ENE434 Energy Industry Analytics

• Topics

  This course is an introduction in using data to analyse, describe and help make decisions within the energy industry and energy markets. The course covers both the power industry and oil and gas extraction. In the course, students will learn how to use data to answer questions of how, when and where investments in energy generation and extraction take place. Students will also learn how to use data to analyse and make decisions related to power- and petroleum-industry operations. A particular focus of the course will be using data to analyse changes in market structure induced by way of changing regulations or the shift towards low-carbon technologies. Students will use the statistical software R to organise, clean, visualise and model energy data.

  Topics:

  • Modelling and forecasting of price and volatility in power markets
  • Modelling and forecasting of price and volatility in oil and natural gas markets
  • Analysing investment decisions in renewable generation
  • Analysing liquidity and trading patterns on multiple interlinked power markets
  • Analysing exploratory- and extraction-drilling decisions for offshore oil and gas
  • Modelling the operation and dynamics of oil and gas extraction in offshore oil and gas
  • The effects of C02 taxes and cap-and-trade allowances on energy markets
  • The effects of taxation rules on oil and gas extraction

• Learning outcome

  Knowledge - Upon completing the course students

  • Know about the regulatory and market structure of energy industries, and how to use data to analyse the effects of changes in these structures
  • Know about the effects of a changing energy industry, especially the shift towards renewable and low carbon energy.
  • Know about the effects on industry of changes in regulation: For example carbon taxes, resource taxes and environmental regulations.

  Skills - Upon completing the course students

  • Know how to access data related to power, oil and gas industry in Norway and internationally.
  • Know how to clean, organise and undertake a descriptive analysis of data to gain insights in energy markets and industry.

  Competences - Upon completing the course students

  • Can use R to clean, process, visualise and analyse data.
  • Can apply forecasting models and elementary machine learning and classification algorithms towards questions relevant for the energy industry.

• Teaching

  The course is organised in 3 intensive 3-day sessions of 2 x 2 class periods.

  Sessions will be interactive, and involve working through "labs" that continue into group assignments.

• Recommended prerequisites

  • Basic statistical knowledge equivalent to MET2
  • Some prior knowledge of R and energy markets can be helpful, but not required  

• Assessment

  • 40% on handing in 4 group* assignments, written in "notebook" form. Groups can be of size 1-2-3, but groups of 2 are encouraged.
    • Assignment 1-2: Week 5 to Week 11
    • Assignment 3: Week 12 to Week 18
    • Assignment 4: Week 19 to Week 22
  • 60% on an individual term analysis, written in a "notebook" form.
    • Starting in Week 12
    • Deadline in Week 23

  Assignments and term analysis can be written in English or a Scandinavian language.

• Grading Scale

  A - F.

• Computer tools

  R statistical programming language  

• Literature

  • Garrett Grolemund and Hadley Wickham (2017). "R for Data Science"
  • James, Witten, Hastie and Tibshirani (2013). "Introduction to Learning Algorithms"
  • Rob J. Hyndman and George Athanasopoulos (2018). "Forecasting: Principles and Practice"