Grants and Contributions:

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Title:
Data Mining for Energy Analysis
Agreement Number:
EGP2
Agreement Value:
$12,500.00
Agreement Date:
Jun 14, 2017 -
Organization:
Natural Sciences and Engineering Research Council of Canada
Location:
Quebec, CA
Reference Number:
GC-2017-Q1-00559
Agreement Type:
Grant
Report Type:
Grants and Contributions
Additional Information:

Grant or Award spanning more than one fiscal year. (2017-2018 to 2018-2019)

Recipient's Legal Name:
Bouguessa, Mohamed (Université du Québec à Montréal)
Program:
Engage Plus Grants for Universities
Program Purpose:

The advent of information technology and electronic have facilitated the recording process of energyx000D
consumption of costumers using smart meters. The recorded information can be explored to figure out relevantx000D
insights. However, digging into this data pose a serious problem in the sense we have tremendous amount ofx000D
costumers, which present various behaviour at different time point. Motivated by the need of effective softwarex000D
for analyzing energy-related big data, SAP Canada Inc. ("SAP"), is developing SAP Energy Analysis, a newx000D
generation of real time business intelligence software for energy-related issues. SAP Energy Analysis offers ax000D
set of tools to run the data collected from smart meters and any other monitoring systems into actionablex000D
insights. Specifically, SAP Energy Analysis provides tools to manage the cost effectiveness of energyx000D
production and consumption in the form of interactive dashboard, personalized report, real time businessx000D
activity monitoring, prescriptive analysis, and more. By effectively analysing customers' consumption datax000D
using SAP Energy Analysis, it becomes possible to monitor the energy production and consumption and, thus,x000D
allow better load balance, load matching and load scheduling through improving energy efficiencies andx000D
management practices. To this end, the company wants to increase SAP Energy Analysis software capabilitiesx000D
to fully harness the potential of the collected data. To achieve this goal, the proposed project aims to investigatex000D
advanced data mining algorithms in order to extract, from the collected data, useful patterns of actions thatx000D
offer insight to track customers' consumption over different time stamps and develop an automatic tool tox000D
instantly aggregate and analyze customers' energy consumption profiles by any dimension or level ofx000D
granularity as well as predicting future consumption trends which in turn permits efficient energy use.