arXiv:2607. 14024v1 Announce Type: cross Abstract: With rising global energy demand and growing awareness of climate change and its impacts, the share of renewable energies in the global energy mix continues to grow.
With rising global energy demand and growing awareness of climate change and its impacts, the share of renewable energies in the global energy mix continues to grow. Unlike conventional power generation, the output of renewable energy sources cannot be controlled as consistently due to their dependence on environmental conditions.
arXiv:2609.06194v1 Announce Type: new Abstract: Offshore wind turbines are widely used to generate renewable energy, but their maintenance can result in decreased efficiency due to forced shutdowns....
The paper presents a machine‑learning based climate classification tailored for photovoltaic (PV) modules, incorporating both energy yield and module lifetime with climate‑dependent degradation. Using an interpolated dataset of twelve input features, the authors identify annual global horizontal irradiation and ambient temperature as the most influential predictors, achieving RMSEs of 0.007 MWh for yield and 1.5 years for lifetime. The resulting hierarchical clustering yields six primary climate clusters (Tropical, Desert, Continental, Temperate, Boreal, Polar) and 15 subclusters, with the low‑temperature continental climate delivering the highest discounted lifetime energy yield.
Offshore wind turbines are widely used to generate renewable energy, but their maintenance can result in decreased efficiency due to forced shutdowns. Accurate wind turbine power predictions can ident...
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: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.
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:2606. 14707v1 Announce Type: cross Abstract: AI training and deployment consume substantial electricity, but carbon outcomes remain weakly integrated into routine model development decisions.
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.
PROSWIN is a probabilistic machine learning model that forecasts hourly solar wind speed at Earth up to four days ahead, using solar images and magnetograms processed by a deep neural network and distributional regression. It introduces a prediction score metric that rewards both timeline and high‑speed solar wind peak accuracy, achieving well‑calibrated uncertainties and superior performance on 14 years of data compared to existing models. The study highlights the importance of the 171 Å channel and demonstrates that probabilistic forecasts outperform single‑value models for both overall timelines and peak events.
arXiv:2505. 05203v3 Announce Type: replace-cross Abstract: With the increasing penetration of renewable energy and inverter-based resources, traditional physics-based power-system operation faces growing challenges in maintaining economic efficiency, security, and robustness.
arXiv:2510. 15780v2 Announce Type: replace-cross Abstract: Artificial intelligence (AI) is increasingly used to support renewable energy forecasting and grid operations.