ibm smarter energy management systems for intelligent buildings june 10, 2009 dr. jane l

© 2007 IBM Corporation

IBM Research

© 2009 IBM Corporation

IBM Smarter Energy Management Systems for Intelligent Buildings

June 10, 2009

Dr. Jane L. Snowdon

IBM T. J. Watson Research Ctr

[email protected]

IBM Research

© 2009 IBM Corporation2

IBM Research Strategic Goals for Energy

• Smart Grid

• Smart Buildings including Data Centers

• Smart Energy

IBM Research


Vision: End-to-End System for Smarter Energy Management

Energy Management Software

Servers, Storage, Networks

Data Center or Office Personalization

Raised Floor

Office Space

Building Infrastructure Systems

HVAC, Lighting, Electrical, phones, etc.

Building Brick and Mortar

.

Power Grid

Cloud , Virtualized Computing

Industry Specific Solutions

Power Grid

Data Centers

OfficeBuildings

Intelligent Buildings

Today:Separate Systems

Tomorrow:Fully Integrated

Systems

IBM Research


Buildings represent one of the largest areas for energy efficiency gains – particularly in the US and other developed countries

Source: Canaccord Adams; IBM Corporate Market Insights Analysis, EPA Report to Congress, August 2, 2007

CO2 Emissions in the US

Transportation

33%

Buildings

39%

Industrial

28%

§ Buildings in developed countries contribute approximately 40% of the greenhouse gas (GHG) emissions that are forcing climate change

– GHG contributions surge to 50% when all the energy-related factors necessary to serve buildings and their occupants are included

– Over the next 25 years, CO2 emissions from buildings are projected to grow faster than those from any other sector

Energy and Resource Consumption for All Types of Buildings in the US

39% of US primary energy use (includes production-related fuel input)

Energy

136M tons building related debris/yrWaste

40% of raw materials globally (3B tons/yr)Materials

12% of all potable water (15T gallons/yr)Water

70% of all consumptionElectricity

Commercial and Data Center increasing dramatically

Data Centers in US consumed 62 billion kWh in 2006, about $4.5B or 1.5% of the total, doubling since 2000. At current trends, it will double again by 2011

IBM Research


IBM Shares Mutual Areas of Interest for Research with CITRIS andIBM Shares Mutual Areas of Interest for Research with CITRIS and HiPerBRICHiPerBRIC

6 © 2009 IBM Corporation

IBM Research


Today:

Multiple “proprietary“ systems

24/7 Monitoring

FIRE & SAFETY

ACCESS

ENERGY HVAC

ELEVATOR

SECURITY

LIGHT

COMMUNICATION

Separate Control Systems

Future:

“One Managed Network of Optimized Systems” for all services

24/7 Monitoring

FIRE & SAFETY

ACCESS

ENERGY HVAC

ELEVATOR

SECURITY

LIGHT

COMMUNICATION

Distributed, but Coordinated, Monitoring & Control

Intelligent Building Transformation

WATERWATER

IP TELEPHONY

IP / DIGITAL TV

HIGH SPEED INTERNET HIGH SPEED

INTERNET

IP TELEPHONY

IP / DIGITAL TV

IBM Research


The Intelligent Energy Efficiency Building Solution project proposes to enable real-time measurement, monitoring and management of building systems through the development of new algorithms, analytics of real-time external events and building system state, and implementation of control mechanisms to better optimize energy so consumption can be reduced.

High-Speed Internet

Backbone

Wireless

Te

na

nt

Se

rvic

es a

nd

Tech

no

log

ies

HVAC sensors

Fire

24/7 monitor

Video surveillance

Lighting

Energy

Elevators

Access

Bu

ildin

g S

erv

ices

an

d T

ech

no

log

ies

Digital / IP TV

Visitor management

Touch ScreenKiosk

Audio and video conferencing

IP Telephony

Real-time external events

-Weather forecast

-Energy prices

-Human location information,- e.g., People leaving or coming to work & home

-Utility

Analytics

-Demand Management System-Are any building systems out of spec?-Gas-Water-HVAC-Pumps-Chillers

Control Mechanisms- Automatically

control the equipment

IBM Research


Intelligent Building High Level Architecture

Data Center Systems• IT & Infrastructure Interfaces

• Threshold Control

Building Control Systems•HVAC, Lighting

• Fire & Security Systems• Utilities & Site Services

Systems Integration and Optimization• Data Warehouse

• On-Site and Remote Operations• Resource Optimization Rules

•Fault Isolation

Premises Management

External Systems Inputs

•Utility & ServiceProviders

•Weather Systems• Emergency Services

• City Controls

Physical Systems•Sensors / Meters

• Monitors•Actuators

•Natural Systems

Resource Management•Data Correlation

•Analytics•Systems Integration

•Resource Optimizations

Business Integration•Information Presentation

•Business Optimization• Property Management

Communications Layer Communication Interconnect & Protocol Translation

Function Mgmt •Information Presentation

• Process Control

•Dashboards• Business Rules

•Decision Support •Business Optimization

Services Mgmt• Presentation Services

• User Services• Recovery Services

• Asset Services

Process Mgmt• Process Automation

• Change Control

InformationControl

IBM Research


Integrated Management of Energy, Air, Water for Buildings

Mobile Measurement Technology (MMT) for Buildings

§Create a robotized version capable of understanding temperature variations and flow patterns in buildings

§Detect HVAC leaks and airborne contaminants

Monitor Airborne Contaminants

§Swap re-circulating air for outside air and vice-versa

§ Integrate with shut down controls for HVAC and access control systems

§Ensure safety and health of occupants and equipment – Risk/Reward Models

Scope: City > Campus > Building

Local Weather and Damage Prediction

§Develop damage prediction model to estimate number of outages

§ Incorporate GIS infrastructure data into weather and outage models

§Support for siting and operation of renewable generation capabilities

Framework, Architecture & Platform

§Develop a framework, extensible architecture, reference semantic model, and platform to support Intelligent Building operational needs

§Supports integration of sensors, actuators, and building sub-systems

Integrated Management

IBM Research


Energy Management Analytics

Energy Procurement Planning

§Select optimal supply of energy blocks (size, duration, time-of-use)

§Based on stochastic optimization over energy demand forecast and energy market prices (futures and spot)

Green Portfolio Planning

§Plan investments in green assets over a time-line

§Based on analysis of trends in demand, energy price, and technology maturity under uncertainty


Demand Response Elicitation

§Software platform for estimating demand shifting and shedding as a function of control signals (info, price)

§Statistical analysis of demand elasticity under various load attributes

Dynamic Demand Management

§Schedule discrete loads (water pumping, PHEV charging, energy storage charge/discharge)

§Generate control signals (info, price, caps) for optimal demand shaping

Energy Usage Model

IBM Software Group | Information Management Software

11© 2008 IBM Corporation

New! InfoSphere Streams analyzes extreme volumes of structured and unstructured information-in-motion

All statements regarding IBM's plans, directions, and intent are subject to change or withdrawal without notice. Any reliance on these Statements of Direction are at the relying party's sole risk and will not create any liability or obligation for IBM.

Statement of Direction IBM intends to make available in the first half of 2010, an offering, IBM InfoSphere Streams, which will help customers continuously analyze massive volumes of information at extreme speeds to improve business insight and decision making. This product will be based on an ongoing stream computing project in IBM's Research Division.

Based on IBM Stream Processing Research§ Stream processing systems research in

IBM’s Watson Labs

§ Developed in conjunction with large scale government initiative

§ Research project continues in parallel with productization

Key Features§ Parallel and high performance stream

processing software platform

§ Analysis of structured and unstructured in information

§ Scalable over a range of hardware

Customers already using Streams§ Early Access Program

§ Over 10 customers in 3 continents

Extreme Volumes

Extreme Analysis

Extreme Speed

•Stream Computing is an extreme evolution in data analytics

IBM Software Group | Information Management Software

12© 2008 IBM Corporation

Single Server Deployment

InfoSphere Streams is scalable on commodity hardware from singlenode to blade centers to high performance multi-rack clusters

Adapts to changes in resources, workload, data rates

For US equity electronic trading brokerage- processing 1.6M events / second

Optimizing scheduler continually manages resource allocation

Deployment on blade servers§ Cell Blades§ Intel Blades§ x Series§ …

Deployment on High Performance Cluster

IBM Blue Gene

millisecond latency

Big Green Innovations

© Copyright IBM Corporation 2007

13

Deep Thunder – Forecasts for Weather-Sensitive Operations§Problem: weather-sensitive business

operations are often reactive to short-term (3 to 72 hours), local conditions (city, county, state) due to unavailability of appropriate predicted data at this scale

– Energy, transportation, agriculture, insurance, broadcasting, sports, entertainment, tourism, construction, communications, emergency planning and security warnings

§Solution: application of reliable, affordable, weather models for predictive & proactive decision making & operational planning

– Numerical weather forecasts coupled to business processes

– Products and operations customized to business problems

– Competitive advantage -- efficiency, safety, security and economic & societal benefit



14

§Weather causes damage and outages

§Outages require restoration (resources)

§Restoration takes time, people, etc.

§Build stochastic model from weather observations, storm damage and related data

–Outage location, timing and response

–Wind, rain, lightning and duration

–Demographics of effected area

–Ancillary environmental conditions

§Can this model be coupled to a weather model to enable a forecast of impact and response?

Storm Impact and Response Prediction

Weather prediction

Damage prediction

Restoration time

prediction

Resource requirement prediction

Business Partner Transformation

© 2006 IBM CorporationIBM Confidential



15

Deep Thunder Damage Prediction -- 18 January 2006

Actual OutagesForecasted Outages

Business Partner Transformation

© 2006 IBM CorporationIBM Confidential



16

Uncertainty in Damage Prediction -- 18 January 2006

Forecasted Outages with Probability of 51 to 100 Outages

Forecasted Outages with Probability Exceeding 100 Outages

17


Thermal Analysis and Optimization

Mobile Measurement Technology (MMT)

hot air is sucked

into cold aisle

Optimize to eliminate hot spots, reduce energy consumption, and extend the life of the data center

Utilizing MMT

Hot spot at long aisle

13.0oC

54.5oC

33.7oC

@ 5.5 feet

Ø Unique cart-based design for measurement collectionØ Creates a High Resolution, 3D temperature map of the

data center – real measurements not CFD guessesØ Samples 100+ temperatures at one time and thousands

of temperatures in the data centerØ Monitors positionØ Surveys large areas in a short time

18


cold aisle

hot aisle z=0.5 feetz=1.5 feetz=2.5 feetz=3.5 feetz=4.5 feetz=5.5 feet

Hot spot at long aisle

z=6.5 feetz=7.5 feetz=8.5 feet

13.0oC

54.5oC

33.7oC

yz

x

- rapid survey capabilities using MMT (5000 square feet per hour)- post-processing and thermal models for DC optimization

Mobile Measurement Technology (MMT): Thermal Scans

Confidential © 2005 IBM Corporation19

2009 Energy and Environment Conference

Enhancement of MMT tool§ robotization of MMT cart for autonomic data collection

§ enhance MMT sensing capabilities

§ integrate laser scanners for 3D layout mapping

§ implement barcode & RFID scanning

(air flow, acoustics, other environmentals)

Robotize MMT and enhance sensor technologies –apply to buildings

Real-time sensor systems and instrumentation§ further develop wired sensing solutions (one-wire protocol)

§ develop appropriate (modular) sensor deployment strategies

§ develop means & techniques (RFID, barcode) for sensor management

§ wireless sensor systems (Zigbee etc..)

§ integration of MQTT-S messaging protocol

IBM Research


Integrated Management Energy ManagementControl of every resource with energy management software

IT Assets

SystemsStorage

Facility Infrastructure Assets

Data Center Infrastructure Assets

New IBM Servers

iPDU/External

Native

Active EnergyManager

Alerts View

Data Center Conditions

Systems Utilization

IBM Values:• Integrated Solution• Solution/Value Growth• Footprint Generation • Reduced Development

Alerts/Data/ActionsAlerts

Alerts/Data/Actions

Alerts

POC Box Data

Confidential © 2005 IBM Corporation21

2009 Energy and Environment Conference

Airborne Contaminants and MMT

§ Airborne contamination in data centers is an important contemporary problem

– Corrosion of silver to silver sulfide

– Galvanic attack of copper by silver over-plating leading to so-called “copper creep corrosion.”

– IBM Lab Services Contamination Filtration Offering Addresses these issues.

– Many chemicals and gases can be culprits in corrosion:

• Chlorine

• Sulfur Compounds, esp. Sulfur Dioxide

• Hydrogen Chloride, Hydrogen Peroxide

• Ozone

• Nitrogen Dioxide

• Ammonia

– These compounds are particularly prevalent in countries getting many new data centers

§ Same issues exist in more general indoor facilities such as manufacturing and storage facilities

– Possibly different airborne contaminants of concern in these facilities:

• Formaldehyde

• Mold

• Radon

• viral agents (e.g. hospitals)

• total particulates

• Others

IBM Research


Energy Management Analytics


§Select optimal supply of energy blocks (size, duration, time-of-use)

§Based on stochastic optimization over energy demand forecast and energy market prices (futures and spot)


§Plan investments in green assets over a time-line

§Based on analysis of trends in demand, energy price, and technology maturity under uncertainty



§Software platform for estimating demand shifting and shedding as a function of control signals (info, price)

§Statistical analysis of demand elasticity under various load attributes


§Schedule discrete loads (water pumping, PHEV charging, energy storage charge/discharge)

§Generate control signals (info, price, caps) for optimal demand shaping

Energy Usage Model

IBM Research


1. Energy Procurement Planning

12AM 9AM Noon 6PM 9PM 12AM

Hedge for a Summer Wednesday (say)

offpeakoffpeak

midpeak midpeak

PEAK

kW

§ Energy Hedge is an “Energy Block” of a particular size, covering a particular duration & time-of-use, at a known predetermined price

§Load over and above the “Hedge” is the exposure to the “spot market”

§ How do you decide what is the optimal Contracted Base Load? (Regulated Markets)

§ Similarly, what is your optimal portfolio of “Energy Blocks” ? (Deregulated Markets)

§Operationally handle peaks using demand shifting using price signals

IBM Research


Optimal Energy Procurement Under Risk§ We can address the Energy Procurement Planning problem as a Stochastic

Optimization question: What is the optimal hedge sizing for the fixed price purchase component?

§ Given (Input)

– A horizon of interest (say 1 month, 1 Quarter, Half-Year, Full-year, etc.)

– Above described Regime-switching Stochastic Models for Real Time Price, Day Ahead Price, and Load, pertinent to the above horizon

– Rate Structure Details

– Candidate set of Energy Hedge-Blocks, along with business constraints, such as Minimum Block Size, Minimum Duration of Purchase, etc.

§ Compute (Output)

– The optimal set of Hedge-Blocks, with size and duration of coverage

– Real-time & Day-Ahead exposure that is recommended with the above Hedge Solution

– Such that the Overall Energy Expenditure Distribution, has an acceptablerisk of exceeding a user-defined, known, threshold (tolerance level)

§ Use Stochastic Mathematical Programming techniques to solve this problem


IBM Research


2. Green Portfolio Planning


Energy Demand• Current energy load types

and consumption pattern• Demand growth trends

Current Infrastructure• For each major load types (HVAC, pumps, lighting, etc.)• Equipment age (remaining lifetime, salvage value)• Energy efficiency

Portfolio Analysis• Timeline for asset

investments• Capacity planning for

local power generation and energy storage

Green Asset Options• Energy efficient technologies• Energy load control systems• Local renewable power generation• Energy storage options• Asset costs and efficiency trends

Energy Supply• Energy cost trends

(peak, off-peak)• Demand response rebates

and retainers• Carbon caps/taxes

IBM Research


Energy-star appliances

Compact fluorescent light bulbs

Geothermal heat pump

Equipment tune-up

Building weatherization

Solar thermal water heating

Analysis of Energy Efficiency

§ Identify options for improving energy efficiency

§ For each option:

– Estimate cost of upgrade

– Estimate usage (peak/off-peak)

– Estimate energy savings (peak/off-peak)

– Calculate cost/KWhr saved (peak/off-peak)

§ Compare with peak and off-peak pricing of electricity

– Current and future variability

§ Select cost effective options

$C

ost

/KW

hr

Save

d

MWhr Saved/Year (cumulative)

Projected Energy Cost

Example: Building Energy Efficiency Options


IBM Research


Analysis of Energy Load Elasticity § Provides analytical support for

– daily peak load management

– demand response

– dynamic control systems

§ Identify loads that can be shed or shifted depending on:

– time of day

– type and level of incentives

§ Based on price elasticity models and discrete choice models

§ Model parameter extraction approach (assumes energy monitoring capability)

– Initial pilots and experiments

– Ongoing monitoring and data mining

Cooling reduction

Lighting Sweep

Reschedule pumps

Pre-cooling

Thermal Energy Storage

$C

ost

/KW

hr

Shift

ed

Peak KWhr Saved/Year (cumulative)

Cost Rebate

VFD Ventilation

Dimmable Ballasts

Example: Building Demand Response Options


IBM Research


3. Demand Response Elicitation

§ Demand Response Metrics:

– Price Elasticity: %increase in demand per %increase in price

– Elasticity of Substitution: %shift of demand per %price difference (peak vs. off-peak)

§ Past studies are at the macro-economic level

– Skewed distribution of elasticity suggests need for microeconomic study

§ Types of demand response signals to study:

– Price

– Information of energy use and carbon emissions (comparison to anonymous peers)

– Curtailment advisories during critical peak periods (with rebate on curtailment)

§ Granularity and stratification of the analysis:

– By type of pricing contract, time-of-day, type of load, end-user profile, weather

§ Benefits:

– Evaluate effectiveness alternative demand response policies

– Parameterize energy demand model for operational decision-making

IBM Research


Demand Conditioning based on Dynamic Price Controls

§ The GridWise Olympic Peninsula Project

– 112 households

– Thermostats programmed to response to price signals

– Customer can choose between ‘more comfort’ and ‘more economy’

– 3 options for pricing contract:

• Fixed price

• Time-of-use (with manually initiated critical peak price periods)

• Real-Time pricing (double auction)

Results

§ Real-Time Pricing group:

– Peak decreased 15-17% (pre-heating/cooling)

– Overall consumption 4% higher than fixed-price group

§ Time-of-Use group:

– -0.17 price elasticity

– Overall consumption 20% lower than fixed-price group


IBM Research


Software Platform for Demand Response Elicitation

Building Management System

BMS DB

Gateway

DRDB

Room nnn Room nnn Room nnn Room nnn Room nnn

Statistical Analysis

Room nnn

Web Services

Experiment Management• Set price (static or dynamic)• Set temp, light set points• Read energy use, set points• Create portal content

Demand Response Model• By time-of-day, resident type, contract, price,

ambient temperature

Supply constraints

§§ Light dimmers Light dimmers –– sense and controlsense and control

§§ Thermostat Thermostat –– sense and controlsense and control§§ Interface device Interface device –– information display, device control, preference settingsinformation display, device control, preference settings§§ Web Web –– information display, preference settingsinformation display, preference settingsB

MS

Ven

do

rIB

M S

oft

ware

Experiment Administration• Enrollment• Surveys• Contract Assignment• Student Management and

Support

Portal Server

(if web services do not exist)

Experiment Results• Demand elasticity by

contract type, time-of-day, end-use, resident type

Exp 1

Exp 2

Exp n


IBM Research


4. Dynamic Demand Management

Operational decision-making

–Demand management: determine signal sent to energy load components

• Demand response (episodic) during critical peak periods

• Continuous demand management in response to:

– Real-time pricing

– Carbon caps

– Variability of on-site power (and stored energy)

–Scheduling of discrete energy loads:

• Energy storage

• Water pumps

• PHEV charging

• Discrete loads (e.g., washers, roof-top HVAC units)

Energy inputsImport price

Local generationCarbon cap/tax

ObjectiveEnergy cost

Carbon emissionsOpportunity cost

SignalsPrice, Info,

Alerts, Caps

EnvironmentConstraints

Energy Usage Model

IBM Research


Example of Decision Support Scenario for Energy Management:Decision maker imposes price premiums based on predicted impact on peak demand

Peak demand reduction


IBM Research


Room/Zone Sense & Control

Interaction of analytics with building automation

Ethernet TCP/IP

Monitor

Network Manager/AdapterBuilding Automation Network

Programmable Controllers

Room/Zone Sense & Control


§HVAC: Ventilation/AHU/Chillers§Lighting§Water pumps

§Temperature§Humidity§Motion

ControlDB

Energy Use Model

Energy Optimization

Energy Use Prediction

Report

Model parametersCurrent state (building, environment)

Future energy demand

Usage/Outage Alerts

Equipment Failure Alerts

Demand Response Signals

Discrete Load Schedules

Ve

nd

or

Te

ch

no

log

ies

IBM

An

aly

tic

s

§On-site generation§Storage systems



Forecasting§Weather§Prices§Tech trends§Demand

Data Mining§Demand response§Statistical process control


IBM Research


WASHER 1

WASHER 2

WASHER 3

WASHER N-1

WASHER n

:

ENERGY

• Finally at an operational level (daily/weekly) how do we stay within the contracted load? (i.e. min spot purchases and control operational budgets)

• smart devices such as washing machines• given the desired time window (based on price signal) schedule washing

machines to level the load • This requires a higher level of individual commitment • Incentive alignment through pricing

• Can we rise to this challenge of energy management and bring discipline to our every day activities

Finally… Energy Reservation System?


IBM Research


On Smart Cities we can already deliver…Public Safety

- S3 Surveillance System- Emergency Management

Integration- Deep Thunder Micro-

Weather Forecasting

Intelligent Transportation Systems

- Integrated Fare Management

- Road Usage Charging- Traffic Information

Management

Energy Management- Network Monitoring &

Stability- Smart Grid – Demand

Management- Intelligent Building

Management

Water Management- Water purity monitoring- Water use optimization- Waste water treatment

optimization

Environmental Management- City-wide Measurements- Key Performance Indicators- Scorecards- Reporting

ibm smarter energy management systems for intelligent buildings june 10, 2009 dr. jane l

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