GCHP system optimal predictive control based on RBFNN and APSO algorithm

Author

Zhang Yating ; Wang Guiyang ; Han Guang

Author_Institution

Coll. of Elctronic & Control Eng., Beijing Univ. of Technol., Beijing, China

fYear

2013

Firstpage

2402

Lastpage

2406

Abstract

Aiming at energy consumption saving problem of ground-coupled heat pump (GCHP) system, a nonlinear optimal control strategy based on adaptive particle swarm optimization (APSO) algorithm and radial basis function neural network (RBFNN) predictive control algorithm is proposed in this paper. This paper firstly utilizes RBFNN for estimating the GCHP system model and forecast the output values, then calculated the optimal control inputs of the GCHP system via the rolling optimization of APSO algorithm. Finally, the simulation results show that this control strategy can efficiently reduce the total energy consumption of the GCHP system.

Keywords

ground source heat pumps; neurocontrollers; nonlinear control systems; optimal control; particle swarm optimisation; predictive control; radial basis function networks; space heating; APSO algorithm; GCHP system optimal predictive control; RBFNN predictive control algorithm; adaptive particle swarm optimization algorithm; control strategy; energy consumption saving problem; ground-coupled heat pump system; nonlinear optimal control strategy; optimal control inputs; output value forecasting; radial basis function neural network; rolling optimization; total energy consumption reduction; Algorithm design and analysis; Energy consumption; Heat pumps; Optimization; Particle swarm optimization; Prediction algorithms; Predictive control; adaptive particle swarm optimization; ground-coupled heat pump; optimal predictive control; radial basis function neural network;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Conference (CCC), 2013 32nd Chinese

Conference_Location

Xi´an

Type

conf

Filename

6639829