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2016 CSE3/4CI Computational Intelligence
Assignment
Proposed by A/Prof. Justin D. Wang
This assignment contributes 30% of your overall marks for students enrolled in CSE3CI,
and 20% of your overall marks for students enrolled in CSE4CI. Please read this sheet
carefully before doing your assignment.
Summary: The assignment aims at consolidating your knowledge base and developing
practical skills to build a fuzzy system for forecasting the electricity price. The task is
formulated as a timeseries prediction problem for business application, and the goal is to
model the behaviour of underlying dynamics of the electricity market. In principle, the
merits of such a fuzzy forecasting system can be evaluated by two aspects:
The number of fuzzy rules in the rulebase and the number of variables used
in the antecedent part of the fuzzy rules (the smaller the better);
System performance in terms of the accuracy (the smaller the better), i.e., the
average relative error between your fuzzy system outputs and the actual
outputs for both the training data set (learning capability) and the test data
set (generalization capability).
This is an INDIVIDUAL assignment and for both 3rd and 4th year students. You are NOT
permitted to work as a group when completing this assignment. The length of the
assignment report is about 750 words.
Copying, Plagiarism: Plagiarism is the submission of somebody else’s work in a manner
that gives the impression that the work is your own. The Department of Computer Science
and Information Technology at La Trobe University treats plagiarism very seriously. When
it is detected, penalties are strictly imposed.
Date due and late submission policy: May 13, 2016 (Friday)
All assignments are due at 10:00 am.
A penalty of 5% per day will be imposed on all late assignments up to 5 days. An
assignment submitted more than five working days after the due date will NOT be
accepted and zero mark will be assigned.
Assignment without the signed declaration of authorship attached will NOT be
accepted and zero mark will be assigned.
Students will not be granted an extension of the assignment deadline. Students
are requested to submit an application for special consideration through Student
Centre. In addition, students are advised to submit whatever incomplete work they
have already done for the assignment.
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Where to Submit: Your assignment report (hardcopy) is to be submitted at a labelled box
opposite to BG 139 lab.
Problem Description (A Fuzzy System for Forecasting Electricity Price)
Develop a fuzzy forecasting system using Matlab Toolbox. The system performs a
forecasting task for power marketing price. The data used in this assignment is from the
real world (Queensland, Australia), and it has been split up into two parts, i.e., a training
dataset which will be used to build your fuzzy forecasting system, and a testing dataset
which will be used to evaluate your system performance in terms of generalization
capability. The data sets can be downloaded from the Assignment directory in LMS.
Related Concept
Outliers: Roughly, an outlier is an observation that lies an abnormal distance from other
values in a random sample from a population. You can read more about this concept via
the links below:
http://www.itl.nist.gov/div898/handbook/prc/section1/prc16.htm
http://mathworld.wolfram.com/Outlier.html
Average Relative Error: A metric for measuring forecasting systems performance is
defined by:

TargetOutput
SystemOutput TargetOutput
1 
1 i
i i
N
N i
RErr
,  (1) 
AssignmentTutorOnline
where N is the cardinality of the data set under testing.
System Inputs and Outputs
Let the temperature and total demand of electricity at time instant t be T(t) and D(t),
respectively. The goal of the fuzzy forecasting system is to predict the RRP price by using
some historical data as system inputs. In this assignment, the historical data set used for
building the fuzzy system at time instant t is composed of a subset of the set M={T(t2),
T(t1), T(t), D(t2), D(t1), D(t)}. The output of your system at time instant t is a forecasting
value of the Recommended Retail Price (RRP) of electricity at the next time instant t+1,
denoted by P(t+1).
Note that you should select a subset of the set M as the system’s input variables by using
correlation analysis.
Tasks Description (the maximum marks for each item below is 20)
Remove outliers of the output variable from the datasets (both training and test),
and give a list of the outliers; and then rebuild the training and the test datasets;
Select appropriate values or fuzzy subsets for linguistic variables used in your
fuzzy rules;
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List the fuzzy rules that are generated by using statistical analysis (correlation
coefficients) with heuristics;
Implement your fuzzy system using the Matlab Fuzzy Toolbox, where all
membership functions involved in your system should be plotted clearly;
Report your system performance in terms of the average relative error for both
training and testing datasets, and analyze the effects of membership functions and
defuzzification methods.
Remarks
Assessment will be done by looking at the average relative prediction accuracy for
both the training data set and the test data set.
Either Mamdanitype or Sugenotype fuzzy rules can be applied. The ANFIS tool
will NOT be acceptable for carrying out this task.
Your report should provide a commented list of the Matlab Commands used in your
system with some graphical illustrations. It will be appreciated to show some finetuning of the system’s parameters to produce sensible results. It is encouraged to
appropriately use appendices to detail your results.
Assessment Criteria
(10080 marks) – An excellent, wellwritten report. You have produced a working system
that produces sensible results. The report summarises the approach taken well. You have
analysed the performance of the system and presented the results in an interesting and
sound way. A thorough and systematic analysis of the effect of different membership
functions and different defuzzification techniques is presented.
(7960 marks) – A wellwritten report. You have produced a working system that
produces good results. You have exhibited some initiative in the approach taken and the
results are presented clearly. An analysis of the effect of different membership functions
and different defuzzification techniques is presented.
(5940 marks) – A reasonable report that presents an account of the approach taken and
the final system. The system performs reasonably well and the results are presented
reasonably clearly. Either different membership functions or different defuzzification
techniques have been explored.
(3920 marks) – A report that presents some results of a working system. Demonstrating
some understandings on fuzzy forecasting system design.
(190 marks) – Either no report submitted or a report that shows little or no understanding
of how to develop a fuzzy system.
– End of Assignment Paper –
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