A Model-free Approach to Optimal Chiller Loading Problem Using Global Simultaneous Perturbation Stochastic Approximation

This paper presents an investigation of a modelfree approach using global simultaneous perturbation stochastic approximation (GSPSA) to optimal chiller loading problem. The GSPSA based method is employed to optimize the partial load ratio (PLR) of each chiller such that the total power consumption of multi-chiller systems is minimized. The main advantage of GSPSA is that it can produce fast design parameter without information of plant model by measuring the input-output data of the system. Our model-free design is validated using an experimental data from a well-known multichiller system of semiconductor factory in Hsinchu Scientific Garden, Taiwan. In addition, the performance of the GSPSA based method is compared to the other stochastic optimization based approaches. Simulation results illustrate that the GSPSA based method has a potential in minimizing power consumption of multi-chiller systems with less number of evaluated cost functions.

[1]  Alessandro Beghi,et al.  A PSO-based algorithm for optimal multiple chiller systems operation , 2012 .

[2]  Shun-ichi Azuma,et al.  Performance analysis of model-free PID tuning of MIMO systems based on simultaneous perturbation stochastic approximation , 2014, Expert Syst. Appl..

[3]  Yung-Chung Chang,et al.  An innovative approach for demand side management—optimal chiller loading by simulated annealing , 2006 .

[4]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[5]  R. Venkata Rao,et al.  Teaching Learning Based Optimization Algorithm: And Its Engineering Applications , 2015 .

[6]  Shang-Ho Tsai,et al.  Economic dispatch of chiller plant by improved ripple bee swarm optimization algorithm for saving energy , 2016 .

[7]  Shun-ichi Azuma,et al.  Identification of continuous-time Hammerstein systems by simultaneous perturbation stochastic approximation , 2016, Expert Syst. Appl..

[8]  Yung-Chung Chang,et al.  Optimal chiller loading by genetic algorithm for reducing energy consumption , 2005 .

[9]  Viviana Cocco Mariani,et al.  Improved firefly algorithm approach applied to chiller loading for energy conservation , 2013 .

[10]  Yung-Chung Chang,et al.  Economic dispatch of chiller plant by gradient method for saving energy , 2010 .

[11]  Hamdan Daniyal,et al.  An Application of Cuckoo Search Algorithm for Solving Optimal Chiller Loading Problem for Energy Conservation , 2015 .

[12]  Matjaz Gams,et al.  Trade-off between Energy Consumption and Comfort Experience in Smart Buildings , 2015, Inf. Technol. Control..

[13]  Seyed Hossein Hosseinian,et al.  A novel approach for optimal chiller loading using particle swarm optimization , 2008 .

[14]  Lung-Chieh Lin,et al.  Optimal Chiller Loading by Team Particle Swarm Algorithm for Reducing Energy Consumption , 2009, Energies.

[15]  Wen Zhong Shen,et al.  Solving the wind farm layout optimization problem using random search algorithm , 2015 .

[16]  Wen-Shing Lee,et al.  Optimal chiller loading by differential evolution algorithm for reducing energy consumption , 2011 .

[17]  Yung-Chung Chang,et al.  Genetic algorithm based optimal chiller loading for energy conservation , 2005 .

[18]  Yung-Chung Chang,et al.  The Optimization of Chiller Loading by Adaptive Neuro-Fuzzy Inference System and Genetic Algorithms , 2015 .

[19]  Abdullah Ates,et al.  Auto-tuning of PID controller according to fractional-order reference model approximation for DC rotor control , 2013 .

[20]  Shun-ichi Azuma,et al.  A Model-Free Approach for Maximizing Power Production of Wind Farm Using Multi-Resolution Simultaneous Perturbation Stochastic Approximation , 2014 .