Modeling and optimization of HVAC systems using artificial neural network and genetic algorithm
HVAC systems, self tuning models, artificial neural network, energy management and control systems, optimization
Intelligent energy management and control system (EMCS) in buildings offers an excellent means of reducing energy consumptions in HVAC systems while maintaining or improving indoor environmental conditions. This can be achieved through the use of computational intelligence and optimization. The paper thus proposes and evaluates a model-based optimization process for HVAC systems using evolutionary algorithm for optimization and artificial neural networks for modeling. The process can be integrated into the EMCS to perform several intelligent functions and achieve optimal whole-system performance. The proposed models and the optimization process are tested using data collected from an existing HVAC system. The testing results show that the models can capture very well the system performance, and the optimization process can reduce cooling energy consumption by about 11% when compared to the traditional operating strategies applied.
Tsinghua University Press
Nabil Nassif. Modeling and optimization of HVAC systems using artificial neural network and genetic algorithm. Build Simul, 2014, 7(3): 237–245.