Prediction and Modelling of Energy Consumption on Temperature Control for Greenhouses

Yıl 2019, Cilt: 22 Sayı: 1, 213 - 217, 01.03.2019

Semih Özden Mahir Dursun Ahmet Aksöz Ali Saygın

https://doi.org/10.2339/politeknik.417757

Cited By: 3

Öz

Prediction of the energy consumption is the most
important topic for planning to build an energy power station. This energy
power station can be non-renewable sources power plants or renewable power
plants like wind and solar. Prediction of the energy consumption also figures
out load modeling problem in new smart grid applications. In this study, energy
consumption 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 inputs
in the ANN based model. In addition, the output of the ANN is energy demand
that is strongly related with temperature data.

Anahtar Kelimeler

Artificial neural network, greenhouse, modeling, temperature control, energy consumption

Kaynakça

• [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).
• [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).
• [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).
• [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).
• [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).
• [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).
• [7] Yelmen, B. And Çakir, M. T. “Yapay Sinir Ağları Kullanılarak Sera Isıtma İhtiyacının Tahmini”, Politeknik Dergisi, 14(4): 235-541, (2011).
• [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).
• [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)
• [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).
• [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).
• [12] Kalogirou S. A., “Applications of artificial neural-networks for energy systems”, Applied Energy, 67: 17–35, (2000).