Modeling of Energy Demand in the Greenhouse Using PSO-GA Hybrid Algorithms. (23rd February 2015)

• Record Type: Journal Article
• Authors: Chen, Jiaoliao; Zhao, Jiangwu; Xu, Fang; Hu, Haigen; Ai, QingLin; Yang, Jiangxin
• Other Names: Mino Hiroyuki Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2015(2015)
• Publication Date: 2015-02-23
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2015/871075
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : Modeling of energy demand in agricultural greenhouse is very important to maintain optimum inside environment for plant growth and energy consumption decreasing. This paper deals with the identification parameters for physical model of energy demand in the greenhouse using hybrid particle swarm optimization and genetic algorithms technique (HPSO-GA). HPSO-GA is developed to estimate the indistinct internal parameters of greenhouse energy model, which is built based on thermal balance. Experiments were conducted to measure environment and energy parameters in a cooling greenhouse with surface water source heat pump system, which is located in mid-east China. System identification experiments identify model parameters using HPSO-GA such as inertias and heat transfer constants. The performance of HPSO-GA on the parameter estimation is better than GA and PSO. This algorithm can improve the classification accuracy while speeding up the convergence process and can avoid premature convergence. System identification results prove that HPSO-GA is reliable in solving parameter estimation problems for modeling the energy demand in the greenhouse.