data w ithin th e pr ogram , but storesthe path to the d ata file. Each time a point is selected for diagn ostic ana lysis, the data areaccessed from a file in an external location to the UT and inserted into a tem porar y d atabasewithin the softwa re.

Pre-Processing

Some EMCS ma y not provide comm on timestamp s for mu ltiple trended da tapoints, wh ich canbe problematic for X-Y scatter and 3-D plots or performing diagn ostic analyses. The UT is theonly tool that synchronizes data throu gh interpolation. Timestam ps are synchronized across allda ta streams through an algorithm that examines data every second. A time-weighted averageis used to interpolate the value of each data p oint for the user-specified timestam p. Thiscapability enables the u se of mu ltiple d ata sources, such as EMCS and dataloggers.Synchronization is also useful for processing EMCS data p oints that d o not h ave the identicalstarting times or da ta collection frequency.

PACRAT and the EEM Suite preserve the real timestam p w ith each d ata point or can u sealgorithms that converge the data onto a single timestamp interval. PACRAT joins data by

converging onto one interval or changing the timestamp to the beginning of the interval. These joining methods d o not keep the exact timestamp w ith each d ata point, but do p ermit da tasynchronization. For examp le, data from tw o different control manu facturers m ay report timedifferently, one including seconds and one withou t second s. The data wou ld not be consideredcoinciden t un til the joining p rocess discards the seconds read ing from the first data stream . TheEEM Suite can synchronize da ta by changing the timestam p to the nearest synchronou s value.This method is typically not employed an d th e data are kep t with the tru e timestamp, except forplots that need coinciden t timestam ps for mu ltiple data stream s (X-Y scatter, 3-D). The


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ENFORMA software assum es sequential data and d oes not imp ort timestam ps. The WBDtypically polls data at a 5-15 minu te polling frequen cy and uses hou rly averages of this data indiagnostics. Therefore, timestam ps d o not need to be synchronized in its pre-processingroutine.

Data va lida tion is essentially pre-diagnostics filtering to ad dr ess the p roblem of erroneou s d ata.Since EMCS data m ay rep ort u nreasonable values, it can be useful to pr e-filter d ata outside aspecified range for use in an alysis. PACRAT, WBD, the ENFORMA software, and EEM Suitecan autom atically pre-filter data. In add ition, PACRAT has algorithm s that fill and recreateda ta throu gh interpolation, distribution, or based on the difference between valu es. A UT usercan build m issing sections of a da taset using a rou tine that p erforms regression with theincomplete dataset and a full dataset. The UT can also identify repeated timestam ps, applytime shifts to data, and ma ke slope and offset adjustm ents.

5.3 Diagnostics

The last two steps for diagnostic tool use are problem d etection an d d iagnosis. As previouslydiscussed, detection requ ires a comparison to th e correct opera tion of the system using eitherqu antitative (mod eling) or qualitative (expert know ledge) baselines. Diagnosis specifiespossible causes and remed ies of the problem d etected.

5.3.1 Summary of Problems Detected

The pr oblems that diagnostic tools detect focus on energy w aste, comfort control, andequipm ent operation. Table 5 sum ma rizes the pr oblems that can be detected by each tool,organized by system categories (wh ole building, central plant, air hand ler, and z ones). Thecolum n for General a cross systems lists p roblems d etected by m ultiple system categories andreferences them by column h eading. This table lists problems detected throug h both man ualand autom ated d iagnostics. To reiterate, man ual diagnostics refers to the m anu al interp retationof results from plots that are au tomatically created by the tool. Autom ated d iagnostics detect(and often diagnose) problems w ithout user know ledge or intervention.

Table 6 is a master list of the p roblem s detected by the tools with brief descriptions.

Most tools focus p roblem d etection on air hand lers and central cooling and heating plants.These systems are typically the most widely instrum ented an d are large energy consumers. Theonly common d iagnostic area across the tools (except EEM Suite) is the airside econom izeroperation. Each tool per forms this analysis differently as described in Section 6. PACRAT has

autom ated detection of the largest nu mber of problems, while the WBD focuses on a fewspecific system d iagnostics.

The ENFORMA software h as ad ditional diagnostic reference plots for many types of stand-alone systems not w ithin the scope of this report such as fan coils, hyd ronic baseboard , DXcooling, thermal energy storage, and heat p um ps. UC Berkeleys Fan Power An alysis tool hasthe un ique ability to compar e namep late data to m easured v alues and calculate motor loadfactor.


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Table 5 Problems Detected through Manual and Automated D iagnostics

I. Wholebuilding

II. Central Plant III. Air Handler (AHU) IV. Zones

PACRATUtility deviationfrom baselinemodel(kWh, therms,ton-hours)

Chiller diagn ostics Chilled water temp control Poor load factor Poor condenser, evaporator

performance Chiller efficiency degradation Excess cycling Chiller electric deviation from

baseline modelDistribution (hydronic) Failed valve Struggling pumps, valves Primary/ secondary system

problems

Lack of economizer cooling Outdoor air excess or inadequate Simultaneous heating/ cooling Unn ecessary mechanical cooling Leaking/ struggling coils Con trol insta bility, excess cycling

of all controlled comp onents

Temp,humidity,and CO2control

WBD,WBE

Electric and gasdeviation frombaseline model

Chiller electric deviation frombaseline model

A U T O M A T E D D I A G N O S T I C S

WBD,OA/E

Lack of economizer cooling Ou tdoor air excess, inadequ ate Simultaneous heating/ cooling Unn ecessary mechanical cooling Failed/ miscalibrated sensors

EEM Suite High energy Unoccupied

operation

ENFORMA

Boiler: see gener alCooling tower (CT) capacityChiller Condenser p erformance Interlock w ith AHU and CT Chiller kW temp d ependence

Lack of economizer cooling Simultaneous heating/ cooling Humidifier operation Evap cooler performance Static pressure & fan control Duct heat gain/ loss

Terminal unoperation

UCBTools

Lack of economizer cooling Motor load factor problems Static pressur e Run-time

Reheatoperations

M A N U A L D I A G N O S T I C S

UT Lack of economizer cooling

* I, II, III, IV in parenthesis denote column headings that include these diagnostics.


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Table 6 Master List of Problems Detected by the Diagnostic Tools

Gene ral Across SystemsUn occu pied op er ation Use of load profiles, tim e-series d ata, or au tom ated ru les to d eter min e system

operation d uring unoccup ied h ours.Failed / m iscalibrated sensors Sensors have erroneou s read ings.

Ru n-tim e th reshold m et A piece of equ ip men t has reach ed a user-d efin ed ru n-tim e thresh old .Alarm occurrence Frequency of alarm occurrence is reported .Override occurrence Frequency of operator overrid e of controls is reported .Per fo rm an ce ch ar acter iza tio n Ar ch iv e of p er for ma nce m easu res ov er tim e.Pr og ram con d ition al alar ms U ser -d efin ed r u les ca n be in pu t for au tom ated d etection u sin g alar ms.Temperature control The ability to meet setpoint and / or control stability.Run-time Calculation of total and percent run-time for an analog point above a

threshold or for a status point (on/ off).Cycling analysis Counts number of times a point crosses threshold and calcu lates cycling rate.Excess cycling Automated detection of equipment that cycles on and off.

Whole BuildingHigh energy Whole bu ild ing energy is indexed and compared across bu ild ings.Deviation from baselin e H istorical d ata used to cr eated a baselin e u sin g a mu lti-v ariable bin m od el.

Measured d ata are compared to baseline.Cooling Plant

Ch illed wa ter tem p co ntr ol Deter min e if ch illed water su p ply tem p er atu re m eets setp oin t.Poor load factor Chiller is ru nning at low load .Poor condenser, evaporatorperformance

Poor heat exchange on the condenser or evaporator.

Chille r e fficiency degrada tion Track ch ille r efficiency over t ime . May use mult i-var iable model.In ter lock w ith AH U an d CT Assess in ter lock s th at pr ev en t ch iller op er ation w ith ou t a loa d or hea t

rejection m ethod .C hiller kW tem p . d ep en d en ce D eter m in e if ch iller m od u la tes w ith ou td o or tem p a nd m eets co olin g lo ad s.Coolin g tow er cap acity Th e ability of th e cooling tow er to meet th e heat rejection load .

Hydronic D istributionFailed valve Detect failed or poorly performing valves.Stru ggling p um ps, valves Assess w hen valves or p um ps are not able to m eet load .Pr im ar y/ secon da ry sy stem Co mp ar ison of p rim ar y an d secon dar y lo ad . Rev er se flo w in br id ge.

Air Handle rsLack o f econ om iz er co olin g N ot fu lly u tiliz in g ou tsid e air to p ro vid e fr ee coo lin g.Ou td oor air excess Excess ou tsid e air that creates need for m echanical cooling or heating.Ou tsid e air inad equ ate Inad equ ate ou tsid e air for ventilation and air qu ality.Simultaneous hea ting and cooling Detect when s ing le duct systems hea t and cool the supply a ir.Leaking coils Coil valve is commanded closed , bu t cooling or heating occurs.Struggling coils Coil not able to meet load .Control instability Unstable or oscillating control of valves.Humid ifier operation Verify correct hu midification.Evap cooler perform ance Verify evap orative cooler effectiveness.Static p ressu re & fa n con tr ol Assess du ct static p ressu re an d su p ply an d retu rn fan con tr ol.

Duct heat gain/ loss Excessive heat gain or loss between the air hand ler and the zones.M oto r loa d factor p roblem s D eter min e h ou rs a t load fa cto r bin s fo r occu p ied / u no ccu p ied hou rs.Static p ressure setpoint Verify static p ressure meets setpoint.

ZonesTemp, humidity, and CO2 control Track stat is tics on zone thermal control and venti lation.Term inal u nit op eration Zon e an d su pp ly air velocity m od ulation , reheat m od ulation.Reheat operations Analyze hot water reheat w ith respect to OAT and ZAT.


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5.3.2 Raw Data Visualization

The most basic method of d etecting problems is through raw data visualization techniques.Tool users can d etect pr oblems man ually by viewing time-series data in d ifferent w ays. Table 7sum mar izes techniques in wh ich the tools help users view da ta. This table does not show th etool interface for visualizing the resu lts of diagn ostic pr ocessing, bu t only the techniqu es forraw d ata visualization. These data visualization method s and features are presented here forinformational purp oses only, as some u sers may not h ave the man ual d ata analysis expertise touse these features or the time to spend reviewing plots. While da ta visualization is notapp ropriate for all users, und erstanding historical data using techniques that help sum ma rizethe d ata are u seful for increasing the u nd erstanding of system operation.

Table 7 Raw data visualization methods and features

Timeseries

Advancetime

seriesX-Y 3D Dailyprofile

Loadduration Filter

Real-time Aggregate

ENFORMA -UCB ToolsUT (PG&E) - +

WBE hourlyupdateWBDOA/E hourlyupdate

PACRAT +EEM Suite +IMDS/Electric Eye

Definitions: Advanced time series : Scroll and zoom capabilities. ( -) indicates zoom only. X-Y scatter : Plot of two poin ts against each other to visualize their relationship. Daily profile : Data point plotted against hours of the day, overlaying mu ltiple days( ) or

calculating average and peak profiles. ( +) Load duration: Sort and bin data for plotting load (or load factor) versu s hour s at each load.Filters: User-defined filtering by day, time, versus another p oint. ( +) ind icates

advanced capabilities. Real-time : Monitoring with continuou s up date of data. Aggregate: Sum d ata across system levels or time.

Raw time-series visualization allows m anu al identification of sched uling problems. Time-series data can be viewed using all tools except the W BD, wh ich focuses on gr aph ical displaysof the results of the au tomated diagnostics, rather than providing data visualization features.The tools with visualization of time-series (trended) data vary in their ability to scroll throughthe d ata or zoom for detail. The IMDS uses Electric Eye visualization software, wh ich isinclud ed h ere as an examp le of an ad vanced d ata visualization tool. For examp le, Electric Eyecan display large d atasets, up to 8 points of 1-minute frequency d ata for over year on a singleplot. An exam ple screen for Electric Eye time-series data visua lization is show n in Figure B2 in


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Ap pen dix B. Figure B3 in the App end ix show s EEM Suites time-series plot, wh ich includ escurrent d ata as well as the previous da y and th e averages in the p revious week.

X-Y scatter plots are u seful to determ ine the effect of one var iable on another , such as chillerefficiency (kW/ ton) versus load (tons

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Diagonstic Tools for Large Commercial HVAC Systems