Energy Industry Analytics

ENE434 Energy Industry Analytics

Spring 2024

  • 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.


    • 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 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 CO2 taxes and cap-and-trade allowances on energy markets
    • The effects of taxation rules on oil and gas extraction

  • Learning outcome


    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.


    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.

    General competence

    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 2 intensive week sessions with 1 x 2 class periods on Monday and Friday and of 2 x 2 class periods the other days.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  

  • Credit reduction due to overlap


  • Compulsory Activity

    Students must submit two group assignments.

  • Assessment

    Term analysis, written in a "notebook" form. Students can work individually or in a group of one other person (maximum 2 students).

    • Starting in Week 10
    • Deadline in Week 20 (before May 17)

    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 (2020). "Introduction to Learning Algorithms", 2nd Edition.
    • Rob J. Hyndman and George Athanasopoulos (2021). "Forecasting: Principles and Practice" , 3rd Edition.


ECTS Credits
Teaching language

Spring. Will be offered Spring 2024.

Course responsible

Adjunct Professor Anne Neumann, Department of Business and Management Science