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• W2905641663 abstract "There is growing concern about how to mitigate climate change in which the reduction of CO2 emissions plays an important role. Buildings have gained attention in recent years since they are responsible for around 30% of greenhouse gases. In this context, advance control strategies to optimize HVAC systems are necessary because they can provide significant energy savings whilst maintaining indoor thermal comfort. Simulation-based model predictive control (MPC) procedures allow an increase in building energy performance through the smart control of HVAC systems. The paper presents a methodology that overcomes one of the critical issues in using detailed building energy models in MPC optimizations—computational time. Through a case study, the methodology explains how to resolve this issue. Three main novel approaches are developed: a reduction in the search space for the genetic algorithm (NSGA-II) thanks to the use of the curve of free oscillation; a reduction in convergence time based on a process of two linked stages; and, finally, a methodology to measure, in a combined way, the temporal convergence of the algorithm and the precision of the obtained solution." @default.
• W2905641663 created "2019-01-01" @default.
• W2905641663 creator A5024011589 @default.
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• W2905641663 date "2018-12-23" @default.
• W2905641663 modified "2023-10-05" @default.
• W2905641663 title "Model Predictive Control Optimization via Genetic Algorithm Using a Detailed Building Energy Model" @default.
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• W2905641663 doi "https://doi.org/10.3390/en12010034" @default.
• W2905641663 hasPublicationYear "2018" @default.
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