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