New and Emerging Technologies - WordPress.com · 2017-12-04 · 1.6 Promote Energy Security Through Net Zero Initiatives at Critical Facilities ... Champion New and Emerging Technologies

Leadership, Energy, and Execution 1UNCLASSIFIED 7 NOV 2017

New and Emerging

Technologies

Mission Resilience & Sustainability Training

Bill Chvala, Anne Wagner, Ben Ford, Emily Wendel Pacific Northwest National Laboratory


Speakers

Bill Chvala, Jr., CEM• Pacific Northwest National Laboratory• Sr. Research Engineer• [email protected] 509-373-4558

Anne Wagner, CEM• Pacific Northwest National Laboratory• Sr. Research Engineer• [email protected] 503-417-7569

Ben Ford• Pacific Northwest National Laboratory• Research Scientist• [email protected] 206-528-3212

mailto:[email protected]




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.


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.


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


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.


New Technology Development Process

Technology Readiness Level (TRL) show path from concept, design,

testing and deployment:

EPRI 2011 (Freeman and Bhown)

10 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.


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


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?


Demonstration Projects

• Demonstration Project Process

• Federal Demonstration Programs

• Lessons Learned

– Forrestal Project

– Other Projects

• Current Emerging Technologies

• Resources


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


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


2017 ILC Army Reserve: 88th

DIV (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


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


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


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


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


More Lessons

• Be proactive:

– Consider possible problems and solutions

• Continuous activities:

– Project Management

– Quality management

– Communication


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


• 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


Troffer Best Practice Resource

Decision tree for determining the best LED trofferoption for your facility


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

• PNNL Lab Homeshttp://labhomes.pnnl.gov/news.stm

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

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

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

https://interiorlightingcampaign.org/

http://www.leepcampaign.org/

http://www.advancedrtu.org/

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

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


Machine Learning for USAR

Buildings Data


Presented By:Ben Ford– PNNL


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


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


What is Machine Learning?

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

ML process

Train dataModel

f(θ)Opt

Update model

How well do model predictions match actual outcomes?

Data input Model specification Optimization

Test dataBest

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


Machine Learning: Industry Use Cases

Recommender Engines Sentiment Analysis

Voice Recognition Anomaly Detection


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,

occupancy)• Operations and maintenance tickets (e.g. CSS)

• Type, date, cost, frequency

Count of Meters by RSC and Type

• Others?


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


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 days3. Heating Analysis

• Boiler appears to be running continuously4. Energy and Cost Savings Estimation

• Comparison of actual performance to “ideal” setbacks5. Meter reporting status and data quality review

0

2

4

6

8

10

12

0 50 100 150

Ele

ctri

city

Use

(kW

h)

OAT (°F)

Actual

Modeled


Quarterly Status Report Example


Machine Learning for Buildings Data

𝒚 = 𝜶𝟏𝒙𝟏 + 𝜶𝟐𝒙𝟐

𝑦 = 𝑓(𝑥1, 𝑥2)

Consumption modeling

Consumption forecasting

Spatio-temporal contextual profiling

Clustering

ConsumptionGas, electricity, water

Enterprise level dataContext data and meta-data

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

“Big data” analytics


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


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?


Thank you for your time!


Bill [email protected]

509-373-4558

Anne [email protected]

503-417-7569

Ben [email protected]

206-528-3212

Emily Wendel [email protected]

206-528-3011





Documents

New and Emerging Technologies - WordPress.com · 2017-12-04 · 1.6 Promote Energy Security Through Net Zero Initiatives at Critical Facilities ... Champion New and Emerging Technologies