Predictive Analytics for Smart Gridto

Make it happen

www.ispredict.com | Copyright

Britta Hilt | MD

Dublin, 6th May 2014

IS Predict & Scheer Group

Employees2010 - 2014

Turnover (million €)2010 - 2014Locations

Prof

. A.

-W. S

chee

r

2www.ispredict.com | Copyright

Visionary, researcher and author of standard works for business information systems

Member of the council for innovation and growth of the German Government

President of the German Association for Information Technology (BITKOM 2007-2011)

Ranked as 2nd most important German IT person (of 100) by Computerwoche magazin (after Hasso Plattner / SAP) in 2011

Founder of international software companies IDS Scheer & IMC AG

Sole Shareholder of Scheer Group GmbH

Germany Australia Austria Benelux

France Great Britain Rumania Switzerland

100

50

800

400

Turkey Ukraine

Smart Grid´s Complexities

Various players High volatile power

generation & consumption On grid / Off grid Reliable & affordable “Green” …

3www.ispredict.com | Copyright

Challenge 1 – How to…

… keep control in volatile grids?

Challenge 2 – How to…

… use energy efficiently?

How to manage Smart Grid`s challenges

Challenge 1 – How to…

… keep control in volatile grids?

Challenge 2 – How to…

… use energy efficiently?

4www.ispredict.com | Copyright

Plannable Energy FlowsHighly accurate knowledge when how much Wind / sun energy will be generated Energy will be consumed by industry

and private households

Transparency on Root & CauseHighly accurate knowledge which (hidden) factors increase energy consumption Complex human behavior Complex machine behavior

Comparions of Prediction Toolsin Highly Volatile Use Cases

Generic regression

Comparison: 24 h prediction gas consumptionExample: Difficult month

Specific forecastExternal supplier for utilities with focus energy

forecastsDeviation

Average: 18%Maximum: 47 %

Generic „Discovery“Resource Intelligence prediction with automatic

model generationDeviation

Average: 8%Maximum: 26 %

5www.ispredict.com | Copyright

Example UtilitiesGas Energy

Reduced costs for energy via more precise 24 h gas prediction

Objective: Plan demand-oriented gas purchase for tomorrow & thus, reduce purchasing costsProblem: Standard load profiles too inflexible for dynamic demand of consumerHow: Dynamic load profiles with flexible pattern recognizionData: Historic gas consumptions, weather (past and forecast); no consumer classification

Accuracy Jan

Feb

Mar

Apr

May

Jun

O 24h (%) 96 89 91 88 86 88

Accuracy Jan

Feb

Mar

Apr

May Jun

O 24h (%) 92 90 81 83 67 74

Resource Intelligence ca. twice as precisethan state of the art solution with standard load profiles

Resource Intelligence

State of the Art SolutionApril

6www.ispredict.com | Copyright

Example UtilitiesPV Power Usage

Objective: Run production machinery mostly on PV power, generated by your ownProblem: PV power very volatile and difficult to plan; energy demand of machinery also

volatile; energy demand does not match energy availabilityHow: Foresighted machinery control via accurate PV power generation predictionData: Weather (past / forecast) power generation (past)

Accuracy Mar Apr May Jun Jul Aug Sep Oct

O Month 94 % 97 % 94 % 93 % 99 % 96 % 97 % 92 %O Day 91 % 93 % 92 % 93 % 95 % 95 % 93 % 93 %

Accurate 24 h PV power generation predictionfor 1 individual installation

Reduced power costs due to optimal usage of own PV power

Resource Intelligence realizes flexible and precise predictions despite high volatility7www.ispredict.com | Copyright

Example UtilitiesWater Consumption

Objective: Cost optimization in drinking water supplyProblem: Water demand highly volatile and difficult to plan; Reduction of power costs

for pumps but also 100% availability of water supplyHow: Running pumps when power price is low thanks to precise water consumption

predictionData: Weather (past / forecast), water consumption (past)

Accuracy Jan Feb Mar Apr May Jun

O Month 98,1 % 97,0 % 99,6 % 99,3 % 99,0 % 98,0 %

O Day 97 % 97 % 99 % 94 % 97 % 95 %

Less costs for water supply

Accurate 24 h water consumption prediction

Resource Intelligence realizes flexible and precise predictions despite high volatility

8www.ispredict.com | Copyright

Example ProductionPredictive Dispatching

Optimized Energy Dispatching

Vari

ati

on

s in

oven Energy forecast for energy planning &

efficiency analysis

Required energy

Steel width

Steel mass

Time: 24 hPredicted EnergyRequired Energy

Objective: Efficient energy dispatching and planned energy purchase in steel companyWay: Enable planning for large energy consumers despite “not planable” consumptionProblem: Highly volatile energy demand which does not seem to be caused by production.Data: Energy consumption & limited production (planning) data

1 monthEner

gy D

eman

d

24 h forecast for oven with 90 - 99 % accuracy!

9www.ispredict.com | Copyright

Machinery ControlReduced Operating Costs

Which factors cause increased energy consumption per tonne of pellets?

Objective: To control two wood dryer in optimal energy usage although product quality stays the samePlease note: No permanent usage of dryers required.

Problem: Highly varying energy consumption of wood dryer. Therefore, it is unclear which factors increase energy consumption

Solution: Discovering influencing factors for energy consumptionvia pattern recognizion and correlation analysis

Data: Consumption of long distance heating, outside temperature, other production data

1st DiscoveryDespite expert expectation:

Air humidity has minimal influence on energy consumption!

10www.ispredict.com | Copyright

Proportional influence

Ener

gy c

onsu

mpti

on

Assembly belt speed

2nd DiscoveryMore than proportional increase when

assembly belt speed is increased.

Example: Predictive Maintenance

Discover anomalies in machinery behavior i.e. in resource consumption

Individual demand-oriented maintenance via anomaly analysis

Objective: Increase efficiency via early information on (future) wear & tearWay: Discover first and hidden signs when machinery does not run efficient anymoreCondition: Individual & cost-reduced analysis per machine without additional sensorsProblem: Strongly volatile energy demand, only engine energy data, no production data Discovery of anomalies between 86% - 100%!

10 minutes: Engine run without disturbances 10 minutes: 51 disturbances due to breaks

Evaluate anomaliesIrregularities with various strengths and frequencyEarly warningAlert for technical service

Anomaly DetailsNo regularity in variable energy demand during disturbance 11

SAP CEO visits IS Predict

CeBIT 2014

SAP CEO Jim Hagemann Snabe informs himself about Resource Intelligence

Projects

The self-learning & adaptive IT system for cost reduction 23 % in Smart Home Grid

Realizing full potential of renewable energy usage 16 % in Smart Utility

More precise energy purchase & sale, also for renewable energy

12 – 62 % in Smart BuildingForesighted and adaptive building energy control

14 % in Smart ProductionResource management, energy dispatching, machinery control, predictive maintenance, process efficiency, capacity planning

Increased efficiency for Man & Machinery thanks to Predictive Control

Honored with 8 Innovation Awards

13www.ispredict.com | Copyright

ff

14www.ispredict.com | Copyright |

Why we are here:

We want to improve Resource Efficiency at Irish Grid /

Production / Power Plants, too.

We are looking for challenging projects to optimize complex and

difficult processes with innovative & self-learning IT

solutions.

Contact:

Britta.Hilt@ispredict.com+49 176 – 63 72 92 28

IS Predict GmbHScheer Tower | Uni Campus Nord D5.1

66123 Saarbrücken | GermanyPhone +49 681 – 96777-200

www.ispredict.com

Next

Generation

BI