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InnoBlog: Data science for energy engineers
One of the key skills to emerge in the 21st century – Data Science. You might have already heard Wired encouraging parents “Tell Your Kids to Be Data Scientists, Not Doctors”. Starting from the nice salary perk and future career sector flexibility, that’s right, data science applications are found in a very broad range of product/services from finance to the gaming industries.
Versatile skill.
Energy engineers are no exception, this skill is so versatile that it is predicted that large number of jobs will require at least basic understanding of data science in the future. In the energy sector data science is becoming more and more relevant due to actual and future computational power, data availability, and architecture evolution.
Recently, EIT InnoEnergy students were offered the possibility to join a data science online course specifically designed for energy engineers. Based on my experience in this course, I have decided to list at least few cases where this skill will play a role:
- Optimising energy production (O&G, renewables, etc.)
- National electricity market operation
- Forecasting future energy demand
- Detecting energy consumption patterns
- Analysis of equipment operation/status/failure
- Data-driven demand side management (DSM)
The last concept in particular has a number of benefits: thermal storage management, demand reduction and response management, electric storage optimisation and automation among others!
Why take a course in Data Science?
Current trends and challenges in data science demand the incorporation of qualified and innovative minds. Among these topics are smart metering, Big Data, Internet of Things, Cloud computing, privacy and security issues, and uncertainty and precision(1). In addition, Harvard Business Review called data science the “sexiest job of the 21st century”.
Above reasons drove me to embark on the journey of “Data Science for Energy Engineers: the case of demand side management” course.
As a side note, it is worth sharing that apart from this course as an EIT InnoEnergy student you receive complimentary access to Professional Learning courses so make sure to make the most of it!
Back to the data science. I saw it as a perfect opportunity to start the data science journey specifically if you have no practical knowledge (which is my case) or simply want to improve your skills in the subject.
The layout of the course.
The adventure begins with a Python refresher This part consisted of several activities to give you a solid foundation to the rest of the course with the occasion to test your knowledge after each section. Moreover, these online sessions are conducted by an expert! As a result, you are able to cover the following questions (to name only a few) in combination with practical exercises in Python using Jupyter notebooks:
- How to access data and different data formats?
- What to do with missing and erroneous data?
- How to perform exploratory data analysis
- How to make forecasts for the future?
- How to measure forecast accuracy?
- What to do when forecasts are not accurate enough?
Additional benefits obtained from the course are DataCamp licenses and the Kaggle competition with several prizes for the winner!
This course is designed for professionals, so it was not only attended by EIT InnoEnergy students. For me this was an excellent opportunity to connect not only to one of the biggest fields in energy engineering, but also with one of the most interesting and relevant work area in the near future!
(1) Molina-Solana, M., et al. Data science for building energy management: A review. Renewable and Sustainable Energy Reviews, Vol. 70, 2017, 598-609.
By David Alvarez, EIT InnoEnergy Master School student
Interested in data science within energy storage? Join our current student on 9 of October for InnoTalks: Master’s in Energy Storage edition to learn about the programme as ask any questions that you might have about it. Register here.
