Solar Irradiation Forecasting Dataset (SURFRAD, 2018–2020): Reproducible Inputs for GA-Optimized Machine Learning Models

This repository contains the solar irradiation and meteorological data derived from the U.S. SURFRAD network for the years 2018–2020. The dataset supports the study “Solar Irradiation Forecasting Using Genetic Algorithms: GA-Optimized Machine Learning Models for Short-Term Solar Irradiation Prediction” and is designed to enable full reproducibility of all results reported in the manuscript. To reduce storage footprint and improve reproducibility, quality-control (QC) flags, redundant time fields, and unused meteorological variables were removed prior to analysis. Only variables explicitly used in feature selection, modeling, tables, and figures were retained. The dataset includes inputs required for Linear Regression (LR), Extreme Gradient Boosting (XGB), and Genetic Algorithm–optimized XGB models, with a strict temporal split: – Training data: 2018–2019 – Validation data: 2020 All data are observational, non-proprietary, and intended solely for methodological demonstration, benchmarking, and reproducibility.