Master Thesis Extreme Event Forecasting using Deep Learning

Extreme Event Forecasting using Deep Learning

Are you a master student planning to write your Master Grade during Spring 2021.

Join us on our journey #SiemensEnergy

Siemens Energy’s 91,000 employees around the world are passionately pursuing one common goal: to energize society with affordable, reliable, and sustainable energy systems.

Join our great team and put your energy to use.

We are now looking for a student to take on the assignment “Extreme Event Forecasting using Deep Learning”

The assignment:

At Siemens Energy, event forecasting enables a broad spectrum of digital products and services. Multiple applications are powered by time series forecasting algorithms that help us to predict anticipated user demands or that help our customers to predict important parameters for their daily power plant operation.

Extreme events—such as holidays, inclement weather, transmission lines breakdowns, natural disasters, pandemics, military conflicts, power plant outages can drive considerable changes in electricity and fuel prices as well as country electric demand. Calculating commodity prices forecasting during extreme events is a critical component of anomaly detection, optimal resource allocation, and budgeting.

Although extreme event forecasting is a crucial piece of power plant operations, data sparsity makes accurate prediction challenging. Usually there are much more normal values than extreme events so the number of samples to train a model to reproduce this behavior is very limited. In addition to the lack historical data, extreme event prediction also depends on numerous external factors that sometimes are not available in all the countries where our customer operates

In this project your main goal would be to re-use our previous developed methodology for time series anomaly detection in combination with time series forecasting algorithms to develop and implement a framework to forecast extreme events in electricity prices. Your tasks will be:
* Literature Review of existing methodologies.
* Explore, process & understand the data available for this MSc thesis project.
* Develop a baseline forecasting model and understand the business impact of the extreme events in the model.
* Link the previous existing work in anomaly detection with your existing dataset
* Improve the baseline forecasting model according to the findings in your literature review.
* Validate the methodology with additional time series.

Your profile?
* The project is suitable for a student with academic background in energy systems, engineering, computer science, statistics, mathematics or another relevant field.
* As a student you have strong analytical skills and solid mathematical background.
* Besides, you are interested in data analytics (especially in prescriptive analytics) and you must have programming skills (preferable: Python, R or Julia).
* We consider meritorious skills the knowledge of machine learning/deep learning-oriented libraries (scikit-learn, caret, mlr, keras, tensorflow, pytorch etc…), data handling libraries (Pandas or tidyverse).
* We consider meritorious a GitHub record with deep learning projects.
* We consider meritorious Open-Source contributions.
* Examples on relevant words: energy, inspiration, leader, motivated, successful, team

Why should you be working at Siemens?

Siemens Energy is one of the world’s leading energy technology companies. The company works with its customers and partners on energy systems for the future, thus supporting the transition to a more sustainable world. With its portfolio of products, solutions and services, Siemens Energy covers almost the entire energy value chain – from power generation and transmission to storage. The portfolio includes conventional and renewable energy technology, such as gas and steam turbines, hybrid power plants operated with hydrogen, and power generators and transformers. More than 50 percent of the portfolio has already been decarbonized. A majority stake in the listed company Siemens Gamesa Renewable Energy (SGRE) makes Siemens Energy a global market leader for renewable energies. An estimated one-sixth of the electricity generated worldwide is based on technologies from Siemens Energy. Siemens Energy employs 91,000 people worldwide in more than 90 countries and generated revenue of around €29 billion in fiscal year 2019. In Sweden Siemens Energy has 2600 employees in 10 locations.

At Siemens we value diversity by inclusion and by cooperating with people with different mindset, background, experience, competence and personal traits – in all organisational levels.

Read more about Siemens here:

www.siemens-energy.com.

Application

Do not hesitate - apply today via https://jobs.siemens-energy.com/jobs refnr 221354 and no later than November 6^th. For questions about the role please contact recruiting manager Ronny Norberg on +46 (122) 82304.

For questions about the technicalities of the projects please contact edgar.bahilo_rodriguez@siemens.com or mohamed-ahmed@siemens.com

Place of work: Finspång

Trade Union representatives:
Veronica Andersson, Unionen, 0122-840 21
Simon Von Eckardstein, Sveriges Ingenjörer, 0122-842 24
Jan Lundgren, Ledarna, 0122-812 33
Jonny Persson, IF Metall, 0122-817 69