Due to the precarious underwater environment, many tasks are impossible to carry out with manned vehicles. It is due to the development of autonomous underwater vehicles (AUVs) that exploration, surveillance, and research have been carried out like never before. AUVs are now extensively used for lengthy missions but face challenges in maintaining an accurate position over a larger operational area due to water currents and oceanic complexities. Hence, more sophisticated navigation systems are required for AUVs. As global positioning systems (GPS) and radio signals are attenuated under water, it becomes difficult to navigate subsea areas. In this research work, we propose the accurate position estimation of an AUV by a Kalman filter using an inertial navigation system (INS). In the MATLAB environment, a sample of the INS accelerometer’s data is simulated to determine position from acceleration using the Kalman filter, and the double integration method of determining position is contrasted. Our experimental results imply that our suggested method produces better outcomes.

Solar irradiance is intermittent and nonstationary, creating a need for accurate multi‐horizon forecasts to support reliable grid operation. This study proposes a hybrid empirical mode decomposition–extreme learning machine–transformer (EMD–ELM–TF) framework. EMD separates the global horizontal irradiance (GHI) series into low‐frequency trend and high‐frequency residual components; ELM forecasts the trend component, while a transformer uses the residual history and meteorological covariates to model longer temporal dependencies. The framework was evaluated on 10‐min datasets from Karachi and Islamabad at 1, 3, and 5 h horizons and compared with seven baseline models. LightGBM achieved the lowest 1 h test RMSE, with 40.29 W/m ² for Karachi and 66.66 W/m ² for Islamabad. The proposed model achieved the lowest test RMSE at 3 h (65.95 and 93.61 W/m ² ) and 5 h (75.92 and 121.11 W/m ² ) for Karachi and Islamabad, respectively. Relative to the strongest baseline at the 5 h horizon, these values represent RMSE reductions of 23.9% for Karachi and 13.8% for Islamabad. The results indicate that separately modeling trend and residual components can improve longer‐horizon short‐term forecasting (STF) after site‐specific training. Computational cost and cross‐site transferability require further evaluation.