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E-raamat: Analytics for Smart Energy Management: Tools and Applications for Sustainable Manufacturing

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Analytics for Smart Energy Management: Tools and Applications for Sustainable ManufacturingSuurem pilt
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This book introduces the issues and problems that arise when implementing smart energy management for sustainable manufacturing in the automotive manufacturing industry and the analytical tools and applications to deal with them. It uses a number of illustrative examples to explain energy management in automotive manufacturing, which involves most types of manufacturing technology and various levels of energy consumption.





It demonstrates how analytical tools can help improve energy management processes, including forecasting, consumption, and performance analysis, emerging new technology identification as well as investment decisions for establishing smart energy consumption practices.





It also details practical energy management systems, making it a valuable resource for professionals involved in real energy management processes, and allowing readers to implement the procedures and applications presented.
1 Introduction
1(28)
1.1 Background of Sustainable Manufacturing
1(3)
1.2 Energy Consumption Review in the US Automotive Industry
4(3)
1.3 Energy and Environment Management in Automotive Manufacturing
7(2)
1.4 Smart Energy and Environment Management Using Data and Model-Based Analytics
9(11)
1.4.1 Example Decision Problem in Energy Management: A Cost Comparison of Pneumatic and Electric Actuator Systems
14(6)
1.5 Outline of
Chapters
20(3)
1.6 Exercises
23(6)
References
26(3)
2 Energy Performance Analysis: Stochastic Frontier Analysis (SFA) and Data Envelopment Analysis (DES) for Energy Performance Analysis
29(50)
2.1 Background of Energy Performance Analysis
29(8)
2.1.1 Background of the Auto Manufacturing Process and the Energy Consumption
31(2)
2.1.2 Literature Review
33(2)
2.1.3 Energy Performance Assessment
35(2)
2.2 SFA for Energy Performance Analysis
37(4)
2.3 DEA for Energy Performance Analysis
41(3)
2.4 Illustrative Study
44(7)
2.5 Summary
51(1)
2.6 Exercises
51(28)
Appendix A Derivation of the Log Likelihood Function and First-Order Partial Derivatives for Cost Frontier Model
52(4)
Appendix B Getting Started with Excel Solver for SFA and DEA Analyses
56(20)
References
76(3)
3 Energy Decision-Making 1: Strategic Planning of Sustainable Manufacturing Projects Based on Stochastic Programming
79(30)
3.1 Background of Planning Sustainable Manufacturing
Projects in the Manufacturing Industry
79(2)
3.1.1 Literature Review
81(2)
3.2 A Problem Formulation in Stochastic Programming
83(4)
3.2.1 Objective Function
83(3)
3.2.2 Constraints
86(1)
3.3 Sample Averaging Approximation as a Solving Method
87(2)
3.4 Illustrative Study
89(7)
3.4.1 Carbon Cost Scenario Generation
89(2)
3.4.2 Parameter Settings for a Hypothetical Plant
91(1)
3.4.3 Assumptions and Cases for Study
92(1)
3.4.4 Results
93(3)
3.5 Summary
96(1)
3.6 Exercises
97(12)
Appendix: Methods and Standards for Preparing Scope-3 Carbon Footprints
99(8)
References
107(2)
4 Energy Decision-Making 2: Demand Response Option Contract Decision Based on Stochastic Programming
109(28)
4.1 Background of Energy Demand Response
109(12)
4.1.1 Motivating Example
110(3)
4.1.2 Activity-Based Costing
113(5)
4.1.3 Activity-Based Plant Energy Forecasting Method
118(1)
4.1.4 Literature Review
119(2)
4.2 Chance-Constrained Stochastic Programming for Strategic Decision Making
121(2)
4.3 Decision Model for Determining Energy Demand Response Option Contract
123(1)
4.4 Illustrative Example
124(8)
4.4.1 Identification of Input Parameters
126(1)
4.4.2 Reduction in the Rate of Energy Demand (kW) for State-Transition Flexible Activities
127(1)
4.4.3 Reduction in the Rate of Energy Demand (kW) for QoS Flexible Activities
127(5)
4.5 Summary
132(1)
4.6 Exercise
133(4)
References
133(4)
5 Pattern-Based Energy Consumption Analysis by Chaining Principle Component Analysis and Logistic Regression
137(42)
5.1 Background of Energy Consumption Analysis
138(2)
5.2 Technologies for Pattern Training and Inference
140(3)
5.2.1 Principle Component Analysis (PCA)
140(2)
5.2.2 Multinomial Logistic Regression
142(1)
5.2.3 K-Means Clustering Algorithm
143(1)
5.3 A Classification Model for Energy Consumption Pattern Training and Inference
143(4)
5.3.1 Training Steps: Design Time
144(2)
5.3.2 Inference Steps: Real Operation Time
146(1)
5.3.3 Scikit-Learn Machine Learning Library in Python
146(1)
5.4 Illustrative Example
147(5)
5.5 Summary
152(1)
5.6 Exercises
153(26)
Appendix: Getting Started with IPython Notebook for Energy Pattern Analysis
153(23)
References
176(3)
6 Ontology-Enabled Knowledge Management in Environmental Regulations and Incentive Policies
179(20)
6.1 Background of Energy and Environment Knowledge Management
179(4)
6.2 EU-ETS and Waxman-Markey Bill (W-M Bill)
183(2)
6.2.1 European Emission Trading Scheme (EU-ETS)
183(1)
6.2.2 Waxman-Markey Bill (W-M Bill)
183(2)
6.3 Technologies for Semantic Data Management
185(2)
6.3.1 Description Logic (DL)
185(1)
6.3.2 Semantic Data Model: RDF
186(1)
6.3.3 Semantic Data Query: SPARQL
186(1)
6.4 ERIPAD Ontology
187(5)
6.4.1 TBox and ABox
187(1)
6.4.2 Knowledge Acquisition and Dissemination in ERIPAD
188(4)
6.5 Illustrative Example of Knowledge Management with ERIPAD
192(3)
6.5.1 Semantic Queries with Apache Jena Fuseki
192(1)
6.5.2 CO2 Emission Management Decision Process with ERIPAD
192(3)
6.6 Summary
195(1)
6.7 Exercises
195(4)
References
197(2)
7 Energy Simulation Using EnergyPlus™ for Building and Process Energy Balance
199(46)
7.1 Background of Energy Simulation and EnergyPlus
199(3)
7.2 Illustrative Example 1: Assessment of the Use of Air Conditioning Economizer
202(5)
7.2.1 What Is an Air Conditioning Economizer?
203(1)
7.2.2 Modelling and Simulation with EnergyPlus
203(2)
7.2.3 Analysis Results
205(2)
7.3 Illustrative Example 2: Assessment of the Use of a Mist Collection System with Different Ventilation Strategies
207(8)
7.3.1 What Is a Mist Collection System?
207(3)
7.3.2 Dynamic Ventilation Strategy for a Mist Collection System
210(1)
7.3.3 Modelling and Simulation with EnergyPlus
210(4)
7.3.4 Analysis Results
214(1)
7.4 Summary
215(1)
7.5 Exercises
215(30)
Appendix: Getting Started with EnergyPlus for Manufacturing Process Simulation
216(28)
References
244(1)
8 Energy Management Process for Businesses
245(24)
8.1 Importance of Energy Management to Business
246(2)
8.2 Integrating Energy Management into the Global Business Plan
248(2)
8.2.1 Make a Commitment
248(1)
8.2.2 Business Planning
249(1)
8.2.3 People
250(1)
8.3 Establishing Targets and Public Goals
250(6)
8.3.1 Data Management
250(2)
8.3.2 Data Verification and Assurance
252(1)
8.3.3 Establishing a Baseline
252(2)
8.3.4 Science-Based Targets
254(2)
8.4 Benchmarking, Budgets, and Forecasts
256(5)
8.4.1 Benchmarking
256(1)
8.4.2 Budgets and Forecasts
257(4)
8.5 Action Plan
261(3)
8.5.1 Sufficiency Plans
261(1)
8.5.2 Energy Projects and Conservation
262(1)
8.5.3 Check Progress
263(1)
8.6 Energy Management Tools
264(2)
8.6.1 Internal Recognition
264(1)
8.6.2 External Recognition
265(1)
8.7 Exercise
266(3)
References
267(2)
9 Energy Efficiency Accounting to Demonstrate Performance
269(22)
9.1 Selling the Need to Fund Projects
269(7)
9.1.1 Strategic Plan
271(2)
9.1.2 Accountability
273(1)
9.1.3 Data Systems
273(3)
9.2 Developing Energy Efficiency Projects
276(3)
9.2.1 Energy Project Tracking
276(2)
9.2.2 Energy Project Technology
278(1)
9.3 Prioritization of Projects
279(2)
9.3.1 Energy Use
279(2)
9.4 Closing the Gap to Benchmark with Energy Efficiency
281(5)
9.4.1 Energy Drivers
281(3)
9.4.2 Design Energy Efficiency into New Processes and Facilities
284(2)
9.5 Measurement and Verification
286(3)
9.5.1 M&V Baseline Plan
287(1)
9.5.2 Post-retrofit M&V
288(1)
9.6 Exercise
289(2)
References
290(1)
Index 291
Seog-Chan Oh, PhD is a senior researcher at the General Motors Research and Development Center, Warren, MI since 2007 where he has been developing analytical models for improvement in sustainable manufacturing. He has strong functional expertise in Operations Research and Advanced Statistics. He received the PhD degree in Industrial Engineering from Pennsylvania State University in 2006. Before he began his PhD studies, he was an IT consultant for seven years at Daewoo Information Systems in Korea. He won a Boss Kettering Award, GMs highest award for recognizing technical inventions and innovations. He also won a Korean Prime Minister Award, TeamGM Award, GM Patent Usage Award, IEEE Appreciation Award, Best Paper Award (KIIE), First Runner-Up Award (IEEE Web Service Challenge) and etc. He holds 4 Patents filed and 45 scholary articles including 3 Books. He has served on AIAG energy working group, IEEE Cloud and SCC program committees and IJWR editorial board.

Al Hildreth, PE, CEM is the Company Energy Manager for General Motors focusing on energy and utility budgets, metrics, benchmarking, efficiency projects, water and carbon reporting. He has worked for GM for over 30 years, including 5 years in Europe and Asia, 10 years with Saturn Corporation in Spring Hill, TN, and previously worked for a manufacturer of air pollution control equipment in R&D. He is a registered Professional Engineer, Certified Energy Manager, and a Certified Hazardous Materials Manager.  He received his Bachelor of Science degree in Engineering from Oakland University and a Masters of Science degree in Engineering from Rensselaer Polytechnic Institute.
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