Modeling and optimization of HVAC systems using a dynamic neural network

The energy consumption of a heating, ventilating and air conditioning (HVAC) system is optimized by using a data-driven approach. Predictive models with controllable and uncontrollable input and output variables utilize the concept of a dynamic neural network. The minimization of the energy consumed while maintaining indoor room temperature at an acceptable level is accomplished with a bi-objective optimization. The model is solved with three variants of the multi-objective particle swarm optimization algorithm. The optimization model and the multi-objective algorithm have been implemented in an existing HVAC system. The test results performed in the existing environment demonstrate significant improvement of the system. Compared to the traditional control strategy, the proposed model saved up to 30% of energy.

[1]  John Wang,et al.  Data Mining: Opportunities and Challenges , 2003 .

[2]  Andrew Kusiak,et al.  Cooling output optimization of an air handling unit , 2010 .

[3]  Luis Pérez-Lombard,et al.  A review on buildings energy consumption information , 2008 .

[4]  Eric R. Ziegel,et al.  The Elements of Statistical Learning , 2003, Technometrics.

[5]  Julian F. Miller,et al.  Genetic and Evolutionary Computation — GECCO 2003 , 2003, Lecture Notes in Computer Science.

[6]  Riccardo Poli,et al.  Particle Swarm Optimisation , 2011 .

[7]  Andrew Kusiak,et al.  Multi-objective optimization of HVAC system with an evolutionary computation algorithm , 2011 .

[8]  Kumpati S. Narendra,et al.  Control of nonlinear dynamical systems using neural networks: controllability and stabilization , 1993, IEEE Trans. Neural Networks.

[9]  Andrew Kusiak,et al.  Reheat optimization of the variable-air-volume box , 2010 .

[10]  Mohammad Ali Abido,et al.  Two-level of nondominated solutions approach to multiobjective particle swarm optimization , 2007, GECCO '07.

[11]  Nabil Nassif,et al.  A cost‐effective operating strategy to reduce energy consumption in a HVAC system , 2008 .

[12]  R. Sabourin,et al.  Evolutionary algorithms for multi-objective optimization in HVAC system control strategy , 2004, IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04..

[13]  Berhane H. Gebreslassie,et al.  Design of environmentally conscious absorption cooling systems via multi-objective optimization and life cycle assessment , 2009 .

[14]  K S Narendra,et al.  Control of nonlinear dynamical systems using neural networks. II. Observability, identification, and control , 1996, IEEE Trans. Neural Networks.

[15]  M. Woloszyn,et al.  Numerical prediction of indoor air humidity and its effect on indoor environment , 2003 .

[16]  Kumpati S. Narendra,et al.  Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.

[17]  Lei Zhao,et al.  Model-based optimization for vapor compression refrigeration cycle , 2013 .

[18]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[19]  J. Friedman Stochastic gradient boosting , 2002 .

[20]  Leslie K. Norford,et al.  Naturally ventilated and mixed-mode buildings—Part I: Thermal modeling , 2009 .

[21]  Tao Lu,et al.  Prediction of indoor temperature and relative humidity using neural network models: model comparison , 2009, Neural Computing and Applications.

[22]  Guilherme De A. Barreto,et al.  Long-term time series prediction with the NARX network: An empirical evaluation , 2008, Neurocomputing.

[23]  Robert Sabourin,et al.  Optimization of HVAC Control System Strategy Using Two-Objective Genetic Algorithm , 2005 .

[24]  Abdullatif Ben-Nakhi,et al.  Energy conservation in buildings through efficient A/C control using neural networks , 2002 .

[25]  G. Casella,et al.  Statistical Inference , 2003, Encyclopedia of Social Network Analysis and Mining.

[26]  Andrew Kusiak,et al.  Modeling and optimization of HVAC energy consumption , 2010 .