New sunshine-based models for predicting global solar radiation using PSO (particle swarm optimization) technique

A support vector machine–firefly algorithm-based model for global solar radiation prediction

“…The statistical analysis results indicated a good correlation between estimated values by the ANN model and the actual data with higher accuracy than other empirical models. Behrang et al (2011) Benghanem et al (2009;Ornella and Tapia, 2010) developed six ANN-based models to estimate horizontal global solar radiation at Al-Madinah in Saudi Arabia. They utilized different combinations of input parameters consisting sunshine hours, ambient temperature, relative humidity and the day of year.…”

Using measured daily meteorological parameters to predict daily solar radiation

“…To this aim, several empirical methods have been developed to avoid performing costly in-situ solar radiation measurements [5,6]. Some of the well-known methods in this area are auto-regression, Markov chain, or robust optimization techniques [7][8][9]. Among the empirical methods, machine learning has been widely used to solve real world problems [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28].…”

Particle Swarm Optimization Assisted by Gaussian Processes for Multimodal Function Optimization

“…It is proved that stochastic search methods can solve multimodal optimization problem. Such as genetic algorithms (GA) [3,4],differential evolution (DE) [5,6] particle swarm optimization (PSO) [7,8] and ant colony optimization (ACO) [9,10]. These methods evaluate the objective function in a random sample of points from the search space and subsequently manipulate the sample.…”