Analytics for Smart Energy Management – Tools and Applications for Sustainable Manufacturing

Analytics for Smart Energy Management – Tools and Applications for Sustainable Manufacturing
اسم المؤلف
Seog-Chan Oh , Alfred J. Hildreth
التاريخ
13 ديسمبر 2023
المشاهدات
227
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Analytics for Smart Energy Management – Tools and Applications for Sustainable Manufacturing
Seog-Chan Oh , Alfred J. Hildreth
Contents
1 Introduction . 1
1.1 Background of Sustainable Manufacturing . 1
1.2 Energy Consumption Review in the US Automotive Industry 4
1.3 Energy and Environment Management in Automotive
Manufacturing . 7
1.4 Smart Energy and Environment Management Using Data and
Model-Based Analytics 9
1.4.1 Example Decision Problem in Energy Management:
A Cost Comparison of Pneumatic and Electric
Actuator Systems . 14
1.5 Outline of Chapters . 20
1.6 Exercises 23
References . 26
2 Energy Performance Analysis: Stochastic Frontier Analysis
(SFA) and Data Envelopment Analysis (DES) for Energy
Performance Analysis 29
2.1 Background of Energy Performance Analysis . 29
2.1.1 Background of the Auto Manufacturing Process
and the Energy Consumption . 31
2.1.2 Literature Review . 33
2.1.3 Energy Performance Assessment . 35
2.2 SFA for Energy Performance Analysis 37
2.3 DEA for Energy Performance Analysis . 41
2.4 Illustrative Study . 44
2.5 Summary 51
2.6 Exercises 51
Appendix A: Derivation of the Log Likelihood Function
and First-Order Partial Derivatives for Cost Frontier Model . 52
viiAppendix B: Getting Started with Excel Solver for SFA
and DEA Analyses . 56
References . 76
3 Energy Decision-Making 1: Strategic Planning of Sustainable
Manufacturing Projects Based on Stochastic Programming . 79
3.1 Background of Planning Sustainable Manufacturing
Projects in the Manufacturing Industry 79
3.1.1 Literature Review . 81
3.2 A Problem Formulation in Stochastic Programming . 83
3.2.1 Objective Function 83
3.2.2 Constraints 86
3.3 Sample Averaging Approximation as a Solving Method 87
3.4 Illustrative Study . 89
3.4.1 Carbon Cost Scenario Generation 89
3.4.2 Parameter Settings for a Hypothetical Plant . 91
3.4.3 Assumptions and Cases for Study 92
3.4.4 Results . 93
3.5 Summary 96
3.6 Exercises 97
Appendix: Methods and Standards for Preparing Scope-3 Carbon
Footprints 99
References . 107
4 Energy Decision-Making 2: Demand Response Option Contract
Decision Based on Stochastic Programming 109
4.1 Background of Energy Demand Response . 109
4.1.1 Motivating Example . 110
4.1.2 Activity-Based Costing . 113
4.1.3 Activity-Based Plant Energy Forecasting Method . 118
4.1.4 Literature Review . 119
4.2 Chance-Constrained Stochastic Programming for Strategic
Decision Making . 121
4.3 Decision Model for Determining Energy Demand Response
Option Contract 123
4.4 Illustrative Example . 124
4.4.1 Identification of Input Parameters 126
4.4.2 Reduction in the Rate of Energy Demand (kW)
for State-Transition Flexible Activities . 127
4.4.3 Reduction in the Rate of Energy Demand (kW)
for QoS Flexible Activities . 127
4.5 Summary 132
4.6 Exercise . 133
References . 133
viii Contents5 Pattern-Based Energy Consumption Analysis by Chaining
Principle Component Analysis and Logistic Regression 137
5.1 Background of Energy Consumption Analysis . 138
5.2 Technologies for Pattern Training and Inference . 140
5.2.1 Principle Component Analysis (PCA) . 140
5.2.2 Multinomial Logistic Regression . 142
5.2.3 K-Means Clustering Algorithm 143
5.3 A Classification Model for Energy Consumption Pattern
Training and Inference . 143
5.3.1 Training Steps: Design Time . 144
5.3.2 Inference Steps: Real Operation Time . 146
5.3.3 Scikit-Learn Machine Learning Library in Python . 146
5.4 Illustrative Example . 147
5.5 Summary 152
5.6 Exercises 153
Appendix: Getting Started with IPython Notebook for Energy
Pattern Analysis . 153
References . 176
6 Ontology-Enabled Knowledge Management in Environmental
Regulations and Incentive Policies . 179
6.1 Background of Energy and Environment Knowledge
Management 179
6.2 EU-ETS and Waxman-Markey Bill (W-M Bill) 183
6.2.1 European Emission Trading Scheme (EU-ETS) . 183
6.2.2 Waxman-Markey Bill (W-M Bill) 183
6.3 Technologies for Semantic Data Management . 185
6.3.1 Description Logic (DL) . 185
6.3.2 Semantic Data Model: RDF 186
6.3.3 Semantic Data Query: SPARQL . 186
6.4 ERIPAD Ontology 187
6.4.1 TBox and ABox 187
6.4.2 Knowledge Acquisition and Dissemination in ERIPAD . 188
6.5 Illustrative Example of Knowledge Management
with ERIPAD . 192
6.5.1 Semantic Queries with Apache Jena Fuseki . 192
6.5.2 CO2 Emission Management Decision Process
with ERIPAD 192
6.6 Summary 195
6.7 Exercises 195
References . 197
Contents ix7 Energy Simulation Using EnergyPlus™ for Building
and Process Energy Balance 199
7.1 Background of Energy Simulation and EnergyPlus . 199
7.2 Illustrative Example 1: Assessment of the Use of Air
Conditioning Economizer . 202
7.2.1 What Is an Air Conditioning Economizer? 203
7.2.2 Modelling and Simulation with EnergyPlus . 203
7.2.3 Analysis Results 205
7.3 Illustrative Example 2: Assessment of the Use of a Mist
Collection System with Different Ventilation Strategies 207
7.3.1 What Is a Mist Collection System? . 207
7.3.2 Dynamic Ventilation Strategy for a Mist Collection
System . 210
7.3.3 Modelling and Simulation with EnergyPlus . 210
7.3.4 Analysis Results 214
7.4 Summary 215
7.5 Exercises 215
Appendix: Getting Started with EnergyPlus for Manufacturing
Process Simulation . 216
References . 244
8 Energy Management Process for Businesses 245
8.1 Importance of Energy Management to Business 246
8.2 Integrating Energy Management into the Global
Business Plan . 248
8.2.1 Make a Commitment . 248
8.2.2 Business Planning . 249
8.2.3 People . 250
8.3 Establishing Targets and Public Goals 250
8.3.1 Data Management . 250
8.3.2 Data Verification and Assurance . 252
8.3.3 Establishing a Baseline . 252
8.3.4 Science-Based Targets 254
8.4 Benchmarking, Budgets, and Forecasts 256
8.4.1 Benchmarking . 256
8.4.2 Budgets and Forecasts 257
8.5 Action Plan 261
8.5.1 Sufficiency Plans 261
8.5.2 Energy Projects and Conservation 262
8.5.3 Check Progress . 263
8.6 Energy Management Tools 264
8.6.1 Internal Recognition . 264
8.6.2 External Recognition . 265
8.7 Exercise . 266
References . 267
x Contents9 Energy Efficiency Accounting to Demonstrate Performance . 269
9.1 Selling the Need to Fund Projects . 269
9.1.1 Strategic Plan 271
9.1.2 Accountability . 273
9.1.3 Data Systems 273
9.2 Developing Energy Efficiency Projects 276
9.2.1 Energy Project Tracking . 276
9.2.2 Energy Project Technology . 278
9.3 Prioritization of Projects 279
9.3.1 Energy Use . 279
9.4 Closing the Gap to Benchmark with Energy Efficiency 281
9.4.1 Energy Drivers . 281
9.4.2 Design Energy Efficiency into New Processes
and Facilities 284
9.5 Measurement and Verification 286
9.5.1 M&V Baseline Plan . 287
9.5.2 Post-retrofit M&V . 288
9.6 Exercise . 289
References . 290
Index . 29
Index
A
AA-1000AS, 252
Activity based costing (ABC), 12, 21, 112,
113, 120
Activity based energy accounting (ABEA), 10,
258
Advancing open standards for the information
society (OASIS), 120
Air compressor, 17
Air conditioning economizer, 13, 22, 201, 203,
216
Air cylinder, 15
Air flow rate per minute, 235
Air leak, 8, 14, 17
Analytics
description, 9, 13
prediction, 9, 10
prescription, 9, 10
Apache Jena – Fuseki, 179, 182, 186, 192
Apache Jetty web server, 192
Apache Tomcat web server, 192
ARPA agent markup language (DAML), 185
Assembly, Casting, Engine, Stamping and
Transmission, 251, 252
Assertion box (ABox), 182, 185–188
Association of Energy Engineer (AEE), 265
Automotive industry, 3, 11, 21
B
Benchmarking, 10, 11, 20, 21, 23, 30–32, 34,
44, 51, 52, 58, 82, 246, 255–257, 264,
269, 272, 281, 287
Bill of equipment (BOE), 117
Biomass, 26
Breakeven point, 17–19
British thermal units (BTU), 4, 5
Building Portfolio Manager, 256, 257, 259
Business plan deployment (BPD), 246, 249,
264
C
California Climate Action Register, US, 102
Carbon accounting, 248
Carbon credit price, 84, 89, 90
Carbon credits, 80, 86, 94, 267, 272
Carbon dioxide (CO2) equivalent, 97, 100
Carbon Disclosure Project (CDP),
International, 102, 248
Carbon emission, 4, 5, 80–86, 93–97, 99, 100
Carbon footprint, 6, 96, 97, 99–101, 105, 107,
248, 262, 270, 274, 275
Car making process
air abatement, 125
air conditioning, 115
baking, 8, 126
building lighting, 115
electro coat primer operation (ELPO), 125
foundry, 137
liquid moving, 124–126
machining centre, 137, 204, 213
manual assembly, 126
moving conveyors, 124, 126
operating chillers, 124–126, 129, 131, 132
operating repairing centers, 124, 126
operating robots, 115, 124, 126
welding, 8, 217, 218, 243
CDP Climate Change, 272
CDP disclosure and performance indices, 265
Charnes-Cooper-Rhodes (CCR) model, 33, 34
Clamping, 14
Clean energy management software, 193
HOMER, 202
PVWatts, 202
RETScreen, 193, 202
Solar Advisor Module, 202
Commitment and Accountability Program
(CAP), 265
Compressed air, 8, 14, 21
Compressor, 8, 14, 17, 24
Constant returns of scale (CRS), 33, 48, 65
Continuous improvement (CI), 247, 249, 257,
262, 266
Convexity, non-convexity, 34
Cooling degree days (CDD), 30, 38, 41, 59,
251
CPLEX
IBM ILOG CPLEX Optimization Studio,
122
Cradle-to-grave (C2G) analysis, 196
CRC Energy Efficiency Scheme, UK, 102
Cross validation, 49
Cubic feet per minute (CFM), 15
D
Data envelopment analysis (DEA), 10, 11, 20,
21, 31
Decision making, 13, 19, 21, 22
Decision making unit (DMU), 34, 65
Demand response option contract, 113
Demand response program, 110, 120, 132
Department of Energy (DOE), 7, 8, 13, 246,
253
Description logic (DL), 185–187, 195
Dow Jones sustainability Index, 265
E
Ecological equilibrium, 1, 6
Econometrics
cost model, 11, 13, 34
production model, 11, 34
Economic Input-Output Lifecycle Assessment
(EIO-LCA), 105–107
Eigenvector, 21, 137, 141–143, 151, 153, 172
Electric acutator, 14, 15, 18
Electricity, 3–5, 7, 25
Energy balancing constraints, 121
Energy consuming, 6–8, 114, 117, 124
Energy consumption pattern, 137, 140, 143,
153, 154, 165
Energy demand and supply conservation
equations, 122, 129
Energy efficiency, 269, 272, 276–278, 281,
284, 285, 287
Energy forecasting, 10, 23, 118
Energy Information Agency (EIA), 102
Energy intensity, 5, 9, 118, 282, 283, 285, 290
Energy load curtailment, 21, 110, 111, 120
Energy load shedding, 21, 115
Energy load shifting, 110
Energy Management, 245, 246, 248–250,
264–266
Energy monitoring and tracking, 138
Energy OnStar, 252, 261, 264, 273, 274
Energy performance index (EPI), 33, 40, 41,
46, 52
Energy performance indicator (EPI), 21, 253,
256
EnergyPlusTM, 13, 22, 199–201, 203–214
EPLaunch, 221, 222
IDF editor, 203, 210, 221, 222
Energy protection agency (EPA), 23, 26, 99,
102
Energy simulation, 22, 199, 201, 202, 207, 215
dynamic ventilation, 210, 240, 241
humidity, 200, 209, 210, 216, 238
infiltration, 205, 206, 213, 236
setpoint, 200, 231–233
thermostat, 231, 232, 234
Energy sourcing, 6, 24
Energy Star’s®
challenge for industry, 246, 265, 266
portfolio Manager, 256, 257, 259
Energy use activity, 115, 122
maintenance, 115
production, 115
setback, 115, 117
shutdown, 115, 117
startup, 115, 117
Environmental principles, 248–249
Environmental Regulations and Incentive
Policies Acquisition and Dissemination
(ERIPAD), 22, 179, 181, 195
EPA Energy Star, 23
European Union Emission Trading
Scheme (EU-ETS), 81, 179, 182, 183
Exhausting, recirculating, 210, 212–215, 236
Expectation of expected value (EEV), 89, 93,
95, 98
Expected value of perfect information (EVPI),
98
Explicit knowledge, 181
Exponential distribution, 41
F
Federal Energy Regulatory Commission
(FERC), 110
First derivatives
MLE, 45, 56, 58
Forecasting energy, 245
292 IndexFrontier line
SFA, 31, 35, 37
Fuel switching, 277, 278
G
General algebraic modeling system (GAMS),
81
Generalized reduced gradient (GRG), 32, 45,
56, 58, 64
General Motors (GM), 5, 9, 10, 22, 245, 246,
249
General Motors Global Manufacturing System
(GMS), 249
Geothermal, 25
GHG Accounting and Reporting System,
Japan, 101, 102
GHG Emissions Reporting Program, Canada,
102
GHG protocol emission factors, 245, 246, 248,
250, 252, 254, 255
GHG Reporting Rule, US, 102
GM Code of Conduct, 249
GNU Linear Programming Kit (GLPK), 97
Greenhouse gas (GHG), 3, 6, 22, 23, 99–105,
248, 250, 254, 255, 269, 270, 273
management plan, 250
protocol, 248
Greenhouse gases, Regulated Emissions, and
Energy use in Transportation
(GREETTM), 196
Grid search, 137, 140, 145, 151, 154, 165, 172,
174
Gross domestic product (GDP), 2
H
Half-normal distribution, 52
Heating degree days (HDD), 30, 37, 58, 251,
282, 283
Heating ventilation and air conditioning
(HVAC), 7, 8, 9, 13, 22, 200, 208, 215,
247, 252, 263, 264, 273, 274, 278, 279,
283
Hicksian neutral technological change, 31, 58
Hurdle rates, 271
Hydropower, 25
I
Illuminating engineers Society of North
America (IESNA), 263
Implicit (tacit) knowledge, 181
Infrared (IR) paint curing, 8
International Energy Agency (IEA), 246, 272
International measurement and verification
protocol (IPMVP), 287, 288
International Organization for Standardization
(ISO), 101
International Performance Measurement &
Verification Protocol (IPMVP), 253
Interoperability, 109, 182
ISO-14064, 252
J
Java-based Expert System (JESS), 185
JOSEKI web server, Hewlett Packard, 182
K
Karush–Kuhn–Tucker condition, 130
K-bin discretized probability distribution, 122,
127
K-Means clustering, 13, 140, 141, 143
Knowledge
acquisition, 188
annotation, 190, 191
artifact, 181, 183, 187
assertion, 185
dissemination, 179, 181, 182, 188, 190
externalization, 181
lifecycle, 181
management, 192, 195
personalization, 181
store, 185, 187
Kyoto Protocol, 102
L
Landfill gas, 25
LED retro-fits, 272
Lieberman-Warner bill, 179
Lighting, 272, 278, 279, 281
Linear programming (LP), 11
Logistic regression, 13, 21, 22, 137, 140, 142,
143, 147, 151–154, 165, 172, 174
M
Machine learning, 146, 147, 153
chaining, 137, 151, 154
classification, 146, 147
clustering, 146
pattern recognition, 140
pipelining, 137, 152, 154
training and inference, 143, 144, 146, 153
Machine-readable, human-readable, 189
Malmquist productivity change index, 31, 51
Malmquist total factor productivity
(TFP) index, 34, 37, 41
Index 293Matplotlib in Python, 156, 164
Maximum likelihood estimation, 11, 45, 56, 58
Measurement and verification (M&V),
286–289
Mist collection, 201, 207–209, 214, 215
Mixed-integer programming, 122
MS Excel Solver, 45, 47
MS Excel Visual Basic for Applications
(VBA), 44, 47, 67
Multicollinearity, 137, 142, 143, 151, 172
Multinomial regression model, 152
Multiple Discriminant Analysis (MDA), 140,
141, 143
Multi-variable regression analysis, 271
N
National Greenhouse and Energy Register,
Australia, 102
National Renewable Energy Laboratory
(NREL), 286
Natural gas, 7
Nonparametric modeling, 11
North American Industry Classification System
(NAICS), 4, 5
Numpy library in Python, 146, 154
O
OASIS Energy Market Information Exchange
Technical Committee (eMIX), 120
Oil, 26
One-sided likelihood-ratio test values (LR), 39,
45, 64
Ontology, 13, 22, 179, 181, 183, 185, 187–189
Open Automated Demand Response
Communication Standards (OpenADR),
132
Optimization, 81, 83, 87, 120–122, 130
Option premium price, 110
Option strike price, 110, 111
Ordinary Least Square (OLS), 11
Orthogonal axe, 137, 142, 143, 151, 172
OWL Web Ontology Language (OWL), 185
P
Parametric modeling, 11
Pattern recognition, 13
Payback, 270, 271, 273, 279, 281, 289, 290
Peak rate of energy demand, 117, 126
Plan, do, check, act methodology (PDCA), 23,
245
Plant utillization, 30, 36–38, 59, 66
Pneumatic actuator, 18
Principle component analysis (PCA), 12, 13,
21, 22, 137, 139–143, 145–147,
151–154, 165, 172, 174
dimension reduction, 137, 142, 143, 147,
172
decomposition, 146, 151, 173
decompression, 151
Publicly Available Specification (PAS), 105
Python, IPython Notebook, 22, 137, 140, 154
Q
Quality of service (QoS), 21, 113, 115,
121–124, 127, 129, 131, 132
R
RACER, Pellet – reasoning engine for
OWL-DL, 185
Radiant, latent heat, 201, 216
Regional Greenhouse Gas Initiative, US, 102
Relative humidity (RH), 251
Renewable energy, 12, 24, 26
Resource consumption accounting (RCA), 133
Resource Description Framework (RDF), 186
S
Sample average approximation (SAA), 21, 87,
88, 93–96
Science-based targets, 254–255
Scikit-Learn Machine Learning Library in
Python, 146, 153, 154
Scope 1 & 2 emissions, 100–103, 107, 248
Scope 3 emissions, 101, 103, 105, 107, 248,
251
Semantics, 182
Shutdown performance, 283
Simplex method
linear programming, 35, 65
Smart energy management, 6, 9
Smart grid, 109–112, 119, 120, 132
SPARQL Protocol and RDF Query Language
(SPARQL), 22, 179, 182, 186, 188,
190, 192–195
Spreadsheet solution, 47, 70
Standard query language (SQL), 186
Steam elimination, 273, 278, 281
Stochastic frontier analysis (SFA), 10, 11, 20,
21, 31–40, 44, 45, 58, 131, 133
Stochastic Programming
chance constraint type, 12, 121
Here and now (HN), 89, 98
294 Indexrecourse type, 12
Wait and see (WS), 98
Supply chain, 3, 4
Sustainable manufacturing, 1, 3, 6, 7, 12, 21
Syntactic, 182
T
Terminological box (TBox), 182, 185–188
Thermodynamics, 13
Thermodynamic simulator
Building Loads Analysis and System
Thermodynamics (BLAST), 200
DOE-2, 200
Ton of CO2 equivalent (tCO2e), 100
Transactional energy market information
exchange (TeMIX), 110
Traveling salesman problem, 70
Truncated normal distribution, 41
U
Ultraviolet (UV) paint curing, 8
Uncertainty in energy demand, 121
Uncertainty in energy demand on climate
change COP, 21, 245
United Nations Global Compact, 254
Universal Modeling Language (UML), 116,
119, 258
state diagram, 116
US DOE Energy Information Agency (EIA),
272
U.S. Environmental Protection Agency
(EPA) Energy Star®, 246
US EPA Boiler maximum achievable control
technology (MACT), 248
Utility service provider (USP), 110, 111
V
Value of stochastic solution (VSS), 89, 95, 98
Variable frequency drives (VFD), 273, 278
Variable returns to scale (VRS), 33, 34
Volatile organic compounds (VOCs), 8
Voluntary Emissions Trading System, Japan,
102
W
Water chiller, 8
Water’s kinetic energy, 25
Waxman Markey Bill, 22, 82, 94, 179,
182–185
Web crawler, 195
Web service, 182
Western Climate Initiative, US, 102
Wheelbase, 36, 37, 42, 66
World Business Council for Sustainable
Development (WBCSD), 102
World Resources Institute (WRI), 101, 102,
254
World Wide Web consortium (W3C), 182, 186
World Wildlife Federation (WWF), 254

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