A Multi-Directional Pyranometer (CUBE-i) for Real-Time Direct and Diffuse Solar Irradiance Decomposition

Abstract

Conventional decomposition models (empirical and numerical decomposition models) estimate direct normal irradiance (DNI) and diffuse horizontal irradiance (DHI) from global horizontal irradiance (GHI) based on empirical correlations or physical equations. These models are designed for long-term averaged data, typically at an hourly or longer timescale, making them less suitable for real-time estimations with shorter time intervals. To address this limitation, this study applies a data-driven approach utilizing multi-directional irradiance measurements and develops a DNI estimation model based on a Deep Neural Network (DNN). The proposed CUBE-i system estimates DNI using irradiance measurements from five directional pyranometers. The measurement data were obtained from the NREL site in Golden, Colorado, USA. The proposed method demonstrates high estimation accuracy at a 1 min resolution, achieving R2 = 0.997 and RMSE = 20.2 W/m2. Furthermore, in estimating both direct and diffuse irradiance on a horizontal plane, the model outperforms conventional empirical decomposition models (Erbs, Reindl, Watanabe), achieving up to five times lower RMSE and higher R2 values. While further considerations regarding sensor accuracy, applicability to different regions, and installation requirements are necessary, this study validates the feasibility of real-time DNI estimation using a compact and cost-effective pyranometer system. This advancement enhances its potential for widespread applications in solar energy systems, building energy management, meteorology, and environmental research.