"Curated Meteorological Dataset for Solar irRadiation Prediction at our geospatial context"

"This dataset presents a processed, machine-learning-ready compilation of hourly time-series meteorological parameters for the purpose of short-term rooftop solar energy prediction. Sourced from public archives of the NASA POWER Project and NOAA, the raw data was rigorously cleaned, merged, and structured.  Key parameters include  a. Solar  Irradiance (Wh\/m\u00b2) \u2013Target variableb. Temperature (\u00b0C)c. Surface Pressure (kPa)d. Specific Humidity  (g\/kg)e. Relative Humidity (%)f. Wind Speed (m\/s)g. Wind Direction (degrees)h. Year, Month, Day, and Hour (used to derive Unix Timestamp)i. Sunrise Time (hh\\:mm)j. Sunset Time (hh\\:mm)k. UNIXTime (seconds), geographically located at Latitude: [19.8797\u00b0 N ], Longitude: [75.3559\u00b0 E] from [30-12-2015 13:30] to [01-07-2023 04:30]. A critical derived feature is the  'Irradiance' column is the target variable (y) for supervised learning tasks. All other columns (e.g., temperature, Temperature, RHumidity,    WindSpeed,WindDirection,Pressure,SHumidity,Hours_of_light,Rel_time) are features (X).  The dataset is curated to enhance predictive modeling and provided in a simple CSV format, This resource is intended to serve as a benchmark for developing, training, and validating scalable and efficient solar forecasting models, ensuring full reproducibility of research built upon it."