Enhancing Solar Radiation Forecasting with Machine Learning

Abstract

This project aimed to enhance local weather forecasts by improving 1-hour, on-site predictions using the High-Resolution Rapid Refresh (HRRR) dataset. These forecasts can support the high tunnel weather forecast model, providing growers with critical insights to respond to high-temperature events. The project’s objectives included developing a streamlined data preparation process and an on-site predictive model using machine learning (ML). After considering all potential weather variables, our analysis focused on solar radiation intensities exceeding 400 W/m2 during the Northern Hemisphere’s transition periods (March and October). The study used HRRR and observational data from three locations, including Wooster, OH, USA; West Lafayette, IN, USA; and Geneva, NY,USA for model training. Data pr eprocessing, including parsing, time synchronization, format unification, and missing data handling, was managed using Python. The complex meteorological HRRR data, originally in GRIB2 format, was transformed into a more accessible CSV format with selected variables and a significantly reduced file size, making it more usable for high tunnel producers. For the ML model, one neural network architecture effectively served all three locations, suggesting the potential for a generalized model that can be applied across sites at similar latitudes. Among the five input-feature designs, the HRRR forecast variables for the current time and next hour performed the best across all locations. The ML model outperformed HRRR, reducing root mean square error (RMSE) from 114 to 64 W/m2 andmeanerrorfrom34to4W/m2 while improving R2 from 0.47 to 0.67 for Wooster, OH. Similar performance gains were observed at the other locations. These findings support broader agricultural applications, including high tunnels, greenhouses, and outdoor farming.