Building air condition and mechanical ventilation (ACMV) systems which provide cooling operations suffers from the balance between thermal comfort (TC) and energy consumption (EC). This dissertation proposes a bilayer stochastic multi-objective optimization model that addresses the TC and EC trade-off by maximizing TC in the upper layer. The lower layer can help the ACMV system operate at an optimal frequency to reduce EC. To simultaneously determine the optimal TC and EC, the model is solved using an artificial neural network coupled with a multi-objective whale optimization algorithm. The contribution and novelty of this study pivots on proposing the joint optimal operating condition for TC and component operating frequencies for the energy conservation of the ACMV system. The present model successfully built and resolved the difficulties of the ACMV system against outdoor and indoor uncertainty in a building.

Building air condition and mechanical ventilation (ACMV) systems which provide cooling operations suffers from the balance between thermal comfort (TC) and energy consumption (EC). This dissertation proposes a bilayer stochastic multi-objective optimization model that addresses the TC and EC trade-off by maximizing TC in the upper layer. The lower layer can help the ACMV system operate at an optimal frequency to reduce EC. To simultaneously determine the optimal TC and EC, the model is solved using an artificial neural network coupled with a multi-objective whale optimization algorithm. The contribution and novelty of this study pivots on proposing the joint optimal operating condition for TC and component operating frequencies for the energy conservation of the ACMV system. The present model successfully built and resolved the difficulties of the ACMV system against outdoor and indoor uncertainty in a building.

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