TY - JOUR T1 - Prediction and Modelling of Energy Consumption on Temperature Control for Greenhouses TT - Prediction and Modelling of Energy Consumption on Temperature Control for Greenhouses AU - Özden, Semih AU - Dursun, Mahir AU - Aksöz, Ahmet AU - Saygın, Ali PY - 2019 DA - March DO - 10.2339/politeknik.417757 JF - Politeknik Dergisi PB - Gazi Üniversitesi WT - DergiPark SN - 2147-9429 SP - 213 EP - 217 VL - 22 IS - 1 LA - en AB - Prediction of the energy consumption is the mostimportant topic for planning to build an energy power station. This energypower station can be non-renewable sources power plants or renewable powerplants like wind and solar. Prediction of the energy consumption also figuresout load modeling problem in new smart grid applications. In this study, energyconsumption model is developed for temperature control of a greenhouse.Artificial Neural Network based modeling is advanced with temperature of inner,temperature of outer and temperature of soil. So, these temperatures are inputsin the ANN based model. In addition, the output of the ANN is energy demandthat is strongly related with temperature data. KW - Artificial neural network KW - greenhouse KW - modeling KW - temperature control KW - energy consumption N2 - Prediction of the energy consumption is the mostimportant topic for planning to build an energy power station. This energypower station can be non-renewable sources power plants or renewable powerplants like wind and solar. Prediction of the energy consumption also figuresout load modeling problem in new smart grid applications. In this study, energyconsumption model is developed for temperature control of a greenhouse.Artificial Neural Network based modeling is advanced with temperature of inner,temperature of outer and temperature of soil. So, these temperatures are inputsin the ANN based model. In addition, the output of the ANN is energy demandthat is strongly related with temperature data. CR - [1] Dursun M. and Ozden S., “Optimization of soil moisture sensor placement for a PV powered drip irrigation system using a genetic algorithm and artificial neural network,” Electrical Engineering, 99: 407–419, (2017). CR - [2] Dursun M. and Ozden S., “An Efficient Improved Photovoltaic Irrigation System with Artificial Neural Network Based Modeling of Soil Moisture Distribution – A Case Study in Turkey”, Computers and Electronics in Agriculture, 102: 120-126, (2014). CR - [3] Zou Q., Ji J., Zhang S. and Shi M., “Model Predictive Control Based on Particle Swarm Optimization of Greenhouse Climate for Saving Energy Consumption,” World Automation Congress (WAC), 123-128, (2010). CR - [4] Avila-Miranda R., Begovich O. and Ruiz-Leon J., “An optimal and intelligent control strategy to ventilate a greenhouse,” Evolutionary Computation (CEC), 779-782, (2013). CR - [5] Ma G., Qin L., Chu Z. and Wu G., “Modeling greenhouse humidity by means of NNARMAX and principal component analysis,” Control and Decision Conference (CCDC), 27th Chinese. IEEE, 2015, 4840–4845, (2015). CR - [6] Liu Q., Jin D., Shen J., Fu Z. and Linge N., “A WSN-based prediction model of microclimate in a greenhouse using extreme learning approaches,” Advanced Communication Technology (ICACT), 2016 18th International Conference on. IEEE, (6): 730–735, (2016). CR - [7] Yelmen, B. And Çakir, M. T. “Yapay Sinir Ağları Kullanılarak Sera Isıtma İhtiyacının Tahmini”, Politeknik Dergisi, 14(4): 235-541, (2011). CR - [8] Yan C. W. and Yao J., “Application of ANN for the prediction of building energy consumption at different climate zones with HDD and CDD,” Future Computer and Communication (ICFCC), 2010 2nd International Conference on. IEEE, 2010, 3: 286–289, (2010). CR - [9] Yuce B. and Rezgui Y., “An ANN-GA semantic rule-based system to reduce the gap between predicted and actual energy consumption in buildings,” IEEE Transactions on Automation Science and Engineering, 1351-1363, (2017) CR - [10] Gezer G., Tuna G., Kogias D., Gulez K. and Gungor V. C., “PI-controlled ANN-based Energy Consumption Forecasting for Smart Grids,” 12th International Conference on Informatics in Control, Automation and Robotics (ICINCO), 110–116, (2015). CR - [11] Ferlito, S., Atrigna, M., Graditi, G., De Vito, S., Salvato, M., Buonanno, A. and Di Francia, G. “Predictive models for building's energy consumption: An Artificial Neural Network (ANN) approach”. AISEM Annual Conference, 1-4, (2015). CR - [12] Kalogirou S. A., “Applications of artificial neural-networks for energy systems”, Applied Energy, 67: 17–35, (2000). UR - https://doi.org/10.2339/politeknik.417757 L1 - https://dergipark.org.tr/tr/download/article-file/461910 ER -