Leveraging Energy Data June 5, 2002 Creating Knowledge From Energy Data
Post on 12-Jan-2016
29 Views
Preview:
DESCRIPTION
Transcript
LeveragingEnergyData020605.ppt
PredictPower Proprietary and Confidential Information
1
Leveraging Energy DataJune 5, 2002
Creating Knowledge
From Energy Data
LeveragingEnergyData020605.ppt PredictPower Proprietary and Confidential Information 2
Agenda
Introduction to PredictPower PredictPower Solutions Value-add for PowerLogic Discussion Next Steps
LeveragingEnergyData020605.ppt PredictPower Proprietary and Confidential Information 3
PredictPower
Advanced technology company based in San Diego, CA
Founded in 1999 Leader in energy market forecasting and enterprise
energy load modeling. Designs and deploys enterprise energy solutions
Advises commercial and industrial customers, utilities, and energy traders on energy policy and specific energy programs
Provides system integration and project management services for energy projects
LeveragingEnergyData020605.ppt PredictPower Proprietary and Confidential Information 4
PredictPower Capabilities
Energy Planning and Information Center Modeling and forecasting Tailored and segmented forecasting Systems engineering Solutions integration Project management Energy consulting
7 56
121110
8 4
21
9 3Independent Meters and
Monitoring Devices
Energy ManagementInformation Systems
Equipment Specificationsand Operating Schedules Utility Invoice Data
text
Specialized Weather Data Sources
EnterpriseOrganization, Structure,
Business Rules, etc.
LaggardsLate
MajorityEarly
MajorityEarly
AdoptorsInnovators
"TheChasm"
Technology Adoption Process
Historical Analyses / Trends / Anomalies
Dat
aIn
form
atio
nK
now
ledg
e
$ $ $
Energy Load and DemandData Warehouse
PredictPower EnterpriseData Warehouse
PredictPower Historical WeatherData Warehouse
PredictPower Enterprise Data Repository
Enterprise EnergyForward-Looking Model
Energy MarketLoad and Price Forecasts
Market FundamentalsData Warehouse
Enterprise Energy Decision Model
Enterprise Energy Presentation Platforms(Alert Technologies / Business Intelligence Platforms)
InvoicesInvoices
Device Monitoring Network
Managed Data Feeds
Energy Market Fundamentals
!Notifications Operational
DecisionsPlanning
and Strategy
!MarketWarnings
LeveragingEnergyData020605.ppt PredictPower Proprietary and Confidential Information 6
{ }
We unite market and enterprise data to create knowledge to move forward
{Energy Market Information}
+ {Enterprise Energy Information}Unique Modeling Technology
= Forward-looking Enterprise Energy Profile
Knowledge that supports better decisions
Making energy a part of your business strategy
LeveragingEnergyData020605.ppt PredictPower Proprietary and Confidential Information 7
Solutions - Enterprise
Baseline
ROI
Energy BusinessIntelligence Platform
LeveragingEnergyData020605.ppt PredictPower Proprietary and Confidential Information 8
Energy Planning andInformation Center
Energy Business Intelligence platform Supports the six components
of ROI improvement Provides management dashboards with
different perspectives Manages both consumption and cost Manages energy as an enterprise resource Manages energy as a financial portfolio
LeveragingEnergyData020605.ppt PredictPower Proprietary and Confidential Information 9
Functionality for Management
Develop energy baselines Prepare and manage energy budgets Develop Key Performance Indicators (KPIs) Identify opportunities for improvement Establish performance objectives and
energy improvement initiatives Monitor energy improvement progress Provide visibility of energy information
LeveragingEnergyData020605.ppt PredictPower Proprietary and Confidential Information 10
Features
Performance objectives and monitoring Energy budgeting and budget management Enterprise consumption modeling and forecasting Climatological (weather / seasonal) normalization Determinant factor analyses (where feasible) Exception reporting and analysis Alerts / notifications Green light / red light metaphors Energy Information Dashboard
Incremental implementation
LeveragingEnergyData020605.ppt PredictPower Proprietary and Confidential Information 11
Key Performance Indicators
Enterprise Structure, Business Rules, etc.
Energy ManagementInformation Systems
Independent Meters andMonitoring Devices
InvoicesInvoices
Utility Invoice andConsumption Data
text
Weather and ExternalEnvironmental Data
7 56
121110
8 4
21
9 3
Equipment Specifications
and Operating Schedules
Market Fundamentals& Market Forecasts
Historical BaselinesIndustry StandardsComparative BenchmarksLike FacilitiesFacility AttributesProductivity Statistics (Quantity, Cost, etc.)Personnel Measures (Accident Rate / Turnover Rate, etc.)Distribution CostsAvailability / Reliability MeasuresMaintenance CostsOperating CostsCustomer Satisfaction Measures
Generally manage consumption, e.g.: KWhr per Square Foot KWhr per served customer KWhr per delivered product KWhr per $revenue KWhr per operating hour
Analyze Cost, e.g.: $ as percentage of budget $ as percentage of production cost Incremental cost to reduce maintenance cost Incremental cost to improve availability
LeveragingEnergyData020605.ppt PredictPower Proprietary and Confidential Information 12
Determinant Factors
Enterprise Structure, Business Rules, etc.
Energy ManagementInformation Systems
Independent Meters andMonitoring Devices
InvoicesInvoices
Utility Invoice andConsumption Data
text
Weather and ExternalEnvironmental Data
7 56
121110
8 4
21
9 3
Equipment Specifications
and Operating Schedules
Market Fundamentals& Market Forecasts
Baseline Factors
Climatological Factors
Environmental Factors
Operational Factors
Discretionary Factors
LeveragingEnergyData020605.ppt PredictPower Proprietary and Confidential Information 14
ROI, Aggregate Savings
Comparison, Managed and Unmanaged Electricity Cost
Date
Re
lati
ve C
um
ula
tive
Co
st
Unmanaged
Pentech-managed
LeveragingEnergyData020605.ppt PredictPower Proprietary and Confidential Information 15
ROI, Interval Savings
Hourly Cost, Managed and Unmanaged
Hour of Day
Re
lati
ve H
ou
rly
Co
st
Unmanaged
Pentech-managed
LeveragingEnergyData020605.ppt PredictPower Proprietary and Confidential Information 16
ROI, Reduced Demand
UnmanagedPentech-managed
S10
25
50
75
Current Demand
LeveragingEnergyData020605.ppt PredictPower Proprietary and Confidential Information 17
Aggregated Historical Baseline
Total MBTU, Del Mar Facility
80%
90%
100%
110%
120%
130%
140%
1 4 7
10
13
16
19
22
25
28
31
34
37
40
43
46
49
52
Week
Pe
rce
nt
of
Av
era
ge
20
00
W
ee
kly
Co
ns
um
pti
on
Year 2000 actuals
Year 2001 actuals
Total kilowatt-hours, Commercial Operations, US
LeveragingEnergyData020605.ppt PredictPower Proprietary and Confidential Information 18
Budget
Total MBTU, Del Mar Facility
80%
90%
100%
110%
120%
130%
140%
1 4 7
10
13
16
19
22
25
28
31
34
37
40
43
46
49
52
Week
Pe
rce
nt
of
Av
era
ge
20
00
W
ee
kly
Co
ns
um
pti
on
Year 2000 actuals
Year 2001 actuals
Year 2002 budget
Total kilowatt-hours, Commercial Operations, US
LeveragingEnergyData020605.ppt PredictPower Proprietary and Confidential Information 19
Actuals
Total MBTU, Del Mar Facility
80%
90%
100%
110%
120%
130%
140%
1 4 7
10
13
16
19
22
25
28
31
34
37
40
43
46
49
52
Week
Pe
rce
nt
of
Av
era
ge
20
00
W
ee
kly
Co
ns
um
pti
on
Year 2000 actualsYear 2001 actuals
Year 2002 budgetYear 2002 actuals
Total kilowatt-hours, Commercial Operations, US
LeveragingEnergyData020605.ppt PredictPower Proprietary and Confidential Information 20
Projected
Total MBTU, Del Mar Facility
80%
90%
100%
110%
120%
130%
140%
1 4 7
10
13
16
19
22
25
28
31
34
37
40
43
46
49
52
Week
Pe
rce
nt
of
Av
era
ge
20
00
W
ee
kly
Co
ns
um
pti
on
Year 2000 actualsYear 2001 actualsYear 2002 budgetYear 2002 actualsYear 2002 projected
Total kilowatt-hours, Commercial Operations, US
LeveragingEnergyData020605.ppt PredictPower Proprietary and Confidential Information 21
Forecast
Total MBTU, Del Mar Facility
80%
90%
100%
110%
120%
130%
140%
1 4 7
10
13
16
19
22
25
28
31
34
37
40
43
46
49
52
Week
Pe
rce
nt
of
Av
era
ge
20
00
W
ee
kly
Co
ns
um
pti
on
Year 2000 actualsYear 2001 actualsYear 2002 budgetYear 2002 actualsYear 2002 projectedYear 2002 forecast
Total kilowatt-hours, Commercial Operations, US
LeveragingEnergyData020605.ppt PredictPower Proprietary and Confidential Information 24
Core Technology - Forecasts
Price forecast by market region Load forecasts by service territory or enterp
rise
LeveragingEnergyData020605.ppt PredictPower Proprietary and Confidential Information 25
Price Signals
LeveragingEnergyData020605.ppt PredictPower Proprietary and Confidential Information 26
Weather Data by ZIP Code- <PredictPower>
- <WeatherReports>- <RequestedLocation>
<ZipCode>92024</ZipCode> <CityName>Encinitas, CA</CityName>
</RequestedLocation>- <Record>
<Station>Carlsbad, McClellan-Palomar Airport</Station> <Distance>5mi N</Distance> <Time>3:53 AM</Time> <Condition>Mostly Cloudy</Condition> <Temperature>44.1</Temperature> <Dewpoint>39.9</Dewpoint> <Humidity>85</Humidity> <Wind>E 6</Wind>
</Record>
LeveragingEnergyData020605.ppt PredictPower Proprietary and Confidential Information 27
Analytical Capabilities
Proprietary physical and market-based models– Custom-fit nonlinear parameterized models– Comprehensive expert knowledge of market dynamics– Scores of high bandwidth, real-time data streams– Data-intensive computations from online database– Computationally intensive nonlinear optimization training
Has the advantages of both nonlinear regression and neural networks – and avoids their weaknesses
– Correctly captures real-world effects– Flexibility and adaptability for complex modeling – Efficient capture of knowledge of energy market dynamics– Avoids overfitting due to large functional search space
LeveragingEnergyData020605.ppt PredictPower Proprietary and Confidential Information 28
Management Team
Peter Czajkowski, President - Akamai, SAIC, system engineering, project management, marketing
Alan Creutz, Ph. D., VP Corp. Strategy - SCT Corporation, $100M/yr energy software division. Director, PDMA. Product management, marketing
Elmer Hung, Ph.D., Chief Scientist - MIT AI Lab, Xerox PARC
Mark Juergensen, VP Sales - President of LAPA. Solar Turbines, SAMS and Advanced Turbine Systems Groups, Sterling Energy
LeveragingEnergyData020605.ppt PredictPower Proprietary and Confidential Information 29
Management Team
Jackson Mueller, Energy Market Analyst - Simpson Paper, Home Depot, Luby’s Diners
Howard Axelrod, Ph.D. - Federal Govt. advisor, utility consultant, econometrician
Charles E. Bayless - Dynegy board member, 3- time utility CEO
LeveragingEnergyData020605.ppt PredictPower Proprietary and Confidential Information 30
Why PredictPower?
Vision Market knowledge Technology Forward-looking analyses Collaborative approach Flexible implementation Strong team Provides you with a competitive edge
Transforms your data into energy knowledge
LeveragingEnergyData020605.ppt
PredictPower Proprietary and Confidential Information
31
Leveraging Energy DataJune 5, 2002
Creating Knowledge
From Energy Data
top related