# Energy Industry Analytics: R Labs

## Johannes Mauritzen

### About Energy Industry Analytics: R Labs

I created the following labs for a masters course I have taught at NHH Norwegian School of Economics called Energy Industry Analytics. The labs are 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.

The aim of the labs is for students to 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 labs will be using data to analyse changes in market structure induced by changing regulations or the shift towards low-carbon technologies. I use the statistical software R to organise, clean, visualise and model energy data.

In the labs I refer to three texts, all with freely available digital versions for personal use. For general data handling, cleaning, and visualisation, I use Hadly Wickham and Garret Groleman [R for Data Science](https://r4ds.hadley.nz). For time series analysis and forecasting, I use Rob Hyndman and George Athanasopoulos [Forecasting: Principles and Practice](https://otexts.com/fpp3/). For a general reference on statistical learning and non-linear models in particular, I use James, Witten, Hastie and Tibshirani [An Introduction to Statistical Learning](https://hastie.su.domains/ISLR2/ISLRv2_website.pdf). Other references are cited, as needed, in the text of the labs.

You can cite as:

Mauritzen, Johannes (2022) Energy Industry Analytics: R Labs. https://jmaurit.github.io/EnergyIndustryAnalytics/. Accessed on (date)

ISBN 978-82-693160-3-2

# Introducing R

# Part I: Cleaning, transforming and managing data/Oil and Gas

# Part II: Time series analysis and power markets

# Part III: Non-linear and non-parametric models/Distributed generation

- Garrett Grolemund and Hadley Wickham (2023). “R for Data Science 2e (RDS)”
- James, Witten, Hastie and Tibshirani, 2nd edition (2021). “Introduction to Statistical Learning” (ISL)
- Rob J Hyndman and George Athanasopoulos (2020). “Forecasting: Principles and Practice” (FPP3)