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The Key to Sustainable
Energy Optimization
Data-Driven Approach for
Manufacturing
Emergent Energy Solutions, LLC
Kevin Wong PMP, CEM, DGCP
25%
©Emergent Energy Solutions 2024
Agenda
• Challenges:
• Manufacturing Energy Demand
• Increasing Demand for Data
• Technology and Departmental Silos
• Opportunities
• Carbon Reporting and Reduction Journey
• Essential Features
• Case Studies
• Value Extraction
• Call to Action
©Emergent Energy Solutions 2024
©Emergent Energy Solutions 2024
Industrial Sector Energy Consumption
• Renewable & Nuclear – 21%
• Fossil Fuel Base – 79%
• The US Industrial Sector - 35% of energy use.
• + Commercial buildings, these 2 sectors
consumes 50% of the total energy
production
• Renewable energy continues to increase its
contribution to the overall production.
• Need for continued investments into energy
optimization
• 90% of utilities are no able to provide whole
building energy data (EPA)
©Emergent Energy Solutions 2024
Industrial Sector Growth
• Industrial Sector
• Forecasting significant growth
• Infrastructure Investment &
Jobs Act
• CHIPS Act
• IRA
• Onshoring of manufacturing
• Rising global demand
• AI
• Sustainability
©Emergent Energy Solutions 2024
Why Industrial?
• High % of built infrastructure, requiring
investments to boost efficiency
• High-Capacity Energy Users with large process
and infrastructure loads
• Limited Generation Capacity for on-site
Renewables
• Offsetting Carbon through PPA & RECs
• Sustained decarbonization efforts include efficiency improvements
• Data is essential and will become more critical
©Emergent Energy Solutions 2024
Non-Productive Energy Use
(Waste)
20 - 30% of C&I building
energy consumption is
wasted*
Lack of Visibility on where
energy is used
Electrification
Limited Grid Capacity,
requiring Capacity
Restrictions
Resiliency & Reliability
Concerns
Co-Generation
Corporate Sustainability
Carbon Accounting &
Reporting
Goal Setting
Target vs. Reality
Growing Federal, State and
Municipal Regulations
Reporting & Disclosures
Efficiency
Benchmarking
Growing Demand for Data
*U.S. Environmental Protection Agency
©Emergent Energy Solutions 2024
U.S. Public Companies Closing
Sustainability Reporting Gap in 2022
• Large-cap companies in the S&P 500 approached 100%
reporting, with a record 98% publishing sustainability reports in
2022.
• Mid-cap companies, which make up the smallest half by market
cap of the Russell 1000, continue to close the reporting gap with
82% publishing reports in 2022, up from 68% in 2021.
• Companies in the total Russell 1000 Index had a record 90%
publishing reports in 2022, up from 81% in 2021.
• Alignment with TCFD continued to grow rapidly, now utilized by
50% of Russell 1000 reporters in 2022 compared to just 4% in
2019.
• SASB remained the most widely-used sustainability standard
among the Russell 1000, utilized by 78% of reporters in 2022,
while alignment with GRI remained consistent at 54%.
• Source: Governance and Accountability Institute
©Emergent Energy Solutions 2024
https://www.ncsl.org
• California Statutory Targets: 40% by
2030
• New York Statutory Targets: 40% by
2030; 85% by 2050
• Colorado Statutory Targets: 26% by
2025; 50% by 2030; 90% by 2050
• Boston Statutory Targets: 80% by
2050
• Maryland Statutory Targets: 40% by
2030
• Puerto Rico Statutory Requirements:
50% by 2025
Legislative Reduction and Reporting Targets
©Emergent Energy Solutions 2024
Manufacturing Technology Infrastructure
Complexity & Data Needs
14
Environmental systems
E.g. industrial heating, air conditioning
units, air handling units and building
management systems.
5
On-site generation
E.g. Combined Heat and Power units
(CHP), solar PV
4
Industry assets
E.g. Pumps with VSD/without VSD,
compressors, production lines and
waste/clean water process
3
Operational assets
E.g. Refrigeration units, chillers,
bitumen tanks, water boilers
2
5 4
4
3
2
1
Energy resilience
E.g. Batteries, standby or back up
generation and diesel generators
1
Process Management
E.g. Cooking, Chilling, Packaging,
Freezing, process energy knowledge,
effluent discharge & air management
6
©Emergent Energy Solutions 2024
Expanding Data Complexity
Project
Development
w/ GHG
Impact
Estimates
PLC
Maintenance
& Technical
Silos
Non-
Integrated
processes
Life Safety,
HVAC &
Security
Systems
Process
SCADA
Overlays w/
Business &
Industrial
Networks
Generation &
Resiliency
Assets
EHS /
Regulatory
©Emergent Energy Solutions 2024
Process SCADA
Infrastructure
Automation
Systems
OEE /
Downtime
Operational /
Maintenance /
Capital Budgeting
ERPS / CMMS
Energy
Barriers - Manufacturing Data Silos
 Data is collected in manufacturing
environments (sensors, IoT devices, etc.)
 Silo’ed based on department and
organizational hierarchy
 Data is not organized for the customer by
the technology partner
 Lack of Energy Metrics as energy has
been an accounting function (budgeting /
forecasting) when invoices are received
 Poor information transfer from attrition and
changing roles
 Increasing cost pressures and
competition
©Emergent Energy Solutions 2024
Process Map and
Capabilities for Energy
Intelligence
©Emergent Energy Solutions 2024
01
02
03
04
05
Sustainability Journey
Commitments
Scope 1, 2, 3, Water Reduction
based on SBTi
DEI
Recyclability / Materials
Success Mapping
Data Shortfall Realized
Project Development
ESCO / Consultant
Benchmark
Commitment vs. Reality
Savings Validation
Engagement
Commitment adoption
Site Leadership
Ownership
Funding
Data Expansion
Capital / Green Bond
©Emergent Energy Solutions 2024
Knowledge Transfer
Prioritize Metrics
Knowledge Transfer
Continuous Engagement
First step
Stakeholder Engagement
We engage with you to
understand your business needs,
energy requirements, challenges
to ensure our solution solves a
problem.
KPI’s & Core Data Prioritization
We conduct a review of usable assets,
desired metrics, gather your power
data and model the commercial
benefits.
Business Case
We lay out our analysis of
optimizing your assets and
share the results.
Engage & Design
We engage our engineering
resources to verify and design the
solution at your site.
Installation
We work with you to
design and install your
solution, in line with your
energy requirements and
flexibility goals.
Train
You run your business as per
normal, all managed through your
energy management platform.
Sustain Sustainability
We automatically manage
your assets to optimize your
load without impacting your
business.
2
3
4
Contracts
We draft a commercial
contract and present to you
for signature.
5
6
7
8
1
Step 1 - Continuous Engagement to Mitigate silos
©Emergent Energy Solutions 2024
Nat.
Gas
Water
Electric
Comp
Air
Steam ChWat N2 HtWat
Step 2 - Technology Roadmap
API / EXPORT / IMPORT
Step 3- Access for all
• Can be accessed globally rather than restricted to a site
• Expand user base – Increasing data engagement by all
levels (Right sizing data for the individual)
• Browser based
• Intelligence driven dashboards, identifying key metrics
• Export / Import capabilities
©Emergent Energy Solutions 2024
Customized Visualization & Report Energy Flow
©Emergent Energy Solutions 2024
Visualize & Report
Time of Use
©Emergent Energy Solutions 2024
Energy Flow – Where and How Energy is Used
©Emergent Energy Solutions 2024
Automated Fault Detection
• AFDD needs energy metrics
 Data Driven Event Triggers
 Energy Thresholds
 Custom Reporting
©Emergent Energy Solutions 2024
• Trend Analysis: Observing how energy consumption or production trends over time, which can help in identifying
patterns or anomalies.
• Comparative Analysis: Comparing energy usage across different periods, locations, or systems to identify areas
of high consumption or efficiency.
• Load Forecasting: Using historical data to predict future energy demand. This is particularly useful for energy
management and planning.
• Cost Analysis: Calculating the cost implications of energy usage, considering different tariffs and operational
hours.
• Carbon Footprint Analysis: Estimating the environmental impact of energy consumption, particularly in terms of
carbon dioxide emissions.
• Efficiency Analysis: Identifying opportunities for energy savings by analyzing energy usage efficiency of
systems or processes.
• Anomaly Detection: Identifying unexpected spikes or drops in energy consumption that may indicate equipment
issues or other problems.
• Renewable Energy Analysis: Analyzing data related to renewable energy sources, like solar or wind, to assess
performance and reliability.
Data Analysis Needs
©Emergent Energy Solutions 2024
Utility Bill Parity
Progress against
targets
Resource
Allocation
REC / Offsets
Reporting &
Disclosure
Reduction Goals
VP Sustainability
Budget Planning
Performance
against budget
Energy
Forecasting
Goal Tracking
Plant Manager
Annual Project
Planning
Efficiency Gains
ROI
Carbon Impact
Utility Rebates
Demand
Response
Capital Team
Engineering
•Daily / Weekly
Performance
•Production Line
•Centrical Plant
•Shift Meetings
•Energy Outliers
•Equipment
Cycling
•Dashboard
Maintenance &
Operations
Step 4 - Right Sizing Data - Customization for the User
• Customized to the User.
• Each team leader has a different
priority and customization.
©Emergent Energy Solutions 2024
Case Study #1 – Product Level Carbon Assignment
Electric Nat Gas
Compressed
Air
Steam Water Total Energy
• Impact – All levels
• Product or Production Line
Level Energy / Carbon
Allocation
• True Cost of Energy / Unit
Produced
©Emergent Energy Solutions 2024
Case Study #2 – Down day vs. Full Production Energy Demand
• Impact –
• Maintenance
• Sanitation
• Down day Efficiency
• Opportunity
• Baseline Site Water
Use
©Emergent Energy Solutions 2024
Case Study #2 – Down day vs. Full Production Energy Demand
• Impact
• Maintenance
• Engineering
• Plant Management
• Sustainability
• Down day efficiency
• Baseline non-volume
specific energy load
• Per Unit Impact
Calculations
©Emergent Energy Solutions 2024
Case Study #3 - Predictive Maintenance
Facility maintenance is a large and
unpredictable budget item for
commercial and industrial buildings.
A few specific examples of spotting energy waste
• VFD equipped pumps and fans not modulating
• Makeup Air Units running after hours
• Systems running out of sequence
• Utilities not correlating with production demand
• Exterior lighting on during daytime hours
 Real-time electrical energy data reveals insights into
the health of mechanical equipment
 Amp data provides insights on run-time, loading,
cycling, and other elements that indicate machine
health
 Track equipment as individual assets within the system,
which creates an electrical fingerprint and maintain a
history of the asset
 Relative to specialized technologies, metering systems
are easy-to-implement, scalable across many asset
types bringing accessibility to predictive maintenance
©Emergent Energy Solutions 2024
Case study #4 – Solar Inverter Output Benchmarking
 Benchmark Inverter
Output
 Notifications of Output
shortfalls
 Set output thresholds
for individual panels to
identify cleaning needs
©Emergent Energy Solutions 2024
Data-Driven Project Development w/ Continuous
Engagement
Development of Capital projects with
Sustainability Impact:
• Estimated savings based on assumptions
• Rules of thumb
• Use estimates provided by vendor to justify
projects
• Lack of true system run hours
Once implemented:
• Assume the project worked and is yielding the
expecting savings
• If energy consumption remains high, assume
something else has changed
• Limited follow-up with the vendor or internal team
to discuss performance
Development of Capital projects with
Sustainability Impact:
• Calculations based on run hours and current energy
use
• Pursue specific projects based on net data driven
value
• Use real data on specific pieces of equipment to have
confidence in potential impact
• Only pursue targeted projects with high-confidence
Once implemented:
• Measure that the project is working as expected, and
make sure it stays that way
• Track changes against your baseline
• Use data to refine performance
• Data drives dialog and continuous commissioning
©Emergent Energy Solutions 2024
Data Driven Financial Incentives
Energy Markets (without data)
• Demand Response - Performance is based on
guessing game
• Dynamic Market Pricing – Limited data on
curtailable resources
• Generation Assets – Limited visibility on
performance, time of use and utilization
Utility Incentives - Process (Rate of Production)
and Infrastructure Enhancing projects – Lack of data,
Custom Incentives are not pursued.
Grants – Lack of data for Grant Applications,
reinforcing net value
Project Development - Increased cost for
project development by ESCOs and consultants
Energy Markets (with data)
• Demand Response – Circuit level monitoring
allowing for improved contract performance and
expansions
• Dynamic Market Pricing – Full visibility into assets
and permitting participation in energy markets
• Generation Assets – Visibility into performance,
time of use and utilization. Generate or Grid
Energy depending on best financials
Utility Incentives - Process (Rate of Production)
and Infrastructure Enhancing projects – Data drives
Custom Incentive. Incentives are typically capped at
1 year payback
Grants – Data driven grant applications, improving
chances of success
Project Development – Reduce project
development time and cost
©Emergent Energy Solutions 2024
Future Trends and Innovations
• Actionable Reports
• ECM M&V
• ASHRAE Energy Audits
• Analytics
• FDD
• Building & Process Science
• Automated Data Interpretation
• Expansion of Devices
• Benchmarking Data
• Carbon Reporting
• Strategic Energy Planning
• SKU Level Carbon Tracking
Decarbonization Data Availability
Data
Interpretation
Innovation & AI
©Emergent Energy Solutions 2024
Cost Reduction and
Operational
Efficiency
Competitive
Advantage
Environmental
Responsibility
Regulatory
Compliance
Predictive
Maintenance
Data-Driven Decision
Making
Resource Allocation
and Budget Planning
Resilience and Risk
Mitigation
Conclusion
©Emergent Energy Solutions 2024
Call to Action:
Data Driven Energy Optimization
Energy Metrics with maintenance /
engineering / accounting for operational
excellence. Perform deep dives using data to
drive discussions.
Migrate to low-carbon and renewable energy
sources while leveraging time of use energy
pricing. Leverage data to evaluate financial
impact.
Continuously monitor performance and
identify opportunities for reductions through
KPI benchmarking. Verify impact from
measures taken
Improve site resilience with a deeper
understanding of your energy operations at all
levels of your organization.
Leverage data to enhance machine efficiency,
optimize production processes and reducing
energy waste by developing baseline
production and non-production Energy Use
Intensity (EUI)
Reduce budgeting risk exposure by forecasting
energy demand and cost based on EUI and
multi-year benchmarking
$
©Emergent Energy Solutions 2024
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The Key to Sustainable Energy Optimization: A Data-Driven Approach for Manufacturing

  • 1.
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  • 5. The Key to Sustainable Energy Optimization Data-Driven Approach for Manufacturing Emergent Energy Solutions, LLC Kevin Wong PMP, CEM, DGCP 25% ©Emergent Energy Solutions 2024
  • 6. Agenda • Challenges: • Manufacturing Energy Demand • Increasing Demand for Data • Technology and Departmental Silos • Opportunities • Carbon Reporting and Reduction Journey • Essential Features • Case Studies • Value Extraction • Call to Action ©Emergent Energy Solutions 2024
  • 8. Industrial Sector Energy Consumption • Renewable & Nuclear – 21% • Fossil Fuel Base – 79% • The US Industrial Sector - 35% of energy use. • + Commercial buildings, these 2 sectors consumes 50% of the total energy production • Renewable energy continues to increase its contribution to the overall production. • Need for continued investments into energy optimization • 90% of utilities are no able to provide whole building energy data (EPA) ©Emergent Energy Solutions 2024
  • 9. Industrial Sector Growth • Industrial Sector • Forecasting significant growth • Infrastructure Investment & Jobs Act • CHIPS Act • IRA • Onshoring of manufacturing • Rising global demand • AI • Sustainability ©Emergent Energy Solutions 2024
  • 10. Why Industrial? • High % of built infrastructure, requiring investments to boost efficiency • High-Capacity Energy Users with large process and infrastructure loads • Limited Generation Capacity for on-site Renewables • Offsetting Carbon through PPA & RECs • Sustained decarbonization efforts include efficiency improvements • Data is essential and will become more critical ©Emergent Energy Solutions 2024
  • 11. Non-Productive Energy Use (Waste) 20 - 30% of C&I building energy consumption is wasted* Lack of Visibility on where energy is used Electrification Limited Grid Capacity, requiring Capacity Restrictions Resiliency & Reliability Concerns Co-Generation Corporate Sustainability Carbon Accounting & Reporting Goal Setting Target vs. Reality Growing Federal, State and Municipal Regulations Reporting & Disclosures Efficiency Benchmarking Growing Demand for Data *U.S. Environmental Protection Agency ©Emergent Energy Solutions 2024
  • 12. U.S. Public Companies Closing Sustainability Reporting Gap in 2022 • Large-cap companies in the S&P 500 approached 100% reporting, with a record 98% publishing sustainability reports in 2022. • Mid-cap companies, which make up the smallest half by market cap of the Russell 1000, continue to close the reporting gap with 82% publishing reports in 2022, up from 68% in 2021. • Companies in the total Russell 1000 Index had a record 90% publishing reports in 2022, up from 81% in 2021. • Alignment with TCFD continued to grow rapidly, now utilized by 50% of Russell 1000 reporters in 2022 compared to just 4% in 2019. • SASB remained the most widely-used sustainability standard among the Russell 1000, utilized by 78% of reporters in 2022, while alignment with GRI remained consistent at 54%. • Source: Governance and Accountability Institute ©Emergent Energy Solutions 2024
  • 13. https://www.ncsl.org • California Statutory Targets: 40% by 2030 • New York Statutory Targets: 40% by 2030; 85% by 2050 • Colorado Statutory Targets: 26% by 2025; 50% by 2030; 90% by 2050 • Boston Statutory Targets: 80% by 2050 • Maryland Statutory Targets: 40% by 2030 • Puerto Rico Statutory Requirements: 50% by 2025 Legislative Reduction and Reporting Targets ©Emergent Energy Solutions 2024
  • 14. Manufacturing Technology Infrastructure Complexity & Data Needs 14 Environmental systems E.g. industrial heating, air conditioning units, air handling units and building management systems. 5 On-site generation E.g. Combined Heat and Power units (CHP), solar PV 4 Industry assets E.g. Pumps with VSD/without VSD, compressors, production lines and waste/clean water process 3 Operational assets E.g. Refrigeration units, chillers, bitumen tanks, water boilers 2 5 4 4 3 2 1 Energy resilience E.g. Batteries, standby or back up generation and diesel generators 1 Process Management E.g. Cooking, Chilling, Packaging, Freezing, process energy knowledge, effluent discharge & air management 6 ©Emergent Energy Solutions 2024
  • 15. Expanding Data Complexity Project Development w/ GHG Impact Estimates PLC Maintenance & Technical Silos Non- Integrated processes Life Safety, HVAC & Security Systems Process SCADA Overlays w/ Business & Industrial Networks Generation & Resiliency Assets EHS / Regulatory ©Emergent Energy Solutions 2024
  • 16. Process SCADA Infrastructure Automation Systems OEE / Downtime Operational / Maintenance / Capital Budgeting ERPS / CMMS Energy Barriers - Manufacturing Data Silos  Data is collected in manufacturing environments (sensors, IoT devices, etc.)  Silo’ed based on department and organizational hierarchy  Data is not organized for the customer by the technology partner  Lack of Energy Metrics as energy has been an accounting function (budgeting / forecasting) when invoices are received  Poor information transfer from attrition and changing roles  Increasing cost pressures and competition ©Emergent Energy Solutions 2024
  • 17. Process Map and Capabilities for Energy Intelligence ©Emergent Energy Solutions 2024
  • 18. 01 02 03 04 05 Sustainability Journey Commitments Scope 1, 2, 3, Water Reduction based on SBTi DEI Recyclability / Materials Success Mapping Data Shortfall Realized Project Development ESCO / Consultant Benchmark Commitment vs. Reality Savings Validation Engagement Commitment adoption Site Leadership Ownership Funding Data Expansion Capital / Green Bond ©Emergent Energy Solutions 2024
  • 19. Knowledge Transfer Prioritize Metrics Knowledge Transfer Continuous Engagement First step Stakeholder Engagement We engage with you to understand your business needs, energy requirements, challenges to ensure our solution solves a problem. KPI’s & Core Data Prioritization We conduct a review of usable assets, desired metrics, gather your power data and model the commercial benefits. Business Case We lay out our analysis of optimizing your assets and share the results. Engage & Design We engage our engineering resources to verify and design the solution at your site. Installation We work with you to design and install your solution, in line with your energy requirements and flexibility goals. Train You run your business as per normal, all managed through your energy management platform. Sustain Sustainability We automatically manage your assets to optimize your load without impacting your business. 2 3 4 Contracts We draft a commercial contract and present to you for signature. 5 6 7 8 1 Step 1 - Continuous Engagement to Mitigate silos ©Emergent Energy Solutions 2024
  • 20. Nat. Gas Water Electric Comp Air Steam ChWat N2 HtWat Step 2 - Technology Roadmap API / EXPORT / IMPORT
  • 21. Step 3- Access for all • Can be accessed globally rather than restricted to a site • Expand user base – Increasing data engagement by all levels (Right sizing data for the individual) • Browser based • Intelligence driven dashboards, identifying key metrics • Export / Import capabilities ©Emergent Energy Solutions 2024
  • 22. Customized Visualization & Report Energy Flow ©Emergent Energy Solutions 2024
  • 23. Visualize & Report Time of Use ©Emergent Energy Solutions 2024
  • 24. Energy Flow – Where and How Energy is Used ©Emergent Energy Solutions 2024
  • 25. Automated Fault Detection • AFDD needs energy metrics  Data Driven Event Triggers  Energy Thresholds  Custom Reporting ©Emergent Energy Solutions 2024
  • 26. • Trend Analysis: Observing how energy consumption or production trends over time, which can help in identifying patterns or anomalies. • Comparative Analysis: Comparing energy usage across different periods, locations, or systems to identify areas of high consumption or efficiency. • Load Forecasting: Using historical data to predict future energy demand. This is particularly useful for energy management and planning. • Cost Analysis: Calculating the cost implications of energy usage, considering different tariffs and operational hours. • Carbon Footprint Analysis: Estimating the environmental impact of energy consumption, particularly in terms of carbon dioxide emissions. • Efficiency Analysis: Identifying opportunities for energy savings by analyzing energy usage efficiency of systems or processes. • Anomaly Detection: Identifying unexpected spikes or drops in energy consumption that may indicate equipment issues or other problems. • Renewable Energy Analysis: Analyzing data related to renewable energy sources, like solar or wind, to assess performance and reliability. Data Analysis Needs ©Emergent Energy Solutions 2024
  • 27. Utility Bill Parity Progress against targets Resource Allocation REC / Offsets Reporting & Disclosure Reduction Goals VP Sustainability Budget Planning Performance against budget Energy Forecasting Goal Tracking Plant Manager Annual Project Planning Efficiency Gains ROI Carbon Impact Utility Rebates Demand Response Capital Team Engineering •Daily / Weekly Performance •Production Line •Centrical Plant •Shift Meetings •Energy Outliers •Equipment Cycling •Dashboard Maintenance & Operations Step 4 - Right Sizing Data - Customization for the User • Customized to the User. • Each team leader has a different priority and customization. ©Emergent Energy Solutions 2024
  • 28. Case Study #1 – Product Level Carbon Assignment Electric Nat Gas Compressed Air Steam Water Total Energy • Impact – All levels • Product or Production Line Level Energy / Carbon Allocation • True Cost of Energy / Unit Produced ©Emergent Energy Solutions 2024
  • 29. Case Study #2 – Down day vs. Full Production Energy Demand • Impact – • Maintenance • Sanitation • Down day Efficiency • Opportunity • Baseline Site Water Use ©Emergent Energy Solutions 2024
  • 30. Case Study #2 – Down day vs. Full Production Energy Demand • Impact • Maintenance • Engineering • Plant Management • Sustainability • Down day efficiency • Baseline non-volume specific energy load • Per Unit Impact Calculations ©Emergent Energy Solutions 2024
  • 31. Case Study #3 - Predictive Maintenance Facility maintenance is a large and unpredictable budget item for commercial and industrial buildings. A few specific examples of spotting energy waste • VFD equipped pumps and fans not modulating • Makeup Air Units running after hours • Systems running out of sequence • Utilities not correlating with production demand • Exterior lighting on during daytime hours  Real-time electrical energy data reveals insights into the health of mechanical equipment  Amp data provides insights on run-time, loading, cycling, and other elements that indicate machine health  Track equipment as individual assets within the system, which creates an electrical fingerprint and maintain a history of the asset  Relative to specialized technologies, metering systems are easy-to-implement, scalable across many asset types bringing accessibility to predictive maintenance ©Emergent Energy Solutions 2024
  • 32. Case study #4 – Solar Inverter Output Benchmarking  Benchmark Inverter Output  Notifications of Output shortfalls  Set output thresholds for individual panels to identify cleaning needs ©Emergent Energy Solutions 2024
  • 33. Data-Driven Project Development w/ Continuous Engagement Development of Capital projects with Sustainability Impact: • Estimated savings based on assumptions • Rules of thumb • Use estimates provided by vendor to justify projects • Lack of true system run hours Once implemented: • Assume the project worked and is yielding the expecting savings • If energy consumption remains high, assume something else has changed • Limited follow-up with the vendor or internal team to discuss performance Development of Capital projects with Sustainability Impact: • Calculations based on run hours and current energy use • Pursue specific projects based on net data driven value • Use real data on specific pieces of equipment to have confidence in potential impact • Only pursue targeted projects with high-confidence Once implemented: • Measure that the project is working as expected, and make sure it stays that way • Track changes against your baseline • Use data to refine performance • Data drives dialog and continuous commissioning ©Emergent Energy Solutions 2024
  • 34. Data Driven Financial Incentives Energy Markets (without data) • Demand Response - Performance is based on guessing game • Dynamic Market Pricing – Limited data on curtailable resources • Generation Assets – Limited visibility on performance, time of use and utilization Utility Incentives - Process (Rate of Production) and Infrastructure Enhancing projects – Lack of data, Custom Incentives are not pursued. Grants – Lack of data for Grant Applications, reinforcing net value Project Development - Increased cost for project development by ESCOs and consultants Energy Markets (with data) • Demand Response – Circuit level monitoring allowing for improved contract performance and expansions • Dynamic Market Pricing – Full visibility into assets and permitting participation in energy markets • Generation Assets – Visibility into performance, time of use and utilization. Generate or Grid Energy depending on best financials Utility Incentives - Process (Rate of Production) and Infrastructure Enhancing projects – Data drives Custom Incentive. Incentives are typically capped at 1 year payback Grants – Data driven grant applications, improving chances of success Project Development – Reduce project development time and cost ©Emergent Energy Solutions 2024
  • 35. Future Trends and Innovations • Actionable Reports • ECM M&V • ASHRAE Energy Audits • Analytics • FDD • Building & Process Science • Automated Data Interpretation • Expansion of Devices • Benchmarking Data • Carbon Reporting • Strategic Energy Planning • SKU Level Carbon Tracking Decarbonization Data Availability Data Interpretation Innovation & AI ©Emergent Energy Solutions 2024
  • 36. Cost Reduction and Operational Efficiency Competitive Advantage Environmental Responsibility Regulatory Compliance Predictive Maintenance Data-Driven Decision Making Resource Allocation and Budget Planning Resilience and Risk Mitigation Conclusion ©Emergent Energy Solutions 2024
  • 37. Call to Action: Data Driven Energy Optimization Energy Metrics with maintenance / engineering / accounting for operational excellence. Perform deep dives using data to drive discussions. Migrate to low-carbon and renewable energy sources while leveraging time of use energy pricing. Leverage data to evaluate financial impact. Continuously monitor performance and identify opportunities for reductions through KPI benchmarking. Verify impact from measures taken Improve site resilience with a deeper understanding of your energy operations at all levels of your organization. Leverage data to enhance machine efficiency, optimize production processes and reducing energy waste by developing baseline production and non-production Energy Use Intensity (EUI) Reduce budgeting risk exposure by forecasting energy demand and cost based on EUI and multi-year benchmarking $ ©Emergent Energy Solutions 2024

Editor's Notes

  1. Intro – Emergent Energy
  2. Why focusing on the industrial sector is key to an overall US Strategy to achieving Net Zero Emissions
  3. Significant growth is forecasted from the onshoring of manufacturing, infrastructure, rising global demand for tech, increasing investments in automation, AI and sustainability.
  4. Common reasons energy is wasted: Large mechanical equipment runs when it shouldn’t Sustaining manufacturing conditions requires continuous energy use BAS / BMS systems fall out of commission, causing energy waste (also known as “Energy Drift”) Worst of all, this energy and water waste is and persists for years as limited granularity is available.
  5. Start off by speaking to the extensive process and infrastructure systems that are needed to facilitate manufacturing success. These systems continue to grow in complexity.
  6. Technical challenges in data integration and analysis. Site based systems Maintenance Windows based Software & Hardware obsolescence Tied to Automation System’s limitations Poor Transition Planning due to limited adoption, use and training
  7. Single Platfrom
  8. Energy Optimization refers to the strategic process of reducing energy use through more efficient practices, technology, and operations without compromising productivity or performance. In manufacturing, this can involve enhancing machine efficiency, optimizing production processes, and reducing energy wastage. Decarbonization - is the process of reducing carbon dioxide emissions associated with energy sources in activities, particularly through the use of low-carbon power sources and increasing the energy efficiency. In the context of manufacturing, decarbonization involves adopting renewable energy sources, improving energy efficiency, and implementing technologies that reduce carbon intensity in production processes. Data-Driven Approaches - in manufacturing refer to the use of data analytics, machine learning algorithms, and other computational methods to inform decision-making processes. This includes collecting and analyzing large sets of data from various sources (such as sensors and IoT devices) to optimize operations, predict maintenance needs, and enhance energy management strategies. These approaches enable more precise control over manufacturing processes, leading to reduced energy consumption and emissions.
  9. Energy Optimization refers to the strategic process of reducing energy use through more efficient practices, technology, and operations without compromising productivity or performance. In manufacturing, this can involve enhancing machine efficiency, optimizing production processes, and reducing energy wastage. Decarbonization - is the process of reducing carbon dioxide emissions associated with energy sources in activities, particularly through the use of low-carbon power sources and increasing the energy efficiency. In the context of manufacturing, decarbonization involves adopting renewable energy sources, improving energy efficiency, and implementing technologies that reduce carbon intensity in production processes. Data-Driven Approaches - in manufacturing refer to the use of data analytics, machine learning algorithms, and other computational methods to inform decision-making processes. This includes collecting and analyzing large sets of data from various sources (such as sensors and IoT devices) to optimize operations, predict maintenance needs, and enhance energy management strategies. These approaches enable more precise control over manufacturing processes, leading to reduced energy consumption and emissions.
  10. Emerging technologies in AI and data analytics for energy management. Innovative practices for sustainable manufacturing. Predictions for future developments in manufacturing decarbonization.
  11. Continuous commissioning – Track performance down to the system and equipment level. Ensure existing systems and savings projects are generating the planned savings. Prevent Machine Failures – Real time electrical data reveals the true health of important assets. Identify issues by leveraging threshold alerts on key systems. Strategic Roadmap – Eliminate guesswork on projects and stop relying on vendor assumptions. Leverage real data from equipment and gain visibility into ROI.
  12. Energy Optimization refers to the strategic process of reducing energy use through more efficient practices, technology, and operations without compromising productivity or performance. In manufacturing, this can involve enhancing machine efficiency, optimizing production processes, and reducing energy wastage. Decarbonization - is the process of reducing carbon dioxide emissions associated with energy sources in activities, particularly through the use of low-carbon power sources and increasing the energy efficiency. In the context of manufacturing, decarbonization involves adopting renewable energy sources, improving energy efficiency, and implementing technologies that reduce carbon intensity in production processes. Data-Driven Approaches - in manufacturing refer to the use of data analytics, machine learning algorithms, and other computational methods to inform decision-making processes. This includes collecting and analyzing large sets of data from various sources (such as sensors and IoT devices) to optimize operations, predict maintenance needs, and enhance energy management strategies. These approaches enable more precise control over manufacturing processes, leading to reduced energy consumption and emissions.