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Foundation models for weather and climate data understanding: A comprehensive survey
As artificial intelligence (AI) continues to rapidly evolve, the realm of Earth and atmospheric
sciences is increasingly adopting data-driven models, powered by progressive …
sciences is increasingly adopting data-driven models, powered by progressive …
Adversarial discriminative domain adaptation for solar radiation prediction: A cross-regional study for zero-label transfer learning in Japan
Deep learning models are increasingly applied in the field of solar radiation prediction.
However, the substantial demand for labeled data limits their rapid application in newly …
However, the substantial demand for labeled data limits their rapid application in newly …
Short-term wind power forecasting based on multi-scale receptive field-mixer and conditional mixture copula
J Li, J Chen, Z Chen, Y Nie, A Xu - Applied Soft Computing, 2024 - Elsevier
Integrating renewable wind power (WP) into the grid exacerbates variability and challenges
reliability. Establishing an effective forecasting system is crucial for risk avoidance, but …
reliability. Establishing an effective forecasting system is crucial for risk avoidance, but …
Solutions to the insufficiency of label data in renewable energy forecasting: A comparative and integrative analysis of domain adaptation and fine-tuning
The prediction of renewable energy plays a critical role in optimizing the operation, fault
diagnosis, and other essential tasks within its energy system. Given the scarcity of labeled …
diagnosis, and other essential tasks within its energy system. Given the scarcity of labeled …
Harnessing AI for solar energy: Emergence of transformer models
This review emphasizes the critical need for accurate integration of solar energy into power
grids. It meticulously examines the advancements in transformer models for solar …
grids. It meticulously examines the advancements in transformer models for solar …
[HTML][HTML] A revolutionary neural network architecture with interpretability and flexibility based on Kolmogorov–Arnold for solar radiation and temperature forecasting
Deep learning models are increasingly being used to predict renewable energy-related
variables, such as solar radiation and outdoor temperature. However, the black-box nature …
variables, such as solar radiation and outdoor temperature. However, the black-box nature …
Harnessing a better future: exploring AI and ML applications in renewable energy
Integrating machine learning (ML) and artificial intelligence (AI) with renewable energy
sources, including biomass, biofuels, engines, and solar power, can revolutionize the …
sources, including biomass, biofuels, engines, and solar power, can revolutionize the …
On the use of sky images for intra-hour solar forecasting benchmarking: Comparison of indirect and direct approaches
The transient stability of the grid is challenged by short-term photovoltaic output fluctuations,
which are mainly caused by local clouds. To address this issue, intra-hour solar forecasting …
which are mainly caused by local clouds. To address this issue, intra-hour solar forecasting …
AI-driven psychological support and cognitive rehabilitation strategies in post-cancer care
This article examines the impact of Artificial Intelligence (AI) on the comprehensive
rehabilitation of post-cancer patients, specifically in the areas of psychological support and …
rehabilitation of post-cancer patients, specifically in the areas of psychological support and …
CRAformer: a cross-residual attention transformer for solar irradiation multistep forecasting
In recent years, solar energy has gained widespread adoption in smart grids due to its
safety, environmental friendliness, abundance, and other advantages, driving the …
safety, environmental friendliness, abundance, and other advantages, driving the …