Reviewing methods and assumptions for high-resolution large-scale onshore wind energy potential assessments

no code implementations • 17 Mar 2021 • Russell McKenna, Stefan Pfenninger, Heidi Heinrichs, Johannes Schmidt, Iain Staffell, Katharina Gruber, Andrea N. Hahmann, Malte Jansen, Michael Klingler, Natascha Landwehr, Xiaoli Guo Larsén, Johan Lilliestam, Bryn Pickering, Martin Robinius, Tim Tröndle, Olga Turkovska, Sebastian Wehrle, Jann Michael Weinand, Jan Wohland

For onshore wind energy this has resulted in an active research field devoted to analysing these resources for regions, countries or globally.

Enhanced spatio-temporal electric load forecasts using less data with active deep learning

1 code implementation • 8 Dec 2020 • Arsam Aryandoust, Anthony Patt, Stefan Pfenninger

We show how electric utilities can apply active learning to better distribute smart meters and collect their data for more accurate predictions of load with about half the data compared to when applying passive learning.

Active Learning BIG-bench Machine Learning +2