Support Vector Machine in
Smart Grid and Cloud Computing
A B M Shawkat Ali
CQUniversity, Australia
• Smart Grid: How much Computational Intelligence
(CI) is involved?
• Cloud Computing: How can CI ensure the services?
• Current Projects
• Demand and Supply
• IT
• Automatic Observation
Figure 1. A sketch of Smart Grid.
Need an Intelligent System for:
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Forecasting demand and supply
Grid security
Monitoring power quality
Power storage
Let us consider n data points
For instance
Recently, a new loss function called -insensitive loss has been proposed by Vapnik (1995):
Subject to
This optimization problem can be transformed into the dual problem (Vapnik, 1995),
and its solution is given by
with coefficient values in the range
,
and
denotes the dot product in the
input space.
age solar radiation w/m^2
in Hour
Figure 2. HourlyTime
average
solar radiation of Rockhampton,
Australia
Outliers, High-leverage Points and Influential Observations
Figure 3. Outlier mapping.
Imon (2005) defines generalized Studentized residuals and generalized weights (leverage) as
Imon (1996) also introduces generalized potentials for identifying multiple high-leverage points by using group-deletion idea for a dataset as
He re-expresses GDFFITS in terms of deletion residuals and leverages as
Outlier Detection in
Linear Regression
Table 1. Solar radiation prediction performance.
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Software
Service
Storage
Data Mining
Data analysis
Figure 4. An overview of Cloud Computing.
Figure 5. A real life Cloud Computing Environment.
Figure 6. Performance chart of Hypervisor at the time of Installing
new VM.
Training Data Domain
Non linear Mapping
by Kernel
To Choose Optimal
Hyperplane
Linear Feature Space of SVM
Figure 7. SVM training process.
Construct Model
through Feature
knowledge
Class I
Class II
Kernel Mapping
Test Data Domain
Figure 8. SVM model testing process.
Mercer’s Condition
Figure 7. Ten (10) fold cross validation process.
Figure 8. Attack classification performance in the real life Cloud scenario.
Student Projects:
PhD Students
Mohammed Mizanul Mazid
Topic: Intrusion Detection Using Machine Learning
Gazi Mohammad Shafiullah
Topic: Experimental Investigation and Development of Renewable Energy Integration into the Smart Grid
Md Rahat Hossain
Topic: Hybrid Forecasting System of Renewable Energy with Smart Grid for a Sustainable Future
Mohammed Arif
Topic: Storage and Its Strategic Impacts on Smart Grid
MD Tanzim Khorshed
Topic: Combating Cyber Attacks in Cloud Computing Using Machine Learning Techniques
MD Akhlaqur Rahman
Topic: Data Mining in Telecommunication Industry of Call Records, Customer Profiles and Network Data
Master’s Student
Choudhury Wahid
Topic: Cancer Classification by Support Vector Machine using Microarray Gene Expression Data
Personal Projects
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Livestock tacking
Road load estimation for a better plan
Industry automation: Magnesia sorting
Cool train monitoring
High Lime Core
EFH2
EFH1
Analysing : Percent correct
Confidence : 0.05 (two tailed)
Date : 7/17/11 12:23 PM
Dataset (1) NB| (2) SMO (3) lBK (4) ABM1 (5) J48 (6) PART
-----------------------------------------------------------------------------------------------------------------------------mgdata (100) 77.62| 73.71 83.76 78.48 80.69 84.31
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Our mission is to establish the
effectiveness of CI theories by solving
industry problems!
“I cannot teach anybody anything, I can only
make them think”. – Socrates (470–399 B.C.)
1. Vapnik, V. N., (2005). The Nature of Statitical Learning Theory , Springer.
2. Imon, A. H. M. R. (1996). Subsample methods in regression residual prediction and diagnostics. PhD
Thesis, University of Birmingham, UK.
3. Imon, A. H. M. R. (2005). Identifying multiple influential observations in linear regression. Journal of
Applied Statistics, 32, 73 – 90.
4. Shafiullah, GM., M. T. Oo, A., Ali, S. A., D. Jarvis, and Wolfs, P., "Prospects of Renewable Energy - A
feasibility study in the Australian context", Accepted for the International Journal of Renewable
Energy, ELSEVIER, 2011.
5. Khorshed, M. T., Ali, S., and Wasimi, S., "Monitoring Insiders Activities in Cloud Computing Using
Rule Based Learning", Accepted for IEEE TrustCom-11, Nov. 16-18, 2011, Changsha, China.
6. Shafiullah, GM., Ali, S. Thompson, A. and Wolfs, P. "Forecasting Vertical Acceleration of Railway
Wagons using Regression Algorithms" IEEE Transactions on Intelligent Transportation Systems, vol.
11, No. 2, June 2010, pp. 290-299.
7. Ali, S. and Pun, D., "Electrofused Magnesium Oxide Classification Using Digital Image Processing
and Machine Learning Techniques", Proceeding of The IEEE International Conference on Industrial
Technology (ICIT 2009), 10-13 February 2009, Australia.
8. Khorshed, M. T., Ali, S., and Wasimi, S., “A survey on gaps, threats remediation challenges and
some thoughts for proactive attack detection in cloud computing “, Submitted to Future Generation
Computer System, Elsevier, 2011. (Under Review).
9. Hossain, M. R., M. T. Oo, A., Ali, S., " Computational Intelligence: The Effectiveness in Smart Grid ",
Submitted to IEEE Transaction on Smart Grid, 2011,
Now your time!
Please ask me your Questions- “?”.