H. Ghasemi Mobtaker; Y. Ajabshirchi; S. F. Ranjbar; M. Matloobi
Abstract
Introduction Greenhouse is a structure which provides the best condition for the maximum plants growth during the cold seasons. In cold climate zones such as Tabriz province, Iran, the greenhouse heating is one of the most energy consumers. It has been estimated that the greenhouse heating cost is attributed ...
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Introduction Greenhouse is a structure which provides the best condition for the maximum plants growth during the cold seasons. In cold climate zones such as Tabriz province, Iran, the greenhouse heating is one of the most energy consumers. It has been estimated that the greenhouse heating cost is attributed up to 30% of the total operational costs of the greenhouses. Renewable energy resources are clean alternatives that can be used in greenhouse heating. Among the renewable energy resources, solar energy has the highest potential around the world. In this regard, application of solar energy in greenhouse heating during the cold months of a year could be considerable. The rate of thermal energy required inside the greenhouse depends on the solar radiation received inside the greenhouse. Using a north brick wall in an east-west oriented greenhouse can increase the absorption of solar radiation and consequently reduces the thermal and radiation losses. Therefore, the main objective of the present study is to investigate the effect of implementing of a north wall on the solar radiation absorption and energy consumption of an east-west oriented single span greenhouse in Tabriz. Materials and Methods This study was carried out in Tabriz and a steady state analysis was used to predict the energy consumption of a single span greenhouse. For this purpose, thermal energy balance equations for different components of the greenhouse including the soil layer, internal air and plants were presented. For investigating the effect of the north wall on the energy consumption, the Ft and Fn parameters were used to calculate the radiation loss from the walls of the greenhouses. These factors were determined using a 3D–shadow analysis by Auto–CAD software. An east-west oriented single span greenhouse which has a north brick wall and is covered with a single glass sheet with 4 mm thickness was applied to validate the developed models. The measurements were carried out on a sunny winter day (November 30, 2015). The hourly variations of solar radiation on a horizontal surface were measured to calculate the total solar radiation received by the greenhouse using the Liu and Jordan equations. For heating of a greenhouse in nighttime, an electrical heater was used while an additional required energy was measured using a single phase meters. The inside and ambient temperatures of the air were recorded using SHT11 temperature sensors. A computer-based program of EES (engineering equations solver) was developed to solve the energy balance equations. Different statistical indicators were used to predict the accuracy of the presented models. Results and Discussion The obtained results showed that in winter months the greenhouse without the north brick wall can receive 14% more solar radiation than the greenhouse with a north brick wall. On the other hand, the use of a north wall in the greenhouses can reduce the radiation and thermal loss from north wall. To maintain the temperature at 25 °C in day-time and 15 °C in night-time, the additional required energy was calculated for greenhouse with and without north brick wall. The results indicated that the total energy requirement to keep the plants warm was 313.8 MJ in greenhouse without north brick wall and 210.8 MJ in greenhouse with the north brick wall. In other word, use of the north brick wall in the greenhouse can contribute to reduce energy consumption by 32%. Comparisons between the predicted and measured results showed a fair agreement for greenhouse energy requirements. The correlation coefficient and mean percentage error for this model were determined to be 0.79 and -2.34%, respectively. Due to the small values, the radiative exchange within greenhouse cover and the sky was neglected. Therefore, the results of the presented model showed fewer values in comparison with the experimental results. It can be concluded from the final results that a considerable amount of the incident radiation has been lost to the ambient by convection from the cover of the greenhouse (glass walls and north walls). Conclusion In the present study, the effect of north brick wall on solar radiation absorption and energy consumption of a single span greenhouse located in Tabriz was investigated. Results showed that use of north brick wall in an east-west oriented single span greenhouse leads to a reduction of 14% in solar radiation absorbed by the greenhouse. The results indicated that use of the north brick wall in the greenhouse can decrease energy consumption by 32%. There was a fair agreement between the experimental and theoretical results with the calculated correlation coefficient and mean percentage error of 0.79 and -2.34%, respectively.
M. Taki; Y. Ajabshirchi; S. F. Ranjbar; A. Rohani; M. Matloobi
Abstract
Introduction Controlling greenhouse microclimate not only influences the growth of plants, but also is critical in the spread of diseases inside the greenhouse. The microclimate parameters were inside air, greenhouse roof and soil temperature, relative humidity and solar radiation intensity. Predicting ...
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Introduction Controlling greenhouse microclimate not only influences the growth of plants, but also is critical in the spread of diseases inside the greenhouse. The microclimate parameters were inside air, greenhouse roof and soil temperature, relative humidity and solar radiation intensity. Predicting the microclimate conditions inside a greenhouse and enabling the use of automatic control systems are the two main objectives of greenhouse climate model. The microclimate inside a greenhouse can be predicted by conducting experiments or by using simulation. Static and dynamic models are used for this purpose as a function of the metrological conditions and the parameters of the greenhouse components. Some works were done in past to 2015 year to simulation and predict the inside variables in different greenhouse structures. Usually simulation has a lot of problems to predict the inside climate of greenhouse and the error of simulation is higher in literature. The main objective of this paper is comparison between heat transfer and regression models to evaluate them to predict inside air and roof temperature in a semi-solar greenhouse in Tabriz University. Materials and Methods In this study, a semi-solar greenhouse was designed and constructed at the North-West of Iran in Azerbaijan Province (geographical location of 38°10′ N and 46°18′ E with elevation of 1364 m above the sea level). In this research, shape and orientation of the greenhouse, selected between some greenhouses common shapes and according to receive maximum solar radiation whole the year. Also internal thermal screen and cement north wall was used to store and prevent of heat lost during the cold period of year. So we called this structure, ‘semi-solar’ greenhouse. It was covered with glass (4 mm thickness). It occupies a surface of approximately 15.36 m2 and 26.4 m3. The orientation of this greenhouse was East–West and perpendicular to the direction of the wind prevailing. To measure the temperature and the relative humidity of the air, soil and roof inside and outside the greenhouse, the SHT 11 sensors were used. The accuracy of the measurement of temperature was ±0.4% at 20 °C and the precision measurement of the moisture was ±3% for a clear sky. We used these sensors in soil, on the roof (inside greenhouse) and in the air of greenhouse and outside to measure the temperature and relative humidity. At a 1 m height above the ground outside the greenhouse, we used a pyranometre type TES 1333. Its sensitivity was proportional to the cosine of the incidence angle of the radiation. It is a measure of global radiation of the spectral band solar in the 400–1110 nm. Its measurement accuracy was approximately ±5%. Some heat transfer models used to predict the inside and roof temperature are according to equation (1) and (5): Results and Discussion Results showed that solar radiation on the roof of semi-solar greenhouse was higher after noon so this shape can receive high amounts of solar energy during a day. From statistical point of view, both desired and predicted test data have been analyzed to determine whether there are statistically significant differences between them. The null hypothesis assumes that statistical parameters of both series are equal. P value was used to check each hypothesis. Its threshold value was 0.05. If p value is greater than the threshold, the null hypothesis is then fulfilled. To check the differences between the data series, different tests were performed and p value was calculated for each case. The so called t-test was used to compare the means of both series. It was also assumed that the variance of both samples could be considered equal. The variance was analyzed using the F-test. Here, a normal distribution of samples was assumed. The results showed that the p values for heat model in all 2 statistical factors (Comparison of means, and variance) is lower than regression model and so the heat model did not have a good efficient to predict Tri and Ta. RMSE, MAPE, EF and W factor was calculated for to models. Results showed that heat model cannot predict the inside air and roof temperature compare to regression model. Conclusion This article focused on the application of heat and regression models to predict inside air (Ta) and roof (Tri) temperature of a semi-solar greenhouse in Iran. To show the applicability and superiority of the proposed approach, the measured data of inside air and roof temperature were used. To improve the output, the data was first preprocessed. Results showed that RMSE for heat model to predict Ta and Tri is about 1.58 and 6.56 times higher than this factor for regression model. Also EF and W factor for heat model to predict above factors is about 0.003 and 0.041, 0.013 and 0.220 lower than regression model respectively. We propose to use Artificial Neural Network (ANN) and Genetic Algorithm (GA) to predict inside variables in greenhouses and compare the results with heat and regression models.
