Edited content version 10
10: Day-ahead active losses forecasting
This challenge is about forecasting the transmission losses on the Swiss transmission grid. More specifically, you will be asked to forecast the losses for the next day in hourly resolution. As the transmission system operator in Switzerland, Swissgrid is responsible to procure energy to compensate losses on the transmission grid. Part of the procurement is done one day before the real present time. Based on the day-ahead forecast, we can know how much to procure. More accurate forecasting will help improve the procurement performance and therefore lower transmission system costs.
In this challenge, you’ll practice your machine learning skills in a well-prepared VM data science environment with cleaned training datasets from the energy industry. You will have access to GPU for your machine learning models so you can focus on modelling!
This is a great chance to explore different models and to improve your skills in forecasting.
Context: The active losses on the transmission grid can be influenced by many factors, such as the historical active losses, renewable generation, cross border flows between Switzerland and the neighboring countries, weather, etc. The raw data will be provided as time series data between 2019 and 2021, in hourly resolution.
The following datasets are provided for you to include in the model:
Historical active losses data Solar generation data for Germany and Italy Wind generation data for Germany and Italy Temperature data for Switzerland, Germany, Italy, France Net Transfer Capacity (NTC) between Swissgrid and the neighboring TSOs (NTC is the maximum exchange programme between two areas which is consistent with the security standards of both areas)
Outcome: The well-performed models will be introduced to our internal forecasting process and support us make better decisions in our market operational tasks, and will eventually help us improvement procurement performance and reduce procurement costs. This is your chance to make a real impact!
We also have various experts from different parts of Swissgrid here so its also a chance to get to know us as an employeer.
Challenge Owner: Swissgrid Liu Xiying, Tim Breitenbach, Giulio Ferraris
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