12019 CSE3/5CI Computational IntelligenceAssignmentProposed by A/Prof. Justin D. WangThis assignment contributes 30% of your overall marks for students enrolled in CSE3CI,and 20% of your overall marks for students enrolled in CSE5CI. Please read this sheetcarefully before doing your assignment.Summary: The assignment aims at consolidating your knowledge base and developingpractical skills to build a fuzzy system for forecasting the electricity price. The task isformulated as a time-series prediction problem for business application, and the goal is tomodel the behaviour of underlying dynamics of the electricity market. In principle, themerits of such a fuzzy forecasting system can be evaluated by two aspects:• The number of fuzzy rules in the rule-base and the number of variables usedin the antecedent part of the fuzzy rules (the smaller the better);• System performance in terms of the accuracy (the smaller the better), i.e., theaverage relative error between your fuzzy system outputs and the actualoutputs for both the training data set (learning capability) and the test dataset (generalization capability).This is a GROUP-based assignment (the maximum number of the group members is 3;all group members will receive same marks) for both 3rd and 5th year students. You areNOT permitted to work as a mixed-group (i.e., all group members must be from the samegrade) when completing this assignment. The length of the assignment report is about1200 words with codes used in Python.Copying, Plagiarism: Plagiarism is the submission of somebody else’s work in a mannerthat gives the impression that the work is your own. The Department of Computer Scienceand Information Technology at La Trobe University treats plagiarism very seriously. Whenit is detected, penalties are strictly imposed.Date due and late submission policy: May 13, 2019 (Monday)• 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. Anassignment submitted more than five working days after the due date will NOT beaccepted and zero mark will be assigned.• Assignment without the signed declaration of authorship attached will NOT beaccepted and zero mark will be assigned.• Students will not be granted an extension of the assignment deadline. Studentsare requested to submit an application for special consideration through StudentCentre. In addition, students are advised to submit whatever incomplete work theyhave already done for the assignment.2Where to Submit: Your assignment report (hardcopy) is to be submitted at a labelled boxopposite to BG 139 lab.Problem Description (A Fuzzy System for Forecasting Electricity Price)Develop a fuzzy forecasting system for data analysis using Python. The system performsa forecasting task for power marketing price. The data used in this assignment is from thereal world (Queensland, Australia), and it has been split up into two parts, i.e., a trainingdataset which will be used to build your fuzzy forecasting system, and a testing datasetwhich will be used to evaluate your system performance in terms of generalizationcapability. The data sets can be downloaded from the Assignment directory in LMS.Related ConceptOutliers: Roughly, an outlier is an observation that lies an abnormal distance from othervalues in a random sample from a population. You can read more about this concept viathe links below:http://www.itl.nist.gov/div898/handbook/prc/section1/prc16.htmhttp://mathworld.wolfram.com/Outlier.htmlAverage Relative Error: A metric for measuring forecasting systems performance isdefined by:|TargetOutputSystemOutput TargetOutput1 |1 i i i=–=NN iRErr

where N is the cardinality of the data set.System Inputs and OutputsLet 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 usingsome historical data as system inputs. In this assignment, the historical data set used forbuilding the fuzzy system at time instant t is composed of a subset of the set M={T(t-2),T(t-1), T(t), D(t-2), D(t-1), D(t)}. The output of your system at time instant t is a forecastingvalue 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 usingcorrelation 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 yourfuzzy rules;3• List the fuzzy rules that are generated by using statistical analysis (correlationcoefficients) with heuristics;• Implement your fuzzy system in Python, where all membership functions involvedin your system should be plotted clearly;• Report your system performance in terms of the average relative error for bothtraining and testing datasets, and analyze the effects of membership functions anddefuzzification methods.Remarks• Assessment will be done by looking at the average relative prediction accuracy forboth the training data set and the test data set.• Either Mamdani-type or Sugeno-type fuzzy rules can be applied.• Your report should provide a full list of Python codes used in your system withsome graphical illustrations. It will be appreciated to show some fine-tuning of thesystem’s parameters to produce sensible results. It is encouraged to appropriatelyuse appendices to detail your results.Assessment Criteria(100-80 marks) – An excellent, well-written report. You have produced a working systemthat produces sensible results. The report summarises the approach taken well. You haveanalysed the performance of the system and presented the results in an interesting andsound way. A thorough and systematic analysis of the effect of different membershipfunctions and different defuzzification techniques is presented.(79-60 marks) – A well-written report. You have produced a working system thatproduces good results. You have exhibited some initiative in the approach taken and theresults are presented clearly. An analysis of the effect of different membership functionsand different defuzzification techniques is presented.(59-40 marks) – A reasonable report that presents an account of the approach taken andthe final system. The system performs reasonably well and the results are presentedreasonably clearly. Either different membership functions or different defuzzificationtechniques have been explored.(39-20 marks) – A report that presents some results of a working system. Demonstratingsome understandings on fuzzy forecasting system design.(19-0 marks) – Either no report submitted or a report that shows little or no understandingof how to develop a fuzzy system.– End of Assignment Paper –

Assignment status: Already Solved By Our Experts

(USA, AUS, UK & CA PhD. Writers)