demand forecast modelling: energy consumption...

IBM Global Energy & Utilities Industry

Demand Forecast Modelling:Energy Consumption Prediction

Dorian Camil Hodorogea, Business Analytics Presales for SEE


Agenda

IBM and ‘Predictive Analytics’Brief introduction to SPSSCase studyCustomer stories

2


Trends in Business Analytics and Opitmization

3Degree of Complexity

Com

petit

ive

Adv

anta

ge

What happened?Standard Reporting

How many, how often, where?Ad hoc reporting

What exactly is the problem?Query/drill down

Is action needed?Alerts

What will happen if … ? Simulation

What will happen next? Machine Learning

How can we achieve the best outcome? Optimization

How can we achieve the best outcome including the effects of variability? Stochastic Optimization

Analytics“Forward-looking”

Reporting“Backward-looking”


Predictive Analytics in Energy: examples

What will be the energy demand tomorrow, next week, next year?

When will elements of my grid will need upgrading?

When is this asset most likely to fail and for what reason?

Which of my customers pose the highest revenue risk and what is the best preventative action to take?

Which customers are most likely to adopt a proposed change in tariff plan and what will be the resulting change in consumption patterns?

4


SPSS Modeler

…is a high-performance data mining and text analytics workbench

– Easy to Learn / Intuitive Visual InterfaceVisual approach - no programmingComprehensive range of data mining functionsFlexible deployment options

– Powerful Automated modelingAutomated data preparationMulti model creation & evaluationIntegrated analysis of text, web, & survey data

– Open and scalable architecureData mining within standard databases with SQL pushdown support Maximized use of infrastructure with multithreading, clustering and use of embedded algorithms (in database mining)Integration with IBM technologies such as IBM Cognos 8 Business Analytics and IBM InfoSphere Warehouse

5


6

Case Study


Input Data

Input EnergyOutput Energy

TemperatureHumidityNebulosityAtm. PressureWind speedWind direction


SPSS Model


Graphical Output


Results


Customer quotes …

11


EDF

“With around five million customer records, we have a vast pool of data that we can mine for insights to improve the efficiency of our business.”

“However, until we started working using Clementine from SPSS, our analysis was carried out on an ad hoc basis meaning that to a large extent that the wealth of information was not exploited.”

“SPSS’ technology means we can maximise the value of our data and use the resulting information to greatly improve our customer service and sales efforts. This enables us to gain better results without having to increase our resources.”

Clifford Budge, Customer Insight Manager at EDF Energy, UK

12


Central Iowa Power Cooperative (CIPCO)

Analyzes critical pricing and energy load data with SPSS

“Many large power companies have entire departments dedicated to analysis and forecasting. Our small group really values the efficiency that we get from instrumenting and integrating our data with IBM SPSS Statistics.”

Lisa SellEnergy Planning Manager

CIPCO

13


PowerGen

“SPSS has more than paid for itself in the short time we’ve had it and, as a result, we’ve started to use text mining tools from the company to look at customer records in call centers. In the analytics team at least, SPSS is the fundamental tool in our toolkit, and has more than proved its value.”

Stewart RobbinsCustomer Knowledge Manager

Powergen, UK

14


Yavor Mihov Bogdena [email protected] [email protected]

9, Vladimir Vazov Blvd., 7th floor1510 Sofia, Bulgaria

Phone/Fax: +359 2/868 79 85; +359 2/868 35 99;+359 2/962 14 48

Thank you!

Dorian Camil [email protected]

demand forecast modelling: energy consumption...

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