A Decadal Dataset of Offshore Weather and Normalized Wind–Solar Power Yield for Long-Term Evolution and Capacity Siting Planning in the Beibu Gulf, China

For offshore renewable energy planning and intelligent power management, access to long-term, high-resolution, and physically consistent meteorological and power generation records is essential. Such data supports a wide range of tasks, including resource assessment, hybrid system capacity sizing, grid operation planning, and data-driven forecasting model development. This article presents the construction of a 10-year continuous hourly dataset for 16 deep-sea grid sites in the Beibu Gulf, China, spanning from January 2016 to December 2025. The raw meteorological variables, including 10 m wind speed, wind direction, solar irradiance, and 2 m air temperature, were retrieved from the NASA POWER satellite database and subsequently cleaned using a 24 h periodic substitution algorithm designed to preserve the physical integrity of daily weather cycles. The dataset is organized into two sub-datasets, the Historical Weather Dataset and the Normalized Power Yield Dataset, with the latter providing normalized wind and solar power outputs on a 1.0 per-unit (p.u.) basis derived from a wind turbine power curve model and a PV thermodynamic model. All 32 CSV files are freely accessible online with UTF-8 encoding. The utility of the dataset is illustrated through two representative application cases including offshore site selection with hybrid capacity sizing and physics-informed deep learning forecasting, demonstrating its suitability for both engineering analysis and machine learning model development.