Factors affecting energy consumption and productivity in greenhouses
Abstract
Aim of study: To investigate the impact factors affecting the greenhouse environment on energy consumption and productivity.
Area of study: Alborz province of Iran during the period 2018–2020.
Material and methods: In this study, 18 active units of greenhouse owners in Alborz province of Iran that had necessary standards were identified. Then, upper and lower amplitudes of the variables affecting productivity and energy consumption in greenhouses were calculated using a type-2 fuzzy neural network, Matlab 2017 software. Area, temperature, energy exchange, environmental evapotranspiration and relative humidity were studied as indicators.
Main results: With each unit of temperature, energy consumption and productivity increased by 0.737% and 0.741%, respectively; with each unit of energy exchange, they increased by 0.813% and 0.696%, respectively; with each unit of evaporation and transpiration of the environment, they increased by 0.593% and 0.869%, respectively; and with each unit of humidity, they increased by 0.398% and 0.509%, respectively.
Research highlights: The factors affecting the greenhouse environment such as area, temperature, evapotranspiration and relative humidity had a significant effect on productivity in studying greenhouses and therefore increasing their productivity. According to the results, the model’s ability in energy consumption was better than that for energy efficiency prediction. Also, greenhouse ranking was done by FAHP method.
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References
Bencheikh A, Nourani A, Chabaca MN, 2017. Sustainability evaluation of agricultural greenhouse structures in southern of Algeria using AHP, case of study: Biskra province. Agric Eng Int CIGR J 19: 56-64.
Canakci M, Akinci I, 2006. Energy use pattern analyses of greenhouse vegetable production. Energ 31: 1243-1256. https://doi.org/10.1016/j.energy.2005.05.021
Castro JR, Castillo O, Melin P, Rodríguez-Díaz A, 2008. Building fuzzy inference systems with a new interval type-2 fuzzy logic toolbox. In: Transactions on computational science I. Springer, Berlin. pp: 104-114. https://doi.org/10.1007/978-3-540-79299-4_5
Djevic M, Dimitrijevic A, 2009. Energy consumption for different greenhouse constructions. Energ 34: 1325-1331. https://doi.org/10.1016/j.energy.2009.03.008
Erb RJ, 1993. Introduction to backpropagation neural network computation. Pharm Res 10: 165-170. https://doi.org/10.1023/A:1018966222807
Escamilla-Garcia A, Soto-Zarazúa GM, Toledano-Ayala M, Rivas-Araiza E, Gastélum-Barrios A, 2020. Applications of artificial neural networks in greenhouse technology and overview for smart agriculture development. Appl Sci 10 (11): e3835. https://doi.org/10.3390/app10113835
Han X, Wei Z, Zhang B, Li Y, Du T, Chen H, 2021. Crop evapotranspiration prediction by considering dynamic change of crop coefficient and the precipitation effect in back-propagation neural network model. J Hydrol 596: e126104. https://doi.org/10.1016/j.jhydrol.2021.126104
Hatirli SM, Ozkan B, Fert C, 2006. Energy inputs and crop yield relationship in greenhouse tomato production. Renew Energ 31: 427-438. https://doi.org/10.1016/j.renene.2005.04.007
He H, Xue H, 2011. The design of greenhouse environment control system based on variable universe fuzzy control algorithm. V Int Conf on Computer and Computing Technologies in Agriculture, Berlin (Germany), pp: 72-78. https://doi.org/10.1007/978-3-642-27281-3_10
Hsu SH, Yu MM, Chang CC, 2003. An analysis of total factor productivity growth in China's agricultural sector. Am Agr Econ Assoc, Working paper 01/2003.
Jin W, Li ZJ, Wei LS, Zhen H, 2000. The improvements of BP neural network learning algorithm.V Int Conf on Signal Processing Proceedings, Beijing (China), Aug 21-25. pp: 1647-1649.
Jones Jr JB, 2016. Hydroponics: a practical guide for the soilless grower. CRC Press, Florida, USA. 440 pp.
Kubler S, Robert J, Derigent W, Voisin A, Le-Traon, Y, 2016. A state-of the-art survey & testbed of fuzzy AHP (FAHP) applications. Exper Syst Appl 65: 398-422. https://doi.org/10.1016/j.eswa.2016.08.064
Moon T, Son JE, 2021. Knowledge transfer for adapting pre-trained deep neural models to predict different greenhouse environments based on a low quantity of data. Comput Electron Agric 185: e106136. https://doi.org/10.1016/j.compag.2021.106136
Murthy DS, Sudha M, Hegde MR, Dakshinamoorthy V, 2009. Technical efficiency and its determinants in tomato production in Karnataka, India: Data Envelopment Analysis (DEA) Approach. Agric Econ Res Rev 22: 215-224.
Ozkan B, Ceylan RF, Kizilay H, 2011. Energy inputs and crop yield relationships in greenhouse winter crop tomato production. Renew Energ 36: 3217-3221. https://doi.org/10.1016/j.renene.2011.03.042
Patil SL, Tantau HJ, Salokhe VM, 2008. Modelling of tropical greenhouse temperature by auto regressive and neural network models. Biosyst Eng 99: 423-431. https://doi.org/10.1016/j.biosystemseng.2007.11.009
Pilkington LJ, Messelink G, Van-Lenteren JC, Le-Mottee K, 2010. Protected biological Control-Biological pest management in the greenhouse industry. Biol Cont.52: 216-220. https://doi.org/10.1016/j.biocontrol.2009.05.022
Reynolds J, Rezgui Y, Kwan A, Piriou S, 2018. A zone-level, building energy optimisation combining an artificial neural network, a genetic algorithm, and model predictive control. Energ 151: 729-739. https://doi.org/10.1016/j.energy.2018.03.113
Shamshiri RR, Jones JW, Thorp KR, Ahmad D, Che-Man H, Taheri S, 2018. Review of optimum temperature, humidity, and vapour pressure deficit for microclimate evaluation and control in greenhouse cultivation of tomato: a review. Int agrophys 32: 287-302. https://doi.org/10.1515/intag-2017-0005
Simpson L, 1996. Do decision makers know what they prefer?: MAVT and ELECTRE II. J Oper Res Soc 47: 919-929. https://doi.org/10.1057/jors.1996.117
Van-Laarhoven PJM, Pedrycz W, 1983. A fuzzy extension of Saaty's priority theory. Fuzz Set Syst 11: 229-241. https://doi.org/10.1016/S0165-0114(83)80082-7
Wilamowski BM, 2009. Neural network architectures and learning algorithms. IEEE Ind Electron Mag 3: 56-63. https://doi.org/10.1109/MIE.2009.934790
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