Accurate photovoltaic (PV) power forecasting is crucial for stable grid integration, particularly under rapidly changing weather conditions. This study presents an ultra-short-term forecasting model that integrates sky imager data and meteorological radar data, achieving significant improvements in forecasting accuracy. By dynamically tracking cloud movement and estimating cloud coverage, the model enhances performance under both clear and cloudy conditions. Over an 8-day evaluation period, the average forecasting accuracy improved from 67.26% to 77.47% (+15%), with MSE reduced by 39.2% (from 481.5 to 292.6), $R^2$ increased from 0.724 to 0.855, NSE improved from 0.725 to 0.851, and Theil’s U reduced from 0.42 to 0.32. Notable improvements were observed during abrupt weather transitions, particularly on 1 July and 3 July, where the combination of MCR and sky imager data demonstrated superior adaptability. This integrated approach provides a robust foundation for advancing ultra-short-term PV power forecasting.

Accurate river flow forecasting is vital for flood warning and water resource management, yet hourly-scale prediction in small catchments remains underexplored despite its importance for rapid response flood control. To address this gap, this study proposes an enhanced temporal attention module xLSTM (TAM-xLSTM) model that combines temporal feature extraction with timestep-level attention to better capture dynamic variations and dependencies. Case studies in the Qiandongnan region demonstrate that TAM-xLSTM substantially outperforms baseline models during wet season forecasting at Panghai Station, reducing RMSE by 9.6%, MAE by 24.1%, and Theil’s U by 6.6%, while increasing NSE by 4.8%. These results highlight the model’s ability to improve short-term river flow prediction in complex mountainous terrain and its potential to support effective flood warning and water resource management.