IDEAS home Printed from https://ideas.repec.org/r/eee/energy/v35y2010i12p5223-5229.html
   My bibliography  Save this item

Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand estimation of oil in Iran

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Zhu, Yongbin & Shi, Yajuan & Wang, Zheng, 2014. "How much CO2 emissions will be reduced through industrial structure change if China focuses on domestic rather than international welfare?," Energy, Elsevier, vol. 72(C), pages 168-179.
  2. Wu, Zhibin & Xu, Jiuping, 2013. "Predicting and optimization of energy consumption using system dynamics-fuzzy multiple objective programming in world heritage areas," Energy, Elsevier, vol. 49(C), pages 19-31.
  3. Behrang, M.A. & Assareh, E. & Noghrehabadi, A.R. & Ghanbarzadeh, A., 2011. "New sunshine-based models for predicting global solar radiation using PSO (particle swarm optimization) technique," Energy, Elsevier, vol. 36(5), pages 3036-3049.
  4. Behrang, M.A. & Assareh, E. & Ghalambaz, M. & Assari, M.R. & Noghrehabadi, A.R., 2011. "Forecasting future oil demand in Iran using GSA (Gravitational Search Algorithm)," Energy, Elsevier, vol. 36(9), pages 5649-5654.
  5. Kisi, Ozgur & Shiri, Jalal & Karimi, Sepideh & Shamshirband, Shahaboddin & Motamedi, Shervin & Petković, Dalibor & Hashim, Roslan, 2015. "A survey of water level fluctuation predicting in Urmia Lake using support vector machine with firefly algorithm," Applied Mathematics and Computation, Elsevier, vol. 270(C), pages 731-743.
  6. Zeng, Yu-Rong & Zeng, Yi & Choi, Beomjin & Wang, Lin, 2017. "Multifactor-influenced energy consumption forecasting using enhanced back-propagation neural network," Energy, Elsevier, vol. 127(C), pages 381-396.
  7. Wei, Zhongbao & Li, Xiaolu & Xu, Lijun & Cheng, Yanting, 2013. "Comparative study of computational intelligence approaches for NOx reduction of coal-fired boiler," Energy, Elsevier, vol. 55(C), pages 683-692.
  8. Yu, Shiwei & Wei, Yi-Ming & Wang, Ke, 2012. "A PSO–GA optimal model to estimate primary energy demand of China," Energy Policy, Elsevier, vol. 42(C), pages 329-340.
  9. Lean Yu & Zebin Yang & Ling Tang, 2016. "Prediction-Based Multi-Objective Optimization for Oil Purchasing and Distribution with the NSGA-II Algorithm," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(02), pages 423-451, March.
  10. Wang, Xiaoyu & Luo, Dongkun & Zhao, Xu & Sun, Zhu, 2018. "Estimates of energy consumption in China using a self-adaptive multi-verse optimizer-based support vector machine with rolling cross-validation," Energy, Elsevier, vol. 152(C), pages 539-548.
  11. Atul Anand & L Suganthi, 2018. "Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand," Energies, MDPI, vol. 11(4), pages 1-15, March.
  12. Askarzadeh, Alireza, 2014. "Comparison of particle swarm optimization and other metaheuristics on electricity demand estimation: A case study of Iran," Energy, Elsevier, vol. 72(C), pages 484-491.
  13. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
  14. Kaboli, S. Hr. Aghay & Fallahpour, A. & Selvaraj, J. & Rahim, N.A., 2017. "Long-term electrical energy consumption formulating and forecasting via optimized gene expression programming," Energy, Elsevier, vol. 126(C), pages 144-164.
  15. Cheng, Ze-Dong & He, Ya-Ling & Du, Bao-Cun & Wang, Kun & Liang, Qi, 2015. "Geometric optimization on optical performance of parabolic trough solar collector systems using particle swarm optimization algorithm," Applied Energy, Elsevier, vol. 148(C), pages 282-293.
  16. Amirnekooei, K. & Ardehali, M.M. & Sadri, A., 2012. "Integrated resource planning for Iran: Development of reference energy system, forecast, and long-term energy-environment plan," Energy, Elsevier, vol. 46(1), pages 374-385.
  17. Wang, Bo & Wang, Shuming & Zhou, Xianzhong & Watada, Junzo, 2016. "Multi-objective unit commitment with wind penetration and emission concerns under stochastic and fuzzy uncertainties," Energy, Elsevier, vol. 111(C), pages 18-31.
  18. Mladenović, Igor & Sokolov-Mladenović, Svetlana & Milovančević, Milos & Marković, Dušan & Simeunović, Nenad, 2016. "Management and estimation of thermal comfort, carbon dioxide emission and economic growth by support vector machine," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 466-476.
  19. Hamed Bagheri & Mohammadali Behrang & Ehsanolah Assareh & Mohsen Izadi & Mikhail A. Sheremet, 2019. "Free Convection of Hybrid Nanofluids in a C-Shaped Chamber under Variable Heat Flux and Magnetic Field: Simulation, Sensitivity Analysis, and Artificial Neural Networks," Energies, MDPI, vol. 12(14), pages 1-17, July.
  20. Mahdis sadat Jalaee & Alireza Shakibaei & Amin GhasemiNejad & Sayyed Abdolmajid Jalaee & Reza Derakhshani, 2021. "A Novel Computational Intelligence Approach for Coal Consumption Forecasting in Iran," Sustainability, MDPI, vol. 13(14), pages 1-16, July.
  21. Karakurt, Izzet, 2021. "Modelling and forecasting the oil consumptions of the BRICS-T countries," Energy, Elsevier, vol. 220(C).
  22. Mengran Zhou & Tianyu Hu & Kai Bian & Wenhao Lai & Feng Hu & Oumaima Hamrani & Ziwei Zhu, 2021. "Short-Term Electric Load Forecasting Based on Variational Mode Decomposition and Grey Wolf Optimization," Energies, MDPI, vol. 14(16), pages 1-17, August.
  23. Reza Mikaeil & Sina Shaffiee Haghshenas & Zoheir Sedaghati, 2019. "Geotechnical risk evaluation of tunneling projects using optimization techniques (case study: the second part of Emamzade Hashem tunnel)," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 97(3), pages 1099-1113, July.
  24. Tsu-Yang Wu & Haonan Li & Shu-Chuan Chu, 2023. "CPPE: An Improved Phasmatodea Population Evolution Algorithm with Chaotic Maps," Mathematics, MDPI, vol. 11(9), pages 1-21, April.
  25. Yu, Lean & Zhao, Yaqing & Tang, Ling & Yang, Zebin, 2019. "Online big data-driven oil consumption forecasting with Google trends," International Journal of Forecasting, Elsevier, vol. 35(1), pages 213-223.
  26. Ma, Weimin & Zhu, Xiaoxi & Wang, Miaomiao, 2013. "Forecasting iron ore import and consumption of China using grey model optimized by particle swarm optimization algorithm," Resources Policy, Elsevier, vol. 38(4), pages 613-620.
  27. Yuemin Zheng & Jin Tao & Hao Sun & Qinglin Sun & Zengqiang Chen & Matthias Dehmer & Quan Zhou, 2021. "Load Frequency Active Disturbance Rejection Control for Multi-Source Power System Based on Soft Actor-Critic," Energies, MDPI, vol. 14(16), pages 1-17, August.
  28. Penghui Qiang & Peng Wu & Tao Pan & Huaiquan Zang, 2021. "Real-Time Approximate Equivalent Consumption Minimization Strategy Based on the Single-Shaft Parallel Hybrid Powertrain," Energies, MDPI, vol. 14(23), pages 1-22, November.
  29. Ke, Ming-Tsun & Yeh, Chia-Hung & Su, Cheng-Jie, 2017. "Cloud computing platform for real-time measurement and verification of energy performance," Applied Energy, Elsevier, vol. 188(C), pages 497-507.
  30. Yu, Shi-wei & Zhu, Ke-jun, 2012. "A hybrid procedure for energy demand forecasting in China," Energy, Elsevier, vol. 37(1), pages 396-404.
  31. Babak Mohammadi & Farshad Ahmadi & Saeid Mehdizadeh & Yiqing Guan & Quoc Bao Pham & Nguyen Thi Thuy Linh & Doan Quang Tri, 2020. "Developing Novel Robust Models to Improve the Accuracy of Daily Streamflow Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(10), pages 3387-3409, August.
  32. Zhou, Cheng & Chen, Xiyang, 2019. "Predicting energy consumption: A multiple decomposition-ensemble approach," Energy, Elsevier, vol. 189(C).
  33. Cheng, Ze-Dong & Zhao, Xue-Ru & He, Ya-Ling, 2018. "Novel optical efficiency formulas for parabolic trough solar collectors: Computing method and applications," Applied Energy, Elsevier, vol. 224(C), pages 682-697.
  34. Izadyar, Nima & Ghadamian, Hossein & Ong, Hwai Chyuan & moghadam, Zeinab & Tong, Chong Wen & Shamshirband, Shahaboddin, 2015. "Appraisal of the support vector machine to forecast residential heating demand for the District Heating System based on the monthly overall natural gas consumption," Energy, Elsevier, vol. 93(P2), pages 1558-1567.
  35. Kaboli, S. Hr. Aghay & Selvaraj, J. & Rahim, N.A., 2016. "Long-term electric energy consumption forecasting via artificial cooperative search algorithm," Energy, Elsevier, vol. 115(P1), pages 857-871.
  36. Sina Paryani & Aminreza Neshat & Saman Javadi & Biswajeet Pradhan, 2020. "Comparative performance of new hybrid ANFIS models in landslide susceptibility mapping," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 103(2), pages 1961-1988, September.
  37. Uzlu, Ergun & Akpınar, Adem & Özturk, Hasan Tahsin & Nacar, Sinan & Kankal, Murat, 2014. "Estimates of hydroelectric generation using neural networks with the artificial bee colony algorithm for Turkey," Energy, Elsevier, vol. 69(C), pages 638-647.
  38. Younes, Mimoun & Khodja, Fouad & Kherfane, Riad Lakhdar, 2014. "Multi-objective economic emission dispatch solution using hybrid FFA (firefly algorithm) and considering wind power penetration," Energy, Elsevier, vol. 67(C), pages 595-606.
  39. Sahraei, Mohammad Ali & Çodur, Merve Kayaci, 2022. "Prediction of transportation energy demand by novel hybrid meta-heuristic ANN," Energy, Elsevier, vol. 249(C).
  40. Mehdi Seraj & Pejman Bahramian & Abdulkareem Alhassan & Rasool Dehghanzadeh Shahabad, 2020. "The validity of Rodrik’s conclusion on real exchange rate and economic growth: factor priority evidence from feature selection approach," Palgrave Communications, Palgrave Macmillan, vol. 6(1), pages 1-6, December.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.