Technologies Bill Chvala, Anne Wagner, Ben Ford, Emily Wendel - - PowerPoint PPT Presentation

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Technologies Bill Chvala, Anne Wagner, Ben Ford, Emily Wendel - - PowerPoint PPT Presentation

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Mission Resilience & Sustainability Training New and Emerging Technologies Bill Chvala, Anne Wagner, Ben Ford, Emily Wendel Pacific Northwest National Laboratory UNCLASSIFIED Leadership, Energy, and Execution 7 NOV 2017 1 Speakers Bill

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Leadership, Energy, and Execution 1

UNCLASSIFIED

7 NOV 2017

New and Emerging Technologies

Mission Resilience & Sustainability Training Bill Chvala, Anne Wagner, Ben Ford, Emily Wendel Pacific Northwest National Laboratory

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Leadership, Energy, and Execution 2

UNCLASSIFIED

7 NOV 2017

Speakers

Bill Chvala, Jr., CEM

  • Pacific Northwest National Laboratory
  • Sr. Research Engineer
  • William.chvala@pnnl.gov 509-373-4558

Anne Wagner, CEM

  • Pacific Northwest National Laboratory
  • Sr. Research Engineer
  • Anne.wagner@pnnl.gov 503-417-7569

Ben Ford

  • Pacific Northwest National Laboratory
  • Research Scientist
  • Benjamin.ford@pnnl.gov 206-528-3212
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Leadership, Energy, and Execution 3

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Learning Objectives

  • Objective 1: Understand how the Army Reserve is working to

promote application of advanced technologies.

  • Objective 2: Understand what makes a successful

demonstration and how to leverage other resources.

  • Objective 3: Discuss how a proposed machine learning

demonstration can utilize our MDMS and EBCS data to teach us about our buildings.

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Leadership, Energy, and Execution 4

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Army Reserve FY18-21 Energy Execution Plan

ESG #5: Maintain and Innovate a Sustainable Energy and Water Program Foundation ESG #2: Promote Energy Conservation ESG #3: Increase Energy Efficiency ESG #4: Leverage Renewable & Alternative (R&A) Energy

5.1 Develop Energy and Water Program Investment Strategy 5.2 Man the Energy and Water Program 5.3 Collect, Validate, and Analyze Data 5.4 Develop and Implement Installation Energy and Water Plan (IEWP) 5.5 Enhance the AR Sustainability Plan 5.6 Provide Energy and Water Training Opportunities and Resources 2.1 Sustain and Improve Building Energy Monitor (BEM) Program 2.2 Communicate AR Conservation Efforts 3.1 Champion New Designs / Construction 3.2 Ensure Efficient Building Operations 3.3 Upgrade Existing Buildings 3.4 Optimize Decommissioning Process 4.1 Track R&A Energy 4.2 Develop Comprehensive Portfolio of R&A Opportunities 4.3 Implement R&A Projects 4.4 Promote New and Emerging Technologies

Readiness

ESG #1: Improve Energy Resilience

1.1 Develop Energy and Water Security Plans 1.2 Create Energy and Water Security Metrics for RSCs 1.3 Track Energy and Water Security Metrics (ISR-MC, others) 1.4 Achieve and adequate energy supply for critical facilities (14 days) 1.5 Inventory Existing Facility Related Control Systems 1.6 Promote Energy Security Through Net Zero Initiatives at Critical Facilities

Recent update (9/21/2017) added new focus area for New and Emerging Technologies.

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Leadership, Energy, and Execution 5

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Army Reserve FY18-21 Energy Execution Plan ESG 4.4. Promote New and Emerging Technologies

  • Description: Champion New and Emerging Technologies
  • End-State: Provide factual performance data and application notes

to encourage adoption of technologies

  • Challenges: Pilots and demonstrations require funding. Long lead

time on some of these.

  • Roadmap:

– Develop list of “emerging technologies“ and track product life-cycle – Highlight 1 new technology on Monthly EM Calls – Develop communications plan.

  • Produce "Technology Fact Sheet" for technologies not in full scale demo
  • Develop deployment strategy for validated technologies

– Promote programs that provide support for new technologies – Fund Army Reserve-specific pilots and demos:

  • Phase Change Material (PCM) demonstration
  • Combined Heat and Power (CHP) demonstration
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Leadership, Energy, and Execution 6

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New Technology Development Process

Technology Readiness Level (TRL) show path from concept, design, testing and deployment:

EPRI 2011 (Freeman and Bhown)

“Real World” demonstrations are important to test new technologies in an uncontrolled, dynamic environment.

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Leadership, Energy, and Execution 7

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New Technology Development Process

Technology Readiness Level (TRL) show path from concept, design, testing and deployment:

EPRI 2011 (Freeman and Bhown)

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Widespread deployment in the Federal Sector

The Federal sector can be slow to deploy commercial technologies. This is another reason to demonstrate technologies at actual Federal facilities.

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Leadership, Energy, and Execution 8

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Different Levels of Technology Evaluation

Definitions for our purposes:

  • Measurement & Verification (M&V):

– Focus on documenting reduced energy use before/after retrofit – Collect enough extra information to normalize energy data – Primarily associated with ESPC/UESC projects.

  • Pilot Project

– Installing equipment at an initial location to validate performance with hopes to install on a larger scale. – Generally, data collected is greater than typical M&V but not as detailed as a full-scale demonstration.

  • Demonstration

– Similar to a pilot but adds additional data points to not just understand how something performs, but why . – Key outcome is ability to generalize how the device will perform in other locations, situations,

  • etc. and the estimated economics in those situations.

– For our purposes this is NOT a prototype or proof-of-concept (TRL 7)

  • Testbed

– A “testbed” is a location established with ample metering, data loggers, and/or test equipment that will be used to conduct multiple demonstrations of technologies or multiple scenarios for a given technology

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Leadership, Energy, and Execution 9

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Active Technology Evaluations

  • Phase Change Materials (PCM)

– 63d RSC, Sacramento California

  • Combined Heat and Power (CHP)

– Planned for FY18, location TBD

  • Waste-to-Energy (WTE): ESTCP/SERDP

– Fort Hunter Liggett

  • Rainwater Harvesting

– Fort Buchanan, PR (with solar ITTP) – 63rd RSC, Grand Prairie, Texas – 81st RSC, Savannah, Georgia

  • CEC microgrid grant (co-funding)

– Submitted FY18, Camp Parks – Submitted FY18, 63rd RSC

  • Deep data dive / machine learning on EBCS/MDMS data

– Proposed, TBD?

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Demonstration Projects

  • Demonstration Project Process
  • Federal Demonstration Programs
  • Lessons Learned

– Forrestal Project – Other Projects

  • Current Emerging Technologies
  • Resources
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Demonstration Project Process

Establish the objective of the project

Design Plan Implement Execute Analyze

  • Technology selection
  • Site selection
  • Overall project plan
  • M & V plan
  • Safety
  • Installation
  • Start up
  • Commissioning
  • Manage operation
  • Collect data
  • Evaluate data
  • Report Findings
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Federal Demonstrations/Pilots

  • GSA Green Proving Grounds

– Building Envelope – Energy Management – HVAC – Lighting – On-Site Power & Renewables – Water

  • DOE Forrestal Building
  • Lab homes
  • DOE “Campaigns” – collect broad

project information and recognize best practices

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2017 17 IL ILC Army y Res eser erve ve: : 88th

th DIV

IV (R (R) )

  • 19,900 kWh saved annually
  • 63% energy reduction

compared to existing usage

Medium Project - IL002 Large Project – MI029

  • 230,800 kWh saved annually
  • 72% energy reduction

compared to existing usage

Recognition: Highest Percentage of annual Savings for Troffer Retrofits

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Forrestal Relighting TLEDs

  • FEMP ENABLE ESPC
  • 2.3 million kWh saved
  • 47% energy savings
  • Completed in less than 1 year
  • Utilizes UL Type A TLEDs

– Operate off existing fluorescent ballasts – Needed an MOU with GSA

14

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Forrestal Field Findings

  • Do your homework:

– select viable technology – determine acceptable products/manufacturers

  • Buy American Act - product compliance challenging
  • Release clear and detailed RFQ/NOO

– Specific M&V requirements and protocols essential

  • Beneficial to complete within one fiscal year
  • Field hurdles – incompatibility with existing equipment or

systems

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Lessons Learned from other PCM Projects

  • PCM melting point temperature selection process

– Utilize engineering calculations & data logging for proper PCM selection – More than one melting temperature is usually necessary

  • Control heating and cooling setpoints & scheduling

– Allowed occupants to change setpoints & scheduling

  • Effects - increases energy usage
  • Establish proper test procedures with M&V

– Inconsistent results based on utilizing utility meter data and not monitoring HVAC equipment

  • Collect longer periods of baseline and post data

– Data analysis is inconclusive with regression analysis when periods are too short

  • Complex HVAC systems create a challenge to monitor

– The simpler the HVAC systems (single zone RTU) the easier the monitoring – Central plant HVAC systems and the effects of ancillary equipment (chillers and boilers) are challenging

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Other Lessons Learned

  • Strive for simplicity

– Equipment/system isolation potential – Personnel needed:

  • Internal staff – onsite and offsite
  • External personnel – contractors, subcontractors
  • Project champions
  • Proper preparation is essential

– Technology selection – Thoroughly understand technology – Verify savings estimates are accurate, reasonable and realistic – Choose the right building – Confer with site about their operations – Good baseline is critical

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More Lessons

  • Be proactive:

– Consider possible problems and solutions

  • Continuous activities:

– Project Management – Quality management – Communication

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Technologies to Watch

  • Connected thermostats
  • Variable speed pumping
  • Heat pump hot water heaters
  • Solid State Lighting development:

– Tunability – Connected lighting (network connectivity)

  • Windows:

– Low E storm – Cellular shades

PA163 Bellefonte

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  • Advanced Lighting Systems Training

– 3-part self-paced series

  • Campaign Resources

– Performance Specifications – Recognition Case Studies

  • Other key federal resources

– GSA Green Proving Ground demo reports – DOE SSL studies – FEMP-Designated guidance

  • Wireless Occupancy Sensors Application

Guide (FEMP)

  • LED Troffer Retrofit Best Practice

Current Resources

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Troffer Best Practice Resource

Decision tree for determining the best LED troffer

  • ption for your facility
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Resource Links

  • Environmental Security Technology Certification Program (ESTCP)

https://www.serdp-estcp.org/About-SERDP-and-ESTCP/About-ESTCP

  • General Services Administration Green Proving Ground Program

https://www.gsa.gov/governmentwide-initiatives/sustainability/gpg-program

  • Department of Energy’s Solid-State Lighting Solutions

https://energy.gov/eere/femp/solid-state-lighting-solutions

  • Interior Lighting Campaign (ILC)

https://interiorlightingcampaign.org/

  • Lighting Energy Efficiency in Parking Campaign (LEEP)

http://www.leepcampaign.org/

  • Advanced RTU Campaign (ARC)

http://www.advancedrtu.org/

  • Smart Energy Analytics Campaign

https://smart-energy-analytics.org/

  • PNNL Lab Homes

http://labhomes.pnnl.gov/news.stm

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Machine Learning for USAR Buildings Data

Mission Resilience & Sustainability Training Presented By: Ben Ford– PNNL

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Outline

  • What is big data?
  • What is machine learning?
  • Industry use cases
  • Utility meter data analysis overview
  • Current process and issue reporting
  • Machine learning for USAR buildings data
  • Key takeaways
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What is Big Data?

Big Data is large-scale data generated and captured continuously through a distributed network of manual and automated processes, characterized by the “Three V’s”. Big data originated with business. Example sources:

  • Transactional data
  • Customer accounts
  • Inventory management
  • Process trending

Volume Velocity Variety

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What is Machine Learning?

Definition: class of computer algorithms that improve their performance on a task through iterative refinement. ML process Train data

Model

f(θ) Opt

Update model How well do model predictions match actual

  • utcomes?

Data input Model specification Optimization Test data

Best Model

f’(θ) Model Output What kinds of tasks?

  • Regression: predict a quantity, e.g. home value from SF, zip code, # bathrooms
  • Classification: predict a category, e.g. determine if an e-mail is spam from

metadata and e-mail text

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Machine Learning: Industry Use Cases

Recommender Engines Sentiment Analysis Voice Recognition Anomaly Detection

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Sources of USAR Buildings Data

  • Utility meter interval data
  • Meter Data Management System (MDMS)
  • Building control system data (e.g. EBCS)
  • Sensors (temperature, pressure, status), alarms, run-time, set-points,

heating/cooling mode status

  • Dozens of equipment types (e.g. Boilers, Chillers, AHUs, VAVs, lighting,
  • ccupancy)
  • Operations and maintenance tickets (e.g. CSS)
  • Type, date, cost, frequency

Count of Meters by RSC and Type

  • Others?
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Current Utility Meter Data Analysis Process

Evaluate

  • Benchmark comparable buildings and identify operational problems,

including meter connection, failure to use setbacks, early start-up, high baseload, and potential equipment controls issues

Prioritize

  • Identify high priority facilities and actions for Installations or Regions

based on energy, water and cost savings from corrective actions

Engage

  • Provide regular Status Reports to facilitate energy manager action with

limited, targeted information on meter operational status and building performance metrics

  • Promote accountability by tasking and/or informally requesting

resolution/justification and comparing performance to previous periods

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Current Issue Identification using MDMS

  • 1. Hourly Load Profile Analysis
  • Building exhibits early start
  • Building exhibits late shutoff
  • High baseload during night hours
  • 2. Cooling Analysis
  • No OAT chiller lockout is in place
  • Chiller appears to be running when temperature is <60F
  • Temperature setbacks for unoccupied hours are not evident
  • Cooling temperature setbacks are in place, but can be more aggressive
  • Building has high electricity use in summer months relative to cooling

degree days

  • 3. Heating Analysis
  • Boiler appears to be running continuously
  • 4. Energy and Cost Savings Estimation
  • Comparison of actual performance to “ideal” setbacks
  • 5. Meter reporting status and data quality review

2 4 6 8 10 12 50 100 150 Electricity Use (kWh) OAT (°F) Actual Modeled

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Quarterly Status Report Example

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Machine Learning for Buildings Data

𝒛 = 𝜷𝟐𝒚𝟐 + 𝜷𝟑𝒚𝟑 𝑧 = 𝑔(𝑦1, 𝑦2)

Consumption modeling Consumption forecasting Spatio-temporal contextual profiling Clustering

Consumption

Gas, electricity, water

Enterprise level data Context data and meta-data

Context

Footprint, time of day, day of week, weather, location, etc.

“Big data” analytics

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ML Economies of Scale

Cost/ building Scale (number of buildings) Capability Level of Effort

  • Diminishing cost per building as ML models

scale from demonstration to enterprise- level

  • Successive models build on previous ones

to deliver more advanced capabilities

  • Greatest value from models that combines

energy, control systems, and maintenance data

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Key Takeaways

  • Machine Learning can yield automated insights,

with minimal human-in-the-loop requirements

  • Potential Machine Learning Applications for

USAR

  • Consumption forecasting and demand

response

  • Fault detection and diagnosis
  • Predictive maintenance
  • Schedule optimization
  • Your ideas?
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Thank you for your time!

Mission Resilience & Sustainability Training Bill Chvala William.chvala@pnnl.gov 509-373-4558 Anne Wagner Anne.wagner@pnnl.gov 503-417-7569 Ben Ford Benjamin.ford@pnnl.gov 206-528-3212 Emily Wendel Emily.wendel@pnnl.gov 206-528-3011