Motonobu Kanagawa

Machine-learning emulators for greenhouse gas retrievals can falter over time, but a simple Lasso model outperforms complex methods by incorporating temporal features.

Ignoring missing data in PV forecasting leads to overconfident predictions, but this work shows how to correct that by explicitly modeling imputation uncertainty.