Zoom Link
https://hku.zoom.us/j/95263844640
Abstract
Wind power forecasting remains highly challenging due to the strong nonlinearity of atmospheric dynamics,pronounced diurnal regime differences, and substantial uncertainties in multi-source meteorological data. Conventional black-box machine-learning models mainly rely on observational correlations, often neglecting physical constraints and causal mechanisms, which leads to limited interpretability and poor robustness under distribution shifts. In this seminar, we proposes a unified forecasting framework that integrates physics-constrained data construction, multi-site causal structure learning, and a global nonlinear structural equation model (NLSEM). The framework combines full-variable Granger causality networks with PCMCI+ to identify distinct day and night-time causal directed acyclic graphs (DAGs). Within the NLSEM, physical monotonicity, environmental invariance, and counterfactual-consistency regularization are explicitly enforced. The resulting model supports causal inference through dointervention analysis, ATE/CATE estimation, and counterfactual reasoning. Experiments conducted on three coastal wind farms demonstrate consistent performance improvements over strong machine-learning baselines, while revealing physically meaningful causal drivers of wind-power generation.
Speaker
Mr. Yuxuan WANG
Department of Electrical and Computer Engineering
The University of Hong Kong
Biography of the Speaker
Yuxuan Wang received the B.S. degree in New Energy Science and Engineering from Huazhong University of Science and Technology, and the M.S. degree in electrical engineering from University of Leeds. He is currently pursuing the Ph.D. degree in electrical and electronic engineering at the Department of Electrical and Electronic Engineering, The University of Hong Kong. His current research interests include wind power forecasting, causal inference, and nonlinear modeling for renewable energy systems.
Organiser
Prof. Yunhe HOU
Department of Electrical and Computer Engineering, The University of Hong Kong
All are welcome.