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.
Greenhouses are used to grow crops for better quality and to protect them against natural environmental effects such as high or low temperature. The energy consumption is necessary to maintain a suitable temperature for crop production in the greenhouse. Modeling of energy demand in agricultural greenhouse is very important to maintain optimum inside environment and decrease energy consumption [
Greenhouse is a complex system with nonlinear, random, and strong coupling uncertain features [
Systems identification is suitable in nonlinear systems for which a mathematical model is known and for which input/output data is available in the experiments but for which actual values of parameters in the model are unknown [
Yet, PSO is easy to prematurely converge and lead to the undesired local solution. GA may require a large number of redundant iterations and result in long computing times and low problem-solving efficiency [
Therefore, this paper proposes a novel method for energy consumption prediction in the greenhouse based on the HPSO-GA. The parameters for physical model of energy demand are calibrated using HPSO-GA for the profound optimization performance in the nonlinear greenhouse system with surface water source heat pumps system.
A multispan glass greenhouse was employed in this experiment, and it was located in Jiangsu Province, China (longitude: 120°29′ east, latitude: 31°76′ north). This greenhouse is covered with a single layer of 4 mm thickness glass, 72 m length in the north-south direction, and 7.5 m height and consisted of 28 spans 4 m wide each. The outside air temperature, wind speed, and PAR were measured by a small weather station (GalCon, Eldarshany Co., Israel). The air temperatures at the height of 4 m from the ground of four positions inside were measured by sensors (HMT100, Vaisala Co., Finland). Inside air temperature was the average of 4 temperature sensors in the greenhouse. Surface water source heat pumps system was applied in the greenhouse. The energy consumption is measured by energy meter (HCM1158, Honeywell Co., Germany) according to the water flow rate and the difference of supply and return water temperatures. The data from the sensors were automatically recorded every 5 min by a data logger which we have developed. The experiment was carried out from June 2 to June 7, 2012.
Greenhouse environment model in the physical and physiological methods that take place inside greenhouses based on mass and energy balances, including the biological behavior of plants. Mathematical models of greenhouse microclimate are influenced by several elements of the greenhouse (heat flow and conduction, vapor diffusion, etc.) and the outside boundaries (solar radiation, air temperature, etc.), which is shown in Figure
The thermal balance of greenhouse environment.
The net solar radiation into greenhouse
According to the nonlinear Stefan-Boltzmann law, the thermal long wave radiation exchange between interior and exterior can be written as follows:
According to Aubinet [
The greenhouse air exchanges energy and water vapor (condensation) with the inner surface of the cover and the cover exchanges energy with the outside air. The heat exchanged by conduction and convection between the cover and the air resulted from driving force for the exchange due to the temperature difference. The heat exchanged by internal thermal curtain and infiltration is also dependent on the inside and outside air temperature. Consider
The transport of energy from the leaf is in general defined in the same way as the heat transfer from other surfaces. The sensible heat flux
Latent heat flux due to crop transpiration in greenhouse can be described in terms of the crop canopy available energy and from the inside air saturation deficit, by means of the Penman-Monteith formula:
Some parameters of the model are changing all the time or are not easy to measure, such as LAI,
In the present study, we propose the HPSO-GA, which could optimize the coefficients of equations with better performance. To better optimize the parameters of energy demand model in the greenhouse, an effective hybrid optimization algorithm is developed based on PSO and GA, which can fully combine the merits of these two methods without their drawbacks.
A 4
The flowchart for HPSO-GA.
The PSO evolution consists of a swarm of particles and each particle represents a position in an
Based on the updated velocity, each particle changes its position as follows:
In order to increase the particles diversity and inhibit premature phenomenon, GA operators, crossover and mutation, are utilized in the HPSO-GA.
Crossover operation on the individuals.
Mutation operation on the individuals.
The parts of
According to formulas (
The parameters in the greenhouse model and the HPSO-GA.
Parameters | Symbol | Value |
---|---|---|
Volume |
|
9958.4 |
Glass transmissivity |
|
0.86 |
Sky emissivity |
|
0.90 |
Glass emissivity |
|
0.90 |
Air density |
|
1.2 |
Specific heat |
|
1008 |
The size of the populations | 4 |
20 |
Dimension of the vector |
|
3 |
Positive constant learning rates |
|
1.4995 |
Crossover probability |
|
0.1 |
Mutation probability |
|
0.01 |
Resolution ratio |
|
10 |
Evolution generation | max_ |
200 |
Setting minimum value Rmse | Min_ |
240000 |
A computer with 2.35 GHz Core Duo processor and 2 GB RAM memory was used to run each optimization algorithm 10 times independently. The simulation data in Table
Optimization results with three optimization algorithms.
Parameters | PSO | GA | HPSO-GA |
---|---|---|---|
|
6.61 | 6.69 | 6.18 |
|
0.65 | 0.78 | 0.75 |
LAI | 4.08 | 5.16 | 5.25 |
Time spent (seconds) | 189 | 677 | 156 |
Exit generation | 86 | 115 | 73 |
Rmse | 238525 | 239228 | 238914 |
Simulated energy demand with three optimization algorithms and actual energy consumption.
When the objective fitness value Min_
As seen in Table
In this study, 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. Simulated energy consumption by the identified model using HPSO-GA is in agreement with the actual energy consumption, which proves that HPSO-GA can be used to predict the energy demand in the greenhouse. Compared with GA and PSO, HPSO-GA saves the optimization time of more than 21%, when the maximum generation is less than 80. The HPSO-GA shows excellent ability in solving parameter estimation problems in the greenhouse system, including the optimized speed and accuracy.
The authors declare no conflict of interests regarding the publication of this paper.
The authors would like to thank the staffs of Shentai Hi-Tech Demonstration Garden of Agriculture for their experimental assistance. This work was supported by the National Natural Science Foundation of China (nos. 61374094 and 51275470), the Program for Zhejiang Leading Team of S&T Innovation (no. 2011R50011-02), and the Open Foundation of Key Laboratory of E&M (Zhejiang University of Technology), Ministry of Education & Zhejiang Province (no. EM2013061802).