University of Technology Sydney

49027 Energy Demand Analysis and Forecasting

Warning: The information on this page is indicative. The subject outline for a particular session, location and mode of offering is the authoritative source of all information about the subject for that offering. Required texts, recommended texts and references in particular are likely to change. Students will be provided with a subject outline once they enrol in the subject.

Subject handbook information prior to 2020 is available in the Archives.

UTS: Engineering: Information, Systems and Modelling
Credit points: 6 cp

Subject level:

Postgraduate

Result type: Grade and marks

There are course requisites for this subject. See access conditions.

Description

This subject introduces students to the concepts and methods of energy demand analysis and forecasting. The main topics include: economic and technical foundations of energy demand; information, data and interpretation of past demand patterns and trends; and modelling and forecasting of energy demand.

Subject learning objectives (SLOs)

Upon successful completion of this subject students should be able to:

1. explain bases and drivers of energy demand;
2. describe the nature of links between energy demand and socioeconomic development;
3. apply mathematical models and approaches to analyse and forecast energy demand; and
4. develop constructive critique on the appropriateness of energy forecasting approaches.

Course intended learning outcomes (CILOs)

This subject also contributes specifically to the development of the following Course Intended Learning Outcomes (CILOs):

  • Socially Responsible: FEIT graduates identify, engage, and influence stakeholders, and apply expert judgment establishing and managing constraints, conflicts and uncertainties within a hazards and risk framework to define system requirements and interactivity. (B.1)
  • Design Oriented: FEIT graduates apply problem solving, design thinking and decision-making methodologies in new contexts or to novel problems, to explore, test, analyse and synthesise complex ideas, theories or concepts. (C.1)
  • Technically Proficient: FEIT graduates apply theoretical, conceptual, software and physical tools and advanced discipline knowledge to research, evaluate and predict future performance of systems characterised by complexity. (D.1)
  • Collaborative and Communicative: FEIT graduates work as an effective member or leader of diverse teams, communicating effectively and operating autonomously within cross-disciplinary and cross-cultural contexts in the workplace. (E.1)
  • Reflective: FEIT graduates critically self-review their own and others' performance with a high level of responsibility to improve and practice competently for the benefit of professional practice and society. (F.1)

Teaching and learning strategies

This subject is offered in block mode, involving three (two day each) modules, spread over a 12 week session.

Each block follows a similar structure, in that students are required to access pre-class review of subject reading/viewing material on UTSOnline. The material is designed to enable students to engage in meaningful discussions in class. Communication is integral to each class and the pre-reading material informs these conversations.

In class, students will have a formal lecture to re-assess understanding of concepts and methods and their applications in real-life situations using case studies. Students engage in extensive consultations to complete set exercises. These exercises provide opportunities for developing an understanding of concepts and methods relevant to the subject. They also provide opportunities for critical evaluation of presented information. Assignments are issued in each Block and are to be handed in before the next Block so that verbal feedback and relevant examples can be provided to guide each Block. Each Block follows an assignment and feedback, hence increasing understanding and increased opportunity for feedback. The assignments are designed to test students’ ability to apply concepts and methods in specific contexts, analyse policy trade-offs and, develop constructive critique. Quizzes are designed to assess knowledge and understanding of subject content. By completing these before each Block, students will receive verbal feedback on their progress in the following Block.

Content (topics)

  1. General overview of the subject
  • Nature of energy
  • Bases of energy demand
  • Evolution of nature and bases of energy demand
  • Policy and political connects of energy demand

  1. Methodological approaches for energy demand analysis and forecasting
  • Econometric energy demand modelling
  • End-use method of energy demand analysis
  • Other methods (including, indicators-based forecasting, time series model, judgemental forecasting)

  1. Econometric models for energy demand analysis and forecasting
  • Multiple regression model
  • Structural and reduced-form models
  • Partial adjustment models
  • Test of regression coefficients and regression equation
  • Test for Heteroskedasticity, Autocorrelation and Multicollinearity issues

Assessment

Assessment task 1: Assignment 1

Objective(s):

This assessment task addresses the following subject learning objectives (SLOs):

1, 2, 3 and 4

This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs):

B.1, C.1, D.1, E.1 and F.1

Type: Exercises
Groupwork: Individual
Weight: 15%

Assessment task 2: Quiz 1

Objective(s):

This assessment task addresses the following subject learning objectives (SLOs):

1, 2, 3 and 4

This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs):

B.1, C.1, D.1 and F.1

Type: Quiz/test
Groupwork: Individual
Weight: 25%

Assessment task 3: Assignment 2

Objective(s):

This assessment task addresses the following subject learning objectives (SLOs):

1, 2, 3 and 4

This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs):

B.1, D.1 and E.1

Type: Exercises
Groupwork: Individual
Weight: 15%

Assessment task 4: Quiz 2

Objective(s):

This assessment task addresses the following subject learning objectives (SLOs):

1, 2, 3 and 4

This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs):

B.1, C.1, D.1 and F.1

Type: Quiz/test
Groupwork: Individual
Weight: 25%

Assessment task 5: Assignment 3

Objective(s):

This assessment task addresses the following subject learning objectives (SLOs):

1, 2, 3 and 4

This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs):

B.1, C.1, D.1, E.1 and F.1

Type: Exercises
Groupwork: Individual
Weight: 10%

Assessment task 6: Class Participation

Intent:

To demonstrate students’ participation to critically engage with materials covered in this subject, and their ability to confidently engage in meaningful discussion in the class.

Objective(s):

This assessment task addresses the following subject learning objectives (SLOs):

1, 2 and 4

This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs):

E.1

Groupwork: Individual
Weight: 10%

Required texts

None prescribed. Selective lecture notes, journal articles and other readings are provided below.

References

Barker, t., Ekins, P., and Johnstone, N. (1995) Global Warming and Energy Demand, Routledge, New York.

Bentzen, J. & Engtad, T. (2001), A revival of the autoregressive distributed lag model in estimating energy demand relationships, Energy 26, 45-55.

Bhattacharyya, S. (2011), Energy Economics: Concepts, Issues, Markets and Governance, Springer-Verlag, London. [Chapters 3-5]

Bhattacharyya, S. & Timilsina, G. (2009), Energy Demand Models for Policy Formulation: A comparative study of energy demand models, Policy Research Working Paper 4866, The World Bank.

Bhattacharyya, S. & Timilsina, G. (2010), Modelling energy demand of developing countries: Are the specific features adequately captured, Energy Policy 38, 1979-1990.

Chateau, B. and Lapillonne, B. (1982) Energy Demand: Facts and Trends, Springer-Verlag, Wien.

Daly, H. (1976) Energy Demand Forecasting: Prediction or Planning?, API Journal, January 1976, pp 4-15.

Donnelly, W. (1987) The Econometrics of Energy Demand: A Survey of Applications, Praeger, London.

Gujarati, D. N. (2009), Basic econometrics, McGraw-Hill Irwin, Boston.

Gujarati, D. N. (2015), Econometrics by example, Macmillan Education Palgrave, London ; New York, NY.

Johansson, O. & Schipper, L. (1997) Measuring the Long-Run Fuel Demand of Cars: Separate Estimations of Vehicle Stock, Mean Fuel Intensity, and Mean Annual Driving Distance, Journal of Transport Economics and Policy 31(3), 277-292.

Medlock III, K.B. (2009) Energy demand theory, in J. Evans & L.C. Hunt (eds.) International Handbook on the Economics of Energy, Edward Elgar, Cheltenham, pp. 89-111.

Medlock III, K.B. & Soligo, R. (2001) Economic development and end-use energy demand, The Energy Journal 22(2), 77-105.

Robinson, J. (1982) Energy Backcasting: A proposed method of policy analysis, Energy Policy, December 1982, pp 337-344.

Ryan, D.L. & Plourde, A. (2009) Empirical modelling of energy demand, in J. Evans & L.C. Hunt (eds.) International Handbook on the Economics of Energy, Edward Elgar, Cheltenham, pp. 112-143.

Watkins, G. (1992) The Economic Analysis of Energy Demand: Perspectives of a Practitioner, in D.Hawdon (ed.) Energy Demand: Evidence and Expectations, Surrey University Press, London.