The paper presents a tailored GradientSHAP method to explain multivariate, multistep wind power forecasts, addressing the opacity of AI models in renewable energy. It introduces new visualizations that reveal local and global relationships among environmental factors, site conditions, and time dynamics. The study claims to be the first to apply GradientSHAP to wind power forecasting, highlighting its potential for day‑ahead grid management while noting implementation challenges.
arXiv:2307. 07191v3 Announce Type: replace Abstract: Energy forecasting is crucial for the power grid, but fundamentally different from general time series analysis: it highly relies on covariates like meteorological factors, and its goals must align with actual power grid operations, such as risk assessment and system reliability.
arXiv:2510. 15780v2 Announce Type: replace-cross Abstract: Artificial intelligence (AI) is increasingly used to support renewable energy forecasting and grid operations.
Engineering use of AI forecasting models requires not only high nominal accuracy but also predictable behavior under uncertain inputs. In photovoltaic (PV) forecasting, this requirement is especially challenging because numerical weather prediction (NWP) errors are temporally correlated, state dependent, and physically coupled across variables.
arXiv:2611.08245v1 Announce Type: new Abstract: Wind farm power control can help the system maintain balance and avoid blackouts. In Europe, this can be achieved through implicit balancing, where a w...
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:2607. 12954v1 Announce Type: cross Abstract: Engineering use of AI forecasting models requires not only high nominal accuracy but also predictable behavior under uncertain inputs.
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.
arXiv:2608. 02088v1 Announce Type: new Abstract: Reliable photovoltaic (PV) forecasts are needed for low-carbon energy systems, but newly deployed sites often have short, imperfect records.
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...
arXiv:2610.27155v1 Announce Type: new Abstract: Accurate multi-horizon photovoltaic (PV) power forecasting is essential for grid stability, energy scheduling, and the efficient operation of PVbattery...
arXiv:2604. 26634v2 Announce Type: replace Abstract: Norway's electricity market is heavily dominated by hydropower, but the 2021-2022 energy crisis and stronger integration with Continental Europe have fundamentally altered price formation, reducing the reliability of forecasting models calibrated on historical data.
arXiv:2607. 01670v1 Announce Type: new Abstract: Day-ahead wind power forecasting is essential for cost-effective power-system operation.