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From the perspective of system control, the smart and green building is a large-scale dynamic system with high complexity and huge amount of information. Proper combination of the available information and effective control of the overall building system turn out to be a big challenge. In this study, we proposed a building indoor energy and comfort management model based on information fusion using...
Central Force Optimization (CFO) is a powerful new metaheuristic algorithm that has been demonstrated to be competitive with other metaheuristic algorithms such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Group Search Optimization (GSO). While CFO often shows superiority in terms of functional evaluations and solution quality, the algorithm is complex and often requires increased...
In this paper a fuzzy adaptive comfort temperature (FACT) model has been proposed for the intelligent control of smart buildings. A multi-agent control system is applied for the energy management and building operation. Particle Swarm Optimization (PSO) is applied to optimize the set points based on the comfort zone. Integrating a grey predictor to predict outdoor temperature with the FACT model shows...
Transmission planning for electric power systems is a challenging task due to the problem complexity. When system reliability is considered, transmission planning becomes even more complicated. In the recent years, significant amounts of wind power are being integrated into the conventional power systems. Wind power is environmentally benign, but it is intermittent since its availability cannot be...
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