Optimizing short-term active power losses forecast

Optimizing the forecasting based on the results of these models by designing an optimization algorithm

#05

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Optimizing short-term active power losses forecast

Currently we use a group of models to forecast the short-term (day-ahead & intraday) active power losses on the transmission grid, using statistical, machine learning, and deep learning models.

The goal of this challenge is to: Optimizing the forecasting based on the results of these models by designing an optimization algorithm, to minimize the expected forecasting error and the procurement costs Designing the visualization for presenting the final forecasting results and the performance evaluation charts

Challenge slides

🧑🏻‍🏫 Swissgrid AG Liu Xiying

Event finished

17.09.2022 16:00

Joined the team

16.09.2022 12:20 ~ flavio

Event started

16.09.2022 09:00

Ask

08.08.2022 13:36

Edited content version 8

08.08.2022 13:36 ~ darienne_hunziker

Edited content version 6

02.08.2022 14:54 ~ darienne_hunziker

Joined the team

02.08.2022 14:54 ~ darienne_hunziker

Edited content version 3

02.08.2022 14:33 ~ darienne_hunziker

Challenge posted

02.08.2022 14:33 ~ darienne_hunziker
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