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Module Title
LM Energy Data and Digitalisation
School
Chemical Engineering
Department
Chemical Engineering
Module Code
04 37626
Module Lead
Dr Grant Wilson
Level
Masters Level
Credits
10
Semester
Semester 2
Pre-requisites
Co-requisites
Restrictions
None
Exclusions
Description
The module aims to provide a practical introduction to the analysis of energy data. Various data sources, with their advantages and disadvantages will be leveraged by using the Python software language. The module is therefore broad in terms of it being an introductory module, and that the learning outcomes and skills can be applied to other areas. However, the module is also specialist in that it has a specific focus on energy data, which includes gas, electrical and liquid fuel data. Students will have a greater understanding of the limits and peculiarities of useful software such as Microsoft Excel, and will gain experience in using the Python language to start to integrate and analyse datasets that are becoming more and more available. In addition, students will gain experience in importing and exporting datasets, exploratory data analysis, error detection and error imputation. Finally, the area of energy data visualisation will be explored through case studies and best practice discussion.
Learning Outcomes
By the end of the module students should be able to:
Explain the main types of energy data and their characteristics
Explain the main datetime formats found in energy datasets, and their advantages and disadvantages
Explain the main Geospatial formats found in energy datasets, and their advantages and disadvantages
Create a Jupyter notebook to create a visualisation of datetime data and of geospatial data
Provide an evidenced critical opinion on the relative benefits of difference software approaches to data and digitilisation
Explain the main sources of energy data at different geospatial and timeframes
Coursework 1 - Creation of Jupyter notebook as coursework (50%) Coursework 2 – 1500-word self-reflection and wider research on visualisation best practice, and sources of energy data (50%) reassessment: Coursework 1 - Creation of Jupyter notebook as coursework (50%)
