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| موضوع: كتاب Analytics for Smart Energy Management - Tools and Applications for Sustainable Manufacturing الأربعاء 13 ديسمبر 2023, 3:24 pm | |
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أخواني في الله أحضرت لكم كتاب Analytics for Smart Energy Management - Tools and Applications for Sustainable Manufacturing Seog-Chan Oh , Alfred J. Hildreth
و المحتوى كما يلي :
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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