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

Reliable global solar radiation (R) information is crucial for the design and management of solar energy systems for agricultural and industrial production. However, R measurements are unavailable in many regions of the world, which impedes the development and application of solar energy. To accurately estimate R, particle swarm optimization (PSO) algorithm integrating Gaussian exponential model (GEM) was proposed for estimating daily and monthly global R in Northeast China.

Simulating reference crop evapotranspiration with different climate data inputs using Gaussian exponential model.

Obtaining accurate data on reference crop evapotranspiration (ET) is important for agricultural water management. A novel Gaussian exponential model (GEM) was developed in this study to predict ET with limited climatic data. The GEM was further compared with the M5 model tree (M5T), extreme learning machine (ELM), and boosted trees (BT) model under local and regional scenarios.