Search

Solar energy is a major type of renewable energy, and its estimation is important for decision-makers. This study introduces a new prediction model for solar radiation based on support vector regression (SVR) and the improved particle swarm optimization (IPSO) algorithm. The new version of algorithm attempts to enhance the global search ability for the PSO. In practice, the SVR method has a few parameters that should be determined through a trial-and-error procedure while developing the prediction model. This procedure usually leads to non-optimal choices for these parameters and, hence, poor prediction accuracy. Therefore, there is a need to integrate the SVR model with an optimization algorithm to achieve optimal choices for these parameters. Thus, the IPSO algorithm, as an optimizer is integrated with SVR to obtain optimal values for the SVR parameters. To examine the proposed model, two solar radiation stations, Adana, Antakya and Konya, in Turkey, are considered for this study. In addition, different models have been tested for this prediction, namely, the M5 tree model (M5T), genetic programming (GP), SVR integrated with four different optimization algorithms SVR-PSO, SVR-IPSO, Genetic Algorithm (SVR-GA), FireFly Algorithm (SVR-FFA) and the multivariate adaptive regression (MARS) model. The sensitivity analysis is performed to achieve the highest accuracy level of the prediction by choosing different input parameters. Several performance measuring indices have been considered to examine the efficiency of all the prediction methods. The results show that SVR-IPSO outperformed M5T and MARS. [ABSTRACT FROM AUTHOR]

Moving military troops, which is critical to tactical success, depends on providing large quantities of fuel. We used simulation to model and analyze the Turkish army's fuel-supply system that consists of sea-going tankers, tank fields, pipelines, and depots. We measured performance of the existing and proposed systems under various scenarios. We developed a simulation optimization model based on a genetic algorithm (GA) to optimize system performance. Based on the results of extensive simulation experiments, we proposed a number of changes. We recommended that the army should open the existing fuel-supply system and that it establish commercial use in peacetime to obtain additional operating revenue. New trigger levels (or fuel-replenishment policies) for wartime allow the fuel-supply system to survive much longer even in the most severe war conditions. These specific recommendations are being considered by the top army officials to obtain benefits worth millions of dollars. [ABSTRACT FROM AUTHOR]