Smart Grid and Cloud Computing

earsplittinggoodbeeInternet and Web Development

Nov 3, 2013 (3 years and 10 months ago)

60 views

A B M Shawkat Ali

CQUniversity, Australia

Support Vector Machine
in

Smart Grid
and
Cloud Computing


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
:



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.

,

Figure 2. Hourly average solar radiation of Rockhampton,
Australia

Average solar radiation w/m^2

Time in Hour

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 fo
r 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.


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

Test Data Domain

Kernel Mapping

















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



Livestock tacking


Road load estimation for a better plan


Industry automation: Magnesia sorting


Cool train monitoring


High Lime Core

EFH1

EFH2

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


----------------------------------------------------------------------------------------------------------------
---------------

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
Q
uestions
-


?
”.