Particle swarm optimization algorithm with Gaussian exponential model to predict daily and monthly global solar radiation in Northeast China

Reliable global solar radiation (Rs) information is crucial for the design and management of solar energy systems for agricultural and industrial production. However, Rs measurements are unavailable in many regions of the world, which impedes the development and application of solar energy. To accurately estimate Rs, particle swarm optimization (PSO) algorithm integrating Gaussian exponential model (GEM) was proposed for estimating daily and monthly global Rs in Northeast China. The PSO- GEM was compared with four other machine learning models and two empirical models to assess its applicability using daily meteorological data from 1997 to 2016 from four stations in Northeast China. The results showed that in different stations, the PSO- GEM with full climatic data as inputs showed the highest accuracy to estimate daily Rs with RMSE, RRMSE, MAE, R-2, and -Ens values of 1.045-1.719 MJ m(-2) d(-1), 7.6-12.7%, 0.801-1.283 MJ m(-2) -d(-1), 0.953-0.981, and 0.946-0.977, respectively. The PSO- GEM showed the highest accuracy to estimate monthly Rs with RMSE, RRMSE, MAE, R-2, and -Ens values of 0.197-0.575 MJ m(-2) -d(-1), 1.5-7.0%, 0.137-0.499 MJ m(-2) d(-1), 0.999-1, and 0.992-0.999, respectively. Overall, the PSO-GEM had the highest accuracy under different inputs and is recommended for modeling daily and monthly Rs in Northeast China.

Automated summary

Reliable solar radiation information is crucial for designing and managing solar energy systems in agriculture and industry. However, measurements are lacking in many regions, which hampers the development and application of solar energy. This study proposes a particle swarm optimization and Gaussian exponential model for accurately estimating solar radiation in Northeast China and compares it with other models. The results show that this method has the highest accuracy and is recommended for modeling solar radiation in the region.