arXiv:2609.39386v1 Announce Type: new Abstract: Pretrained time-series foundation models (TSFMs) are evaluated as forecasters of future values, yet for sparse series many decisions depend only on whi...
arXiv:2609.24444v1 Announce Type: new Abstract: Accurate, reliable, and deployable wind power forecasting is critical for power system dispatch, renewable energy integration, and electricity market o...
arXiv:2611.06130v1 Announce Type: new Abstract: Accurate short-term wind power forecasting is critical for grid stability and electricity trading. While transformer-based deep learning is state of th...
The paper benchmarks a range of AI methods—conventional ML, ensemble learning, deep neural networks, recurrent architectures, Transformers, graph models, and hybrid ensemble deep learning—on renewable energy datasets. Tree ensembles, especially Extra Trees, outperform other models on structured wave energy converter (WEC) layout data, reducing MAE by about 63.7% relative to an MLP baseline. The RF BiLSTM hybrid delivers the best overall forecasting accuracy for wind farm SCADA data, cutting MAE by roughly 75% compared to a standalone LSTM and surpassing STGCN by about 10%.
The paper benchmarks a range of AI methods—conventional ML, ensemble learning, deep neural networks, recurrent architectures, Transformers, graph models, and hybrid ensemble deep learning—on three renewable energy datasets, including large‑scale wave energy converter (WEC) data and wind farm SCADA measurements. Tree ensembles, particularly Extra Trees, outperform traditional ML and neural predictors on structured WEC layout data, achieving a 63.7% MAE reduction over an MLP baseline. Spatial‑temporal graph networks (STGCN) and an RF‑BiLSTM hybrid further improve forecasting accuracy, with the hybrid model reaching an MAE of 150.5 kW, a 75% reduction over a standalone LSTM and 10% better than STGCN. The study concludes that no single architecture dominates; randomized ensembles excel for structured surrogate modeling, graph networks for explicit spatial interactions, and hybrid recurrent ensembles for combined nonlinear tabular and temporal dynamics.
arXiv:2610.31959v1 Announce Type: cross Abstract: Short-term building load forecasting remains challenging because load patterns can change rapidly with weather and operating conditions. Data-driven...
arXiv:2606. 08630v1 Announce Type: cross Abstract: Global wind power capacity, especially in China, is booming, with new farms spanning diverse terrains and climates.
arXiv:2609.08412v1 Announce Type: new Abstract: Machine-learning weather models (MLWMs) now match or outperform operational numerical weather prediction (NWP) at global medium-range forecasting, at f...
arXiv:2608.23058v1 Announce Type: new Abstract: Large language models (LLMs) now support forecasting systems that combine language-based reasoning with temporal data, evidence retrieval, external too...
arXiv:2606. 02849v1 Announce Type: new Abstract: Interval wind speed forecasting is essential for the efficient integration of wind energy into power systems, as it accounts for the inherent uncertainty of wind resources.
arXiv:2604. 22328v2 Announce Type: replace-cross Abstract: Driven by the transition towards a climate-neutral energy system, accurate energy time series forecasting is critical for planning and operations.
UniWind is a wind power forecasting framework that blends physics-informed generation priors with data-driven state-aware corrections. It constructs a site‑calibrated physical prior using a monotone warp and a shared power curve, then refines this prior with a latent state encoder and a state‑aware power corrector guided by pseudo‑labels. Experiments on 24 real‑world datasets show that UniWind achieves strong forecasting accuracy, robustness, and generalization across full‑shot, cross‑farm zero‑shot, and cross‑region zero‑shot scenarios.