The paper "Temporal Collaborative Attention for Wind Power Forecasting" by Yue Hu, Hanjing Liu, Senzhen Wu, Yuan Zhao, Zhijin Wang, and Xiufeng Liu introduces a novel data-driven method called Temporal Collaborative Attention (TCOAT) for wind power forecasting. The method aims to capture both temporal and spatial dependencies in wind power generation data, as well as long-term and short-term patterns. TCOAT utilizes attention mechanisms to dynamically adjust the weights of each input variable and time step based on their contextual relevance, and employs collaborative attention units (CAUs) to model interactions and correlations among different variables or time steps. A temporal fusion layer integrates long-term and short-term information using concatenation and mapping operations, along with hierarchical feature extraction and aggregation. The effectiveness of TCOAT is validated through extensive experiments on a real-world wind power generation dataset from Greece and compared against twenty-two state-of-the-art methods. The results demonstrate that TCOAT outperforms existing methods in terms of accuracy and robustness, and shows comparable or better performance on an additional dataset from a different climate condition, confirming its generalization ability. The paper also discusses the challenges and limitations of existing methods and highlights the unique contributions of TCOAT in addressing these issues.The paper "Temporal Collaborative Attention for Wind Power Forecasting" by Yue Hu, Hanjing Liu, Senzhen Wu, Yuan Zhao, Zhijin Wang, and Xiufeng Liu introduces a novel data-driven method called Temporal Collaborative Attention (TCOAT) for wind power forecasting. The method aims to capture both temporal and spatial dependencies in wind power generation data, as well as long-term and short-term patterns. TCOAT utilizes attention mechanisms to dynamically adjust the weights of each input variable and time step based on their contextual relevance, and employs collaborative attention units (CAUs) to model interactions and correlations among different variables or time steps. A temporal fusion layer integrates long-term and short-term information using concatenation and mapping operations, along with hierarchical feature extraction and aggregation. The effectiveness of TCOAT is validated through extensive experiments on a real-world wind power generation dataset from Greece and compared against twenty-two state-of-the-art methods. The results demonstrate that TCOAT outperforms existing methods in terms of accuracy and robustness, and shows comparable or better performance on an additional dataset from a different climate condition, confirming its generalization ability. The paper also discusses the challenges and limitations of existing methods and highlights the unique contributions of TCOAT in addressing these issues.