Abstract
This paper proposes a parallel grid search algorithm to find an optimal operating point for minimizing the power consumption of an experimental heating, ventilating and air conditioning (HVAC) system. First, a multidimensional, nonlinear and non-convex optimization problem subject to constraints is formulated based on a semi-physical model of the experimental HVAC system. Second, the optimization problem is parallelized based on Graphics Processing Units to simultaneously compute optimization loss functions for different solutions in a searching grid, and to find the optimal solution as the one having the minimum loss function. The proposed algorithm has an advantage that the optimal solution is known with evidence as to the best one subject to current resolutions of the searching grid. Experimental studies are provided to support the proposed algorithm.
Highlights
Heating, ventilation, and air conditioning (HVAC) systems are usually comprised of the heating, air conditioning, and ventilation systems in residential or commercial buildings, to provide thermal comfort and air quality in indoor spaces [1,2]
The power consumption optimization is usually composed by two steps: (i) an analytical or numerical model is built to describe the relationship between the input and output variables of the HVAC system; (ii) an optimal operating point of the HVAC system is found to achieve the minimum power consumption subject to some constraints
This paper proposes a parallel grid search algorithm to find a global solution for the power consumption optimization problem of an experimental HVAC system
Summary
Ventilation, and air conditioning (HVAC) systems are usually comprised of the heating, air conditioning, and ventilation systems in residential or commercial buildings, to provide thermal comfort and air quality in indoor spaces [1,2]. Lu et al [5,6] presented a modified genetic algorithm (GA) for minimizing power consumption of HVAC systems, based on mathematical models of major components and their heat exchangers. Libraries [13] to model the overall HVAC system for a local subway station, and took the robust evolutionary algorithm (REA) to optimize the energy consumption of the HVAC simulation models. Wei et al [19] constructed a quad-objective optimization problem to balance the energy consumption and the indoor air quality, and a modified particle swarm optimization algorithm (PSO) to minimize the total energy consumption of a dynamic overall HVAC systems. Kim et al [20] developed an integrated meta-model for a lighting, heating, ventilating, and air conditioning system, and the GA algorithm for the minimum energy consumption with the constraints on both thermal and visual comfort. Ghahramani et al [23] introduced an adaptive hybrid algorithm to learn optimal HVAC settings with no prior historical data
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