SIMULATION OF ENERGY HARVESTING IN A SEMICLOSED GREENHOUSE

The limited supply of fossil fuels has increased the potential for using closed greenhouses or solar collector greenhouses, where the heat accumulation gives an opportunity to recover and store thermal energy.
This study focuses on a Venlo-type semiclosed greenhouse (307 m²) equipped with cooling, heating, and CO₂ enrichment.
Also, there are additional components such as a 40 kW heat pump, water tank with capacity of 300 m³ (for storing thermal energy), a cooling tower and a phytomonitoring system.
Since plants are able to convert solar radiation in latent heat, a fin pipe cooling system was installed under the roof to collect both latent and sensible heat from solar radiation and plant transpiration, respectively.
In this way the canopy is operating as cooling surface.
The energy stored in the water tank is used for heating purposes.
If the energy contained in the storage tank is not enough for heating the greenhouse, there is a need to buy district heating.
On the other hand, if there is too much energy, the storage should be discharged using the cooling tower.
Therefore, it is necessary to have a better control of the system in such a way that a minimum amount of energy is lost through the cooling tower.
An artificial neural network (ANN) was implemented based on the outside weather conditions: temperature, relative humidity, solar radiation, wind velocity and precipitation, as well as leaf area index and days from sowing.
The ANN was trained, validated and tested using data from 2010 and 2011, and the year 2012 was used for simulation purposes.
The most important variables were solar radiation, temperature and wind velocity.
This ANN will be used for estimation of the energy harvesting in a season for planification purposes, and also for simulation of the amount of energy harvesting for a greenhouse located in a different place.

International Symposium on New Technologies for Environment Control, Energy-Saving and Crop Production in Greenhouse and Plant Factory – Greensys 2013

R. Salazar, L. Miranda, U. Schmidt, A. Rojano, I. Lopez

neural networks, tomato, leaf area index, energy flux

https://doi.org/10.17660/ActaHortic.2014.1037.57