The field of weather forecasting and energy management is experiencing significant developments, driven by the increasing importance of accurate predictions and resilient infrastructure. Researchers are focusing on creating comprehensive benchmarks and datasets to facilitate consistent training and evaluation of models, such as those for regional weather forecasting and electricity forecasting. These efforts aim to address the limitations of existing methods and promote the development of more robust and generalizable models. Notably, innovative loss functions and graph-based approaches are being explored to improve forecasting performance and incident detection. The use of real-world datasets and expert-annotated labels is also becoming more prevalent, enabling the evaluation of model robustness and reliability. Overall, the field is moving towards more advanced and specialized models that can handle complex correlation dynamics and non-stationary data. Noteworthy papers include: IndiaWeatherBench, which provides a comprehensive benchmark for data-driven regional weather forecasting, and Real-E, which offers a foundation benchmark for advancing robust and generalizable electricity forecasting. AT Loss is also notable for introducing a simple penalty expression and reinterpretation as a quadratic unconstrained binary optimization formulation, resulting in a differentiable advanced torrential loss function.