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Mathematical Problems in Engineering
Volume 2015, Article ID 871075, 6 pages
http://dx.doi.org/10.1155/2015/871075
Research Article

Modeling of Energy Demand in the Greenhouse Using PSO-GA Hybrid Algorithms

1Key Laboratory of E&M, Zhejiang University of Technology, Ministry of Education & Zhejiang Province, Hangzhou 310014, China
2Institute of Manufacturing Engineering, Zhejiang University, Hangzhou 310027, China
3College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310014, China

Received 30 May 2014; Accepted 14 October 2014

Academic Editor: Hiroyuki Mino

Copyright © 2015 Jiaoliao Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.