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Modeling and optimization of HVAC systems using artificial neural network and genetic algorithm

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  • Building Systems and Components
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Abstract

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.

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Authors and Affiliations

  1. Department of CAAE Engineering, North Carolina A&T State University, 455 McNair Hall, 1601 E. Market Street, Greensboro, NC, 27411, USA

    Nabil Nassif

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  1. Nabil Nassif
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Correspondence to Nabil Nassif.

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Nassif, N. Modeling and optimization of HVAC systems using artificial neural network and genetic algorithm. Build. Simul. 7, 237–245 (2014). https://doi.org/10.1007/s12273-013-0138-3

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  • DOI: https://doi.org/10.1007/s12273-013-0138-3

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