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3130. Day-ahead probability forecasting for redispatch 2.0 measures
Invited abstract in session MB-8: AI in Eco-Finance: Time, Space, and Networks, stream AI & Innovation in Sustainable Finance.
Monday, 10:30-12:00Room: 1020 (building: 202)
Authors (first author is the speaker)
| 1. | Vlad Bolovaneanu
|
| Statistics and Econometrics, Bucharest Academy of Economic Studies |
Abstract
The purpose is to advance a data-driven, day-ahead forecasting model for assessing the probability, direction, and scale (load) of electrical congestions within Germany’s complex electrical power grid. Utilizing state-of-the-art machine learning algorithms, the model is specifically designed to operate on an hourly basis, thereby offering timely insights for grid management. The analysis uncovers compelling evidence that key exogenous variables, such as real-time meteorological conditions, electricity supply-demand indicators, and Brent oil price fluctuations, can be harnessed to make highly reliable predictions concerning grid congestion events. Also, seasonal patterns are uncovered. Our contribution has the potential to serve as a useful resource for transmission system operators (TSOs) and policymakers interested in grid management and cost mitigation efforts.
Keywords
- Robust Optimization
- Energy Policy and Planning
- Electricity Markets
Status: accepted
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