Jul 2026· Stochastic environmental research and risk assessment (Print)· Vol 40· 0 citations· 52 references
TL;DR
A Diffusion-Encoder framework is proposed that reformulates forecasting as a conditional diffusion-based generative process and serves as a model-agnostic enhancement layer for existing forecasting architectures, thus providing a practical approach for improving forecasting robustness and data efficiency under complex and uncertain wind conditions.
To address the nonlinear amplification of numerical weather prediction (NWP) errors and the difficult trade-off between coverage and sharpness in short-term offshore wind power forecasting, this paper proposes PRWind, a physics-guided framework for short-term probabilistic forecasting. Built upon a Transformer encoder,...
Xiu-Yong Zhao, Hai-Chuan Long, Kai-Ze Liu et al.· Atmosphere· 0 citations
The findings highlight the value of training strategies that allow models to directly learn bias correction during forecast transitions, emphasize the operational potential of combining sequential processing with near real-time discharge observations and identify physiographic catchment characteristics as key modulator...
O. Konold, Moritz Feigl, Patrick Podest et al.· Hydrology and Earth System S...· 3 citations
The integration of physics-guided constraints with the temporal convolutional architecture significantly enhances prediction accuracy, stability, and generalization capability, making it suitable for real-time wind energy forecasting applications, intelligent energy management systems, and microgrid power system operat...
S. Marisargunam, T. Mariprasath, Mohit Bajaj et al.· Energy Exploration & Exp...· 0 citations
WDANet, a frequency-aware forecasting framework that integrates stationary wavelet decomposition, a Feature-wise Linear Modulation strategy, and a dual-branch encoder-decoder architecture, enabling separate modeling of trend and fluctuation components is proposed, highlighting its potential for offshore wind power oper...
Xue-Fei Wang, Tingting Liu, Heng Zhang et al.· 0 citations
To address the substantial increase in wind power forecasting errors under stable weather conditions, this paper examines a typical wind farm in Xinjiang and systematically analyzes the uncertainty mechanism through which the power curve nonlinearly amplifies wind speed forecasting errors. On this basis, a multimodule...
Guo-Qing Li, Bin Zhang, Da-Gui Liu et al.· Archives des sciences: a mul...· 0 citations
The analysis reveals a transition from deterministic DI models to hybrid, physics-informed, uncertainty-aware, and operational forecasting systems that increasingly integrate AI with physical knowledge and heterogeneous environmental observations.
Braiton U. Mukhalela, S. Viriri, D. Ndzi et al.· Frontiers in Artificial Inte...· 0 citations
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