A multi-objective evolutionary algorithm for energy management of agricultural systems—A case study in Iran. (April 2015)

• Record Type: Journal Article
• Authors: Shamshirband, Shahaboddin; Khoshnevisan, Benyamin; Yousefi, Marziye; Bolandnazar, Elham; Anuar, Nor Badrul; Abdul Wahab, Ainuddin Wahid; Khan, Saif Ur Rehman
• Is Part Of: Renewable & sustainable energy reviews. Volume 44(2015:Apr.)
• Page Start: 457
• Page End: 465
• Publication Date: 2015-04
• Subjects: Optimization -- Energy management -- Watermelon production -- Greenhouse gas emissions; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.rser.2014.12.038
• Languages: English
• ISSNs: 1364-0321

Abstract

Abstract: Energy consumption and its negative environmental impacts are of interesting topics in the recent centuries. Agricultural systems are both energy users and suppliers in the form of bio energy and play a key role in world economics as well as food security. A high amount of energy from different sources is used in this sector while researchers who investigated energy flow in crops production especially in developing countries, have reported a high degree of inefficiency. It is necessary for the modern management of cropping systems to have all factors (economics, energy and environment) in the decision-making process simultaneously. Accordingly, the application of multi-objective genetic algorithm (MOGA) was investigated in this study and it was employed to find the best mix of agricultural inputs, which could be able to minimize GHG emissions and maximize output energy and benefit cost ratio. The results revealed that on average 28% of the total energy input in watermelon production, as a case study, can be reduced and simultaneously 33% of the total GHG emissions can be decreased while the benefit cost ratio shows a significant increase under optimum application of inputs.