Fault Detection and Diagnostics for Rooftop Air Conditioners

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9 NOMENCLATURE AHU α α β c = Air handling unit = Thermal resistance portion = False alarm threshold = Fault diagnosis threshold = Distance from fault point to axes χ 2 ( n) = Chi-square distribution 2 d i = Normalized distance square d max = Maximum normalized distance ∆ T ca = Condenser air temperature difference ∆ T ea = Evaporator air temperature difference EER = Equipment efficiency ratio η v = Compressor volumetric efficiency FDD = Fault detection and diagnosis F i = Fault points f i = Frequency GRNN = General regression neural network h o = Heat transfer coefficient between sensor and ambient air HVAC = Heating, Ventilating, and Air-Conditioning IA = Independence Assumption λ i = Eigenvalue of Σ Λ = Eigenvalue matrix M = Mean vector of residuals M = Mean vector for normal operation normal M current = Mean vector for current operation MCS = Monte-Carlo Simulation m& r = Refrigerant mass flow rate µ = Mean µ normal = Mean value for normal operation M X = Mean vector for sample X M = Mean vector for sample Y Y 9
10 N = Number of generated sample points N X = Normal distribution for sample X Ω X = Area for probability evaluation P dis = Discharge pressure Φ = Eigenvector matrix P i = Data points P Ω = Probability within area Ω X φ ra = Relative humidity of return air P = Discharge pressure dis P suc = Suction pressure Q & cap = Cooling capacity ratio dist = Distance ratio SRB = Statistical rule-based RTU = Rooftop unit σ = Standard deviation σ normal = σ for normal operation Σ = Covariance matrix Σ = Σ with independence assumption assumption Σ current = Σ for current operation Σ normal = Σ for normal operation Σ X = Covariance matrix for X T amb = Ambient temperature T cond = Condensing temperature T = Discharge line temperature dis T = Evaporating temperature evap T ll = Liquid line temperature T ma = Mixed air temperature T oa = Outdoor air temperature T ra = Return air temperature T sc = Sub-cooling T sh = Superheat T wb = Wet bulb temperature VAV = Variable air volume v 1 = Specific volume of compressor inlet refrigerant X = Sample point in original space Y i i = Sample point in transformed space 10
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CALIFORNIA ENERGY COMMISSION Final
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Acknowledgements Jim Braun and Haor
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Abstract Project 2.1, Fault Detecti
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TABLE OF CONTENTS LIST OF TABLES LI
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LIST OF FIGURES Page 1 - Field Test
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The report “Description of Labora
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mounted heat pumps for heating and
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The retail stores are in Southern C
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Figure 1 - Field Test Sites Data Co
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Gibson School (Cont’d) Woodland S
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Table 1 - Data List for Modular Sch
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BUILDING TYPE: Modular School Rooms
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HEATING / AIR CONDITIONING EQUIPMEN
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Sacramento Area McDonalds PlayPlace
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Bradshaw Road (Sacramento Area) McD
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TEST INSTRUMENTATION: Tables 2 and
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Table 2 - Data List for Inland Rest
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Castro Valley (San Francisco Bay Ar
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Castro Valley McDonalds PlayPlace P
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more or less custom design, publish
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BUILDING TYPE: Retail Store ADDRESS
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TEST INSTRUMENTATION: Similar test
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• Temperature and humidity levels
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November 2001 - December/January 20
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Table of Contents 1. Introduction..
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1. Introduction All the thermodynam
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Table 1.1 Comparisons of Three Mode
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solution procedure involves the non
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independent of the moisture content
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function, f ( X , y) , if it can be
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Figure 2.1 Neural-Network Implement
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prototype was developed by using th
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3. Comparison of black-box modeling
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which is more accurate than the exp
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Similar to GRNN, RBF has very good
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Table 3.2 RMS error (Polynomial,GRN
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Table 3.4 Contrast of Black-box mod
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Polynomial plus GRNN Training Desir
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interpolation(poly+GRNN) extrapolat
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used to build the steady-state mode
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α , ρ, τ I t t s h o A t a Figur
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Temperature (F) 82 79 76 73 Condens
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0.050 0.045 Pressure = 101.3 [kPa]
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T T − T − T W = W −W −W m =
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Figure 4.8 Output of three steady-s
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will be calculated to represent the
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180 175 RMS error = 0.3984 (F) Pred
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180 175 RMS error =1.1472 (F) Predi
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51 50 RMS error=0.3860 (F) Predicte
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4.4 California field site results S
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105 100 RMS error =0.5982 (F) Predi
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Since air conditioners always cycle
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6. Conclusions and future work So f
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Lee, W., House, J.M. and Shin, D.R.
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Table of Contents 1 Introduction...
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AHU α β 2 d i EER ∆ ∆ η v T
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$ !! !! )
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Paper statistics in HVAC FDD Number
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011>" - , ?" 8?"
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+ : 6336" ,
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+ :: !!- :: !!
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6 24 122 7) 6 3++, ) . !! /011
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)
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336 F / s $ S
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N $ V v1
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Figure 3-4 2-dimensional residual d
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10 5 Normal and current operation p
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7 N Ω f N N Ω = Ω X ,
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0 + α 6 d χ ( )
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Normal operation region Residual-2
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Table 3-2 Refrigerant Leak at 20% l
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ratio dist = P F 1 P F 1 2 1 9.0
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Page 153: Table 4-2 Polynomial plus GRNN mode
Page 157 and 158: 4.2 m r,predict (kg/min) 4 3.8 3.6
Page 159 and 160: 6 1)( ( ( 6 1)( ) , ( )
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Page 163 and 164: ? "J> *"J060 #"J083 *$"J670 #"J08<
Page 165 and 166: Table 4-17 Detected (normal, fault)
Page 167 and 168: c=1 c=10 c=20 1 0.8 Distance ratio
Page 169 and 170: $ /) $ 0111" .. !!
Page 171 and 172: Chen, Bin and Braun, J.E, 2000. Sim
Page 173 and 174: Ventilating, Air-Conditioning and R
Page 175 and 176: 1.T amb 2.T ra 3.RH ra Plant Prepro
Page 177 and 178: F) $
Page 179 and 180: +: + $ 5
Page 181 and 182: .44(' * 5 09( ( 0( F N ( M , Σ)
Page 183 and 184: Load 20% 40% 60% 80% 100% 3 4 Fault
Page 185 and 186: Load 20% 40% 60% 80% 100% 3 4 Fault
Page 187 and 188: 3 4 0 0 0 0 0.4173 0 0.0010 0 0.000
Page 189 and 190: .44(' $ () ) ) - $
Page 191 and 192: ∆P Restrictio n Level=100%* ∆P
Page 193 and 194: 2 TABLE OF CONTENTS TABLE OF CONTEN
Page 195 and 196: 4 A1.2.3 Valve Position Expression
Page 197 and 198: 6 Figure 2-10 Decoupling refrigeran
Page 199: 8 LIST OF TABLES Table 1-1 Fault di
Page 203 and 204: 12 with a fixed-orifice as the expa
Page 205 and 206: 14 (residuals) should have a zero m
Page 207 and 208: 16 expected distribution of the res
Page 209 and 210: 18 operation. Another advantage is
Page 211 and 212: 20 1.1.2.1 Original SRB Fault Diagn
Page 213 and 214: 22 Corresponding to the SRB fault d
Page 215 and 216: 24 integration of the probability d
Page 217 and 218: 26 1. Simplifies fault detection fr
Page 219 and 220: 28 Step i Do FDD on fault i . After
Page 221 and 222: 30 ROOFTOP UNIT FAULTS COMPONENT-LE
Page 223 and 224: 32 has two possible causes: refrige
Page 225 and 226: 34 1.2.3 Decoupling of Component Fa
Page 227 and 228: 36 1.2.3.2 Condenser-Related Faults
Page 229 and 230: 38 mixture, χ ref is known as the
Page 231 and 232: 40 Table 1-3 also lists the refrige
Page 233 and 234: 42 Refrigerant Property T cond, pre
Page 235 and 236: 44 as an independent feature only f
Page 237 and 238: 46 evaporator air flow rate reducti
Page 239 and 240: 48 5. 2 Pll ∆ deviates drasticall
Page 241 and 242: 50 System-Level Faults RefUnder Ref
Page 243 and 244: 52 1.2.5 Summary of Decoupling Sche
Page 245 and 246: 54 noise, system disturbances and m
Page 247 and 248: 56 compressor data. When there is a
Page 249 and 250: 58 Figure 2-5 Decoupling evaporator
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60 Total Pressure Drop of Liquid- L
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62 Figure 2-11 Illustration of Demo
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64 bypass the compressor. At this t
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66 for T dis because there is no ov
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68 Figure 2-14 Outputs of the FDD d
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70 the system requires more refrige
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72 recommended that: the system req
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74 Figure 2-20 Outputs of the FDD d
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76 Figure 2-21 Histogram bar plot o
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82 2.3.2 Summarized Results for Oth
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84 3 CONCLUSIONS AND RECOMMENDATION
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86 Breuker, M.S. and Braun, J. E.,
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88 Lee, W., House, J.M. and Shin, D
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90 APPENDIX 1 PHYSICAL MODELS OF EX
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92 A1.1.2 Short-Tube Models Many re
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94 m& = CA ρ P up − P ) (A1-5) (
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96 The abrupt change of CA for the
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98 These equations can be combined
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100 H h T sh , max opening T sh , s
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102 A1.2.5 Parameter Estimation Met
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104 T (2 T sh, ratingopening , sh,m
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106 Harms’Result Harms plotted al
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108 Mass flow rate Local linearizat
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110 temperature on the RTD is unifo
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112 in cold water application, it i
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1 ACKNOWLEDGEMENTS The research tha
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3 2.2.3 Todd Harms’ Data ........
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5 LIST OF FIGURES Figure E-1. FDD d
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7 LIST OF TABLES Table E-1 FDD resu
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9 IA = Independence Assumption λ i
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11 EXECUTIVE SUMMARY Packaged air c
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13 current values of the fault indi
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15 Figure E-4 Histogram of the EER
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17 2. Operational cost savings, whi
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19 Table E-5 Conservative Lifetime
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21 constraints. Economic constraint
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23 Paper statistics in HVAC FDD 35
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25 on directional changes to identi
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27 fault levels at different operat
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29 was concluded that the method wa
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31 However, several improvements ar
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33 point sensor placement is genera
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35 2 DATA SOURCES USED FOR EVALUATI
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37 The fives types of faults are re
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39 Occupation Type Climate Location
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41 The SRB FDD method determines wh
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43 The following sections describe
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45 points rather than on a distribu
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47 current operation point P 1, P F
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49 4 A DECOUPLING-BASED FDD TECHNIQ
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51 This approach overcomes the draw
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53 ⎡ ∆T ⎢ ⎢ ∆T 2 ⎢ ∆
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55 improving the compressor model p
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57 Condenser Air Mass Flow Rate (lb
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59 Evaporator Air Mass Flow Rate (l
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61 Liquid-Line Pressure Drop (PSI)
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63 4.2.2 Purdue Field Emulation Sit
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65 1. Evacuated the system, and the
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67 normal _ value − current _ val
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69 Figure 4-16 shows one frame afte
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71 Figure 4-18 shows one frame afte
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73 valve position can be seen from
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Figure 4-22 Outputs of the FDD demo
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83 4.2.3.2 Summarized Results for O
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85 5 ECONOMIC ASSESSMENTS Since the
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87 inspection savings would be $2,0
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89 Q & Cap = W & × EER (5-2) The e
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91 4. electricity costs ( C e ). Ut
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93 Table 5-3 lists estimates of equ
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95 5.4 Smart Service Schedule Savin
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97 7. A 6-ton RTU having a cost of
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99 Location North California South
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101 5. Three case studies were inve
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103 REFERENCES Aaron, D. A., and P.
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105 Davis, Coby. 1993. Comparison o
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107 Rossi, T.M., 1995. Detection, D
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Fault Detection and Diagnostics for Rooftop Air Conditioners

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