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A methodology called Intelligent State Space Pruning (ISSP) has recently been developed and applied in order to reduce the computational resources necessary to achieve convergence when using non-sequential Monte Carlo Simulation (MCS). The main application of this algorithm has been the probabilistic evaluation of composite power system reliability. ISSP has been shown to perform differently when...
Climate change is a matter of pressing importance for modern society. One method that has been suggested for mitigating the role that fossil fuel based transportation plays in this issue is the introduction of Plug-in Hybrid Electric Vehicles (PHEVs) in order to electrify the transportation sector. While these vehicles would have a significant impact in reducing greenhouse gases (GHGs), they may also...
The probabilistic reliability evaluation of composite power systems is a complicated and computation intensive task. Monte Carlo Simulation (MCS) is often used as the method of choice for tackling this difficult problem, though MCS may also suffer from issues regarding high dimensionality leading to an increased need for computational resources. In order to address this issue an algorithmic method...
Work has recently been completed that improves the computational aspects of Monte Carlo simulation (MCS) including its total computational time and iterations required for convergence through the use of a novel technique known as state space pruning. This methodology currently exists in two distinct flavors: The analytical method and a method built on Population-based Intelligent Search (PIS) techniques...
State space pruning is a methodology that has been successfully applied to improve the computational efficiency and convergence of Monte Carlo Simulation (MCS) when computing the reliability indices of power systems. This methodology increases performance of MCS by pruning state spaces in such a manner that a conditional state space with a higher density of failure states than the original state space...
In this paper, a new control system with an intelligent optimizer is developed which can be applied to energy and comfort management in the smart and energy-efficient buildings. Hierarchical multi-agent theory is used to build this control system, which contains agent-controllers at two levels - a central coordinator-agent at the higher level and multiple local controller-agents at the lower level...
One methodology that has been previously developed to improve the computational efficiency and convergence of Monte Carlo Simulation (MCS) when computing the reliability indices of power systems is a technique known as state space pruning. This technique works by pruning the state space in such a way that the MCS samples a state space that has a higher density of failure states than the original state...
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