Understanding building operation from semantic context
Anika Schumann, Joern Ploennigs, et al.
IECON 2015
This study investigates near-shore circulation and wave characteristics applied to a case-study site in Monterey Bay, California. We integrate physics-based models to resolve wave conditions together with a machine-learning algorithm that combines forecasts from multiple, independent models into a single “best-estimate” prediction of the true state. The Simulating WAves Nearshore (SWAN) physics-based model is used to compute wind-augmented waves. Ensembles are developed based on multiple simulations perturbing data input to the model. A learning-aggregation technique uses historical observations and model forecasts to calculate a weight for each ensemble member. We compare the weighted ensemble predictions with measured data to evaluate performance against present state-of-the-art. Finally, we discuss how this framework that integrates data-driven and physics-based approaches can outperform either technique in isolation.
Anika Schumann, Joern Ploennigs, et al.
IECON 2015
Stefan Wolff, Fearghal O'Donncha, et al.
Journal of Marine Systems
Francesco Fusco, Pascal Pompey, et al.
EDBT/ICDT 2016
Mathieu Sinn, Bei Chen
NeurIPS 2012