employed in a real-time control system (Ferreira et al., 2002). ... attributed to the use of mean nightly values and the assumption that the sky temperature was.
Estimation of the Temperatures in an Experimental Infrared Heated Greenhouse Using Neural Network Models A. Kavga1, V. Kappatos2 1 Department of Greenhouse Cultivations and Floriculture, Technological Educational Institute of Messologi, Greece 2 Department of Mechanical and Water Resources Engineering, Technological Educational Institute of Messologi, Greece Abstract A high quality greenhouse control demands continuous measurements of indoor and outdoor greenhouse conditions. In this work, neural network models were used for reducing the cost and the time of temperature measurements, estimating two of the most important parameters in the operation of the greenhouse, namely the inside air temperature and the cover temperature of greenhouse. An extensive experimental investigation was carried out in an infrared heated greenhouse, using the experimental data to train and validate the neural network models. The modelling results show that the estimated temperatures have been in very good agreement with the experimental data with accuracy ranging from 95.64% to 97.67%. Keywords: greenhouses, heating systems, infrared radiation, neural networks.
Introduction Nowadays, greenhouses are increasingly used as a modern way of agricultural production. The main purpose of greenhouses is to improve the environmental conditions in which the plants are grown, achieving better quality and more quantity of the production. The most significant cost component in greenhouse operation is the energy consumption. Therefore, the reduction of energy consumption for heating purposes constitutes a task of crucial importance for greenhouse operators throughout the world (Bot, 2001; Tiwari, 2003). Conventional greenhouse heating systems are based either on circulation of hot water through a piping system or on the direct use of air heaters (Van de Braak, 1988; Teitel et al., 1999). The above methods, in order to achieve the required plant temperature, have to heat the internal greenhouse air to the same or even to a slightly higher temperature than the value targeted for the plants. This practice results in increased heat losses due to convection and radiation through the cover, and also due to the leakages through openings, caused by unavoidable construction defects. To reduce energy consumption, some straightforward measures can be applied. They include, insulation improvement by means of double glazing (Gupta & Chandra, 2002), insulation of side walls (Singh & Tiwari, 2002; Gupta & Chandra, 2002), introduction of thermal screens (Kittas et al., 2003; Ghosal & Tiwari, 2004), use of different types of cover materials, use of zigzag covering that restricts transmission losses (Swinkels et al., 2001) etc. Although, the above methods do result to energy savings, they do not question the basic premise of heating the entire greenhouse environment and then letting the plants gain energy from it. An alternative method for reducing energy consumption in greenhouse heating could emerge by using Infrared Radiation (IR). The main advantage of IR heating is the direct delivery of thermal energy from the source to the canopy, thus eliminating the need to increase the inside air temperature in order to deliver the necessary heat by convection. As a result, cover and inside air may ideally remain at significantly lower temperatures than the target value for the plants, and the heat losses are significantly reduced (Nelson, 2003). However the use of infrared radiation for greenhouse heating has been so far scarcely 1
investigated. Few works (Itagi & Takahashi, 1978; Blom & Ingratta, 1981; Rotz & Heins, 1982) have been published in the early 80’s, investigating the suitability of low intensity IR for greenhouse heating. The investigations are mainly experimental and provide a preliminary proof of the IR concept. Energy savings ranging from 33% to 41% are reported (Blom & Ingratta, 1981) by using an infrared heating system, compared to the conventional heating method. Current trends (Kavga et al., 2009; 2012) indicate that IR is worth reconsidering as a reasonable alternative to conventional forced air or pipe heating. To achieve high quality greenhouse control, indoor and outdoor greenhouse conditions have to be continuously measured. Usually, measurements of the greenhouse conditions are very costly and time consuming. Thus, the development of alternative reliable methods to estimate the indoor and outdoor greenhouse conditions would provide a useful tool and, in parallel, would appreciably reduce both time and cost. Several efforts have been undertaken to formulate the thermal behaviours of a greenhouse. More specifically, many works (Pieters & Deltour, 1997; Gupta & Chandra, 2002; Tiwari, 2003; Singh et al., 2006; Dimokas et al., 2009) have been accomplished to model the inside air and cover temperature of a heated greenhouse as a function of the outside temperature, wind speed, sky temperature, and inside plant canopy temperature. These models are slowly time varying in the above parameters. This finding makes both off-line and on-line methodologies important. While the initial design can be done off-line, the network will probably need on-line adaptation when employed in a real-time control system (Ferreira et al., 2002). In [Kavga et al, 2009], a theoretical model was developed to predict steady-state values of the inside air and cover temperatures for steady external conditions. The model was typically based only on mean values of outdoor temperature and wind speed and incorporated reasonable assumptions for other variables. The uncertainties introduced in the energy calculations were balanced by selecting appropriate coefficients used in designing a heating system. Measured mean nighttime conditions were used as inputs and the predicted inside air and cover temperatures were compared to the measured mean values. The general impression is that the model was quantitatively reliable. More specifically, the average difference between data and predictions was 0.37°C and the standard deviation of the differences was 0.59°C. Deviations were mainly attributed to the use of mean nightly values and the assumption that the sky temperature was equal to the outside air temperature. To overcome these kinds of difficulties, computational analysis techniques are used (Seginer et al., 1994; Ferreira et al., 2002; Eredics and Dobrowiecki, 2010). One of the most known computational analysis techniques is Neural Networks (NNs). NN is a mathematical model or computational model that is inspired by the structure and/or functional aspects of biological neural networks. A NN consists of an interconnected group of artificial neurons, and it processes information using a connectionist approach to computation. In most cases an NN is an adaptive system that changes its structure based on external or internal information that flows through the network during the learning phase. They are usually used to model complex relationships between inputs and outputs or to find patterns in data. Readers can find more information about NNs in several books, including Haykin (Haykin, 1999). ΝΝ of the greenhouse climate are shown to be potentially useful for the following tasks: as models for optimal environmental control, and as a screening tool in preparation for developing physical models. The main advantages of NN models are that they do not require explicit evaluation of transfer coefficients, and need no model formulation. The main disadvantage is that they cannot be used for design purposes. NN models were trained with experimental data from research greenhouses (France, UK), and it was found that these produced good predictions of the inside environment, given the outside conditions and the operation of the control equipment (Seginer et al., 1994). A decomposed model is the only way to tackle the complexity of such a system. A very important module of the decomposition is the heating 2
system, due to its high impact on the overall financial cost of the greenhouse. The best performance is produced by using two neural networks separately for the warming and cooling. The main aim of this study is to develop a new approach based on the artificial intelligence, estimating two of the most important temperatures, the inside air temperature and the cover temperature in infrared heated greenhouse using only three parameters, the outside temperature, the wind speed and finally the sky temperature. The structure of this paper is as follows: In the next section a short description of the experimental setup and procedure are given. In the section 3, the estimation model is described in detail. Finally, the estimation results are presented with comments in section 4.
Experimental Study In the present study, the extensive experimental investigation (Kavga, 2010) was used to derive all the necessary data for the training and evaluation module of the developed ΝΝ model. The experimental setup, experimental procedure and experimental results were described in detail in Kavga et al. (2009). In the following paragraphs, a short description of the used experimental investigation is presented. The small-scale greenhouse is constructed with aluminium framework, with 3mm thick glass sheet as covering material. Its dimensions are width 2.13m, length 2.00m, eaves height 1.00m and total height up to the top 1.50m. The base area of the greenhouse Ap is equal to 4.26m2, the area of the cover is Ac=14.05m2 and the volume of the greenhouse is V=5.33m3. The design of the experimental greenhouse has taken into consideration several constrains, such as similarity to real production greenhouses, implementation of the measuring equipment and efficient cultivation and servicing of the plants. Interior microclimatic parameters monitored in the greenhouse are the temperature, Relative Humidity (RH) and radiation fluxes. Temperature was monitored at several locations at the canopy (Tplants), in the greenhouse air (Tair) and on the inside and outside surface of the cover (Tcover), using a number of thermocouples (Cu-Ni 0.5 mm diameter). The outdoor environmental conditions, which consist of the temperature (Tout), wind speed (WS), RH, sky temperature (Tsky) and rain, were monitored at a height of 2.50m above the ground, on a meteorological mast close to the greenhouse. (Figure 1). All instruments and sensors were calibrated using corresponding certified instruments as reference or using standard samples traceable to European or International standards. Maximum thermal requirements for the experimental infrared heated greenhouse was estimated to be 600W, by using the simulation model developed by Kavga et al., (2009), under the following conditions: Plant canopy temperature Tplants=15°C, outside temperature Tout =0°C and wind speed WS=1ms-1. Based on these heat losses estimates, an infrared heating system was implemented in the greenhouse. The system utilized four incandescent IR lamps, with blown-bulb reflectors, of nominal power 250W each and 50° beam angle, located at the four corners of the greenhouse, 1m above the ground. The key characteristic of the IR lamps is their radiative efficiency, n=60%. For the adopted cultivation (lettuce), the reference temperature is set to the value Tplants=15±1°C, i.e. the heating system turns on when Tplants drops below 14°C and off when Tplants exceeds 16°C.
The Estimation Model The estimation of the temperatures Tair and Tcover in the greenhouse is based on a Radial Basis Function (RBF)-NN. The RBF-NN can model any nonlinear function using a single hidden layer, which eliminates considerations of determining the number of hidden layers and nodes. The simple linear transformation in the output layer can be fully optimized
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by using conventional linear modeling techniques, which are fast and less susceptible to the local minima problem (Haykin, 1999). To develop an adequate database for the training and the validation module, the experimental data (Kavga, 2010) were used. The definition of the input parameters is a very important issue for NN modeling. Processing many parameters not only leads to more computational overhead but often enormously increases the required size of the training data, thereby degrading the classifier performance. In the present work, the input vector consists of three parameters, namely: (i) outside temperature (Tout); (ii) wind speed (WS); and (ii) sky temperature (Tsky). As outputs of the NN, inside air temperature (Tair) and cover temperature (Tcover) were used. In figure 2, the structure of the RBF-NN which is used for estimation of temperatures is displayed. The NN estimation accuracy is significantly affected by the training method. Model training includes the choice of architecture, training algorithms and parameters of the network. The RBF synaptic weights were estimated using the training procedure described in Haykin (1999). For this study, in total 6026 input/output data pairs have been used. After network training, where 5526 randomly selected pairs were used, the remaining 500 pairs were used to evaluate the RBF-NN. The main advantage of validation technique (over "Kfold-cross-validation") is that the proportion of the training/validation split is not dependent on the number of iterations. Of course, there is the case that some observations may never be selected in the validation subsample, others may be selected more than once. In order to overcome this difficulty, the training/validation procedure is repeated 20 times, where each time the data subset is selected, randomly.
Estimation Results and Discussion The RBF-NN structure constitutes a very important factor for the estimation accuracy. The NN complexity is relevant to the number of hidden neurons since the estimated values are more accurate with increasing the number of hidden neurons, however always there is the risk of overriding in data. The dimensionality reduction of the hidden neurons has an important effect to the computational load in applications, where low-cost hardware is required. In this regard, extensive trials were carried out to determine the optimum number of hidden neurons, defined by the RBF NN structure where the best estimation accuracy is achieved. These trials were repeated 20 times, selecting randomly the data pairs. The mean percent estimation error, for different number of hidden neurons, from 5 to 150 neurons, using the Tout, the WS and the Tsky as input parameters, and as output of the NN the Tair and the Tcover, is given in the figure 3. For the two temperatures (Tair, Tcover), the highest estimation rate is achieved using a great number of hidden neurons, as have been shown in figure 3. Greater RBF networks give higher accuracy in the estimation of the temperatures. In the cases of Tair and Tcover, the best estimation results were achieved using 125 and 140 neurons, respectively. The figure 3 shows that there isn't a great fluctuation about mean estimation error between from 120 to 140 hidden neurons. For to have only one approach (one common system), it is assumed that a stable network configuration can include from the mean value of 125 and 140 neurons i.e. 132 neurons. In order to confirm the stability of the method, the training and the validation module were repeated 20 times, selecting randomly the input/output pairs. As shown in the figure 4, there is a very good agreement between experimental and estimation values of the two temperatures. In the most times of iterations (16 times), the estimation accuracy of Tair is greater than Tcover. More specifically, the estimation accuracy ranges from 95.64% to 97.67% (standard deviation 0.15) for Tair and 96.03%-96.61% (standard deviation 0.68) for Tcover, showing that the estimation of Tcover is more stable than Tair. 4
To assess the significance of each input parameters, Tout, WS and Tsky, for the estimated temperatures, a parametric study has been realized. For this study, new RBF-NNs were designed, applying all the possible combinations of input parameters. The best results are derived using two parameters. Due to limited size of the paper, only these results are presented. For each new NN, the input vector consists of two parameters, (i) Tout and WS, (ii) Tout and Tsky, and finally (iii) WS and Tsky. In all cases, the same data subset for training and validation module was used in order to compare the results. The figures 5, 6 and 7 present the estimation error (%) for the above cases (i, ii, iii). The corresponding standard deviations are 0.72 and 0.27 (i), 0.70 and 0.20 (ii), and 0.69 and 0.58 (iii). The results obtained by the above analysis lead to the conclusion that the Tout and Tsky have a stronger influence on the estimation of the Tcover. A similar behaviour is observed in the case of Tair, following with a small difference in estimation accuracy using the input vector of Tout and WS. The figures 4, 5, 6 present a slight fluctuation in terms of the number of iterations. The greatest range of this fluctuation is less than 3.5%. This probably occurred because each time different data subset for training and validation has been used. In addition to this, all the measurements (outside temperature (Tout), wind speed (WS), sky temperature (Tsky), inside air temperature (Tair) and cover temperature (Tcover)) could be affected by many factors, such as inside and outside relative humidity (RH) which affects cover humidity and therefore cover temperature, thermocouples accuracy, extreme climatic conditions.
Conclusions In this work, the authors investigate a reliable and cost efficient way based on NN models in order to substitute the continuous temperature measurements. RBF-NN model were used to estimate the inside air temperature and the cover temperature of greenhouse, two of the most important parameters in the operation of greenhouse. An extensive experimental investigation was carried out to train and validate the RBF-NN. Τhe results show that the used RBF-NN models are available and effective to estimate these two temperature measurements. It is argued that NN models can be developed to eliminate the need for expensive experimental investigation in agricultural field. Moreover, it would be worthwhile to carry out further experimental investigation as to explain the correlation between inside and outside conditions of the infrared heated greenhouse. Finally, the paper recognizes that the experimental investigation should be in a full-scale productive greenhouse, giving more reliable results.
References Blom, J. T. H., & Ingratta, J. F. (1981). The use of low infrared for greenhouse heating in Southern Ontario. Acta Horticultural 115, 205-216. Bot, G. (2001). Developments in indoor sustainable plant production with emphasis on energy saving. Computers and Electronics in Agriculture 30, 151-165. Dimokas, G., Tchamitchian, M., & Kittas, C. (2009). Calibration and validation of a biological model to simulate the development and production of tomatoes in Mediterranean greenhouses during winter period. Biosystems Engineering 103 (2), 217-227. Eredics, P., & Dobrowiecki, T. P. (2010). Neural models for an intelligent greenhouse - The heating. In:11th IEEE International Symposium on Computational Intelligence and Informatics CINTI 2010, Budapest. Ferreira, P.M., Faria, E. A., & Ruan A. E. (2002). Neural network models in greenhouse air temperature prediction. Neurocomputing 43, 51-75. Ghosal, M.K. & Tiwari, G.N. (2004). Mathematical modeling for greenhouse heating by using thermal curtain and geothermal energy. Solar Energy 76, 603-613.
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Gupta, M.J. & Chandra, P. (2002). Effect of greenhouse design parameters on conservation of energy for greenhouse environmental control. Energy 27, 777-794. Haykin, S. (1999). Neural networks: a comprehensive foundation. (2nd ed.) New York: Prentice-Hall. Itagi T., & Takahashi, M. (1978). Studies on the practical use of infrared heater in greenhouse. Kanagawa Hort. Exp. Station Bill 25, 45-5. Kavga, A. (2010). PhD Thesis, Greenhouse energy improvement using infrared heating system (IR). University of Patras, Greece. Kavga, A., Panidis, Th., Bontozoglou, V., & Pantelakis, S. (2009). Infra-Red Heating of Greenhouses Revisited: An Experimental and Modeling Study. Transactions of the ASABE 52(6), 1-11. Kavga, A., Alexopoulos, G., Bontozoglou, V., Pantelakis, S. & Panidis, Th. (2012). Experimental investigation of the energy needs for a conventionally and an infrared heated greenhouse. Advances in Mechanical Engineering, vol. 2012, Article ID 789515, 16 pages. doi:10.1155/2012/789515. Kittas, C. Katsoulas, N. & Baile, A. (2003). Influence of an aluminized thermal screen on greenhouse microclimate and canopy energy balance. Transactions of the ASABE 46 (6), 1653-1663. Nelson, P.V. (2003). Greenhouse operation and management. (6rd ed). New Jersey: Upper Saddle River. Pieters, J.G. & Deltour, J.M. (1997). Performances of greenhouses with the presence of condensation on cladding materials. Journal of Agriculture Engineering Research 68 (2), 125–137 (1997). Rotz, C. A., & Heins, R. D. (1982). Evaluation of infrared heating in a Michigan greenhouse. Transactions of the ASABE 25, 402-407. Seginer, I., Boulard, T. & Bailey, B.J. (1994). Neural Network models of the greenhouse cimate. Journal of Agriculture Engineering Research 59, 203-216. Singh, G., Singh, P.P., Lubana, P.P.S., & Singh, K.G. (2006). Formulation and validation of a mathematical model of the microclimate of a greenhouse. Renewable Energy 31 (10), 15411560. Singh, R.D. & Tiwari, G.N. (2000). Thermal heating of a controlled environment greenhouse: a transient analysis. Energy Conversion and Management 41, 505-507. Swinkels, G.L.A., Sonneveld, P.J., & Bot, G. (2001). Improvement of greenhouse insulation with restricted transmission loss through zigzag covering material. Journal of Agriculture Engineering Research 79, 91-97 Teitel, M., Segal, L. Shklyar, A. & Barak, M. (1999). A comparison between pipe and air heating methods for greenhouses. Journal of Agricultural Engineering 72, 259-273. Tiwari, G.N. (2003). Greenhouse Technology for Controlled Environment. New Delhi: Narosa Publishing House. Van de Braak, N. J. (1988). New methods of greenhouses heating. Engineering and Economics Aspects. Acta Horticulturae 245, 149-157.
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Figure Captions Figure 1. The experimental greenhouse and the meteorological station. Figure 2. The structure of the RBF-NN. Figure 3. Mean estimation error (%) of the temperatures (Tair, Tcover) for different number of hidden neurons. Figure 4. Estimation error (%) of the temperatures for different times of iterations, using the optimum number of hidden neurons. Figure 5. Estimation error (%) of the temperatures for different times of iterations, using the optimum number of hidden neurons and using Tout and WS as the inputs. Figure 6. Estimation error (%) of the temperatures for different times of iterations, using the optimum number of hidden neurons and using Tout and Tsky as the inputs. Figure 7. Estimation error (%) of the temperatures for different times of iterations, using the optimum number of hidden neurons and using WS and Tsky as the inputs.
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Figure 1. The experimental greenhouse and the meteorological station
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Figure 5. Estimation error (%) of the temperatures for different times of iterations, using the optimum number of hidden neurons and using Tout and WS as the inputs.
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Figure 6. Estimation error (%) of the temperatures for different times of iterations, using the optimum number of hidden neurons and using Tout and Tsky as the inputs.
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Figure 7. Estimation error (%) of the temperatures for different times of iterations, using the optimum number of hidden neurons and using WS and Tsky as the inputs.
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