Inge van den Ende
At Dexter Energy, Inge, a data scientist, is developing machine learning-powered products for short-term power trading optimization. Involved since the start of the product, she contributes to probabilistic time series forecasts and overall product development.
Sessions
11-30
15:55
30min
Leveraging conformal prediction for calibrated probabilistic time series forecasts to accelerate the renewable energy transition
Inge van den Ende
Probabilistic energy price forecasts can help balance the electrical grid in the face of volatile renewable energy sources, especially when the forecasts are well-calibrated. Conformal prediction can calibrate probabilistic forecasts, producing a distribution with valid coverage in finite samples. This presentation will delve deeper into probabilistic time series forecasting and how to calibrate your forecast.
Bohr