M. A. Behrang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
M. A. Behrang has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Afshin Ghanbarzadeh
Mechanical Engineering Department, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
2 shared publications M. R. Assari
Department of Mechanical Engineering , Islamic Azad University , Dezful Branch, Iran
2 shared publicationsE. Assareh
Department of Mechanical Engineering , Islamic Azad University , Dezful Branch, Iran
1 shared publication- Ehsanolah Assareh
Department of Mechanical Engineering, Dezful Branch, Islamic Azad University, Dezful, Iran
1 shared publication
Latest Publications
- 23 June 2011
- Published by Taylor & Francis in Energy Sources, Part B: Economics, Planning, and Policy
- Vol. 6 (3) , 294-303
Total Energy Demand Estimation in Iran Using Bees Algorithm
Many studies are performed by researchers to estimate energy demand but the bees algorithm (BA) technique has never been used for such a study. This paper presents application of the BA technique to estimate total energy demand in Iran, based on socioeconomic indicators. The BA demand estimation models (BA-DEM) are first developed in two forms (exponential and linear) in order to estimate energy demand based on population, gross domestic product, import and export data. Then, two scenarios are designed for forecasting each socioeconomic indicator in the years 2006–2030. Finally, these two scenarios are used to forecast energy demand up to year 2030 using demand estimation models. Energy consumption in Iran from 1981–2005 is considered as the case of this study. The available data is partly used for finding the optimal, or near optimal, values of the weighting parameters (1981–1999) and partly for testing the models (2000–2005). In order to show the accuracy of the BA, obtained results of BA-DEM models are compared with particle swarm optimization and genetic algorithm demand estimation models which are developed for the same problem.
- 23 June 2011
- Published by Taylor & Francis in Energy Sources, Part B: Economics, Planning, and Policy
- Vol. 6 (3) , 294-303
Total Energy Demand Estimation in Iran Using Bees Algorithm
Many studies are performed by researchers to estimate energy demand but the bees algorithm (BA) technique has never been used for such a study. This paper presents application of the BA technique to estimate total energy demand in Iran, based on socioeconomic indicators. The BA demand estimation models (BA-DEM) are first developed in two forms (exponential and linear) in order to estimate energy demand based on population, gross domestic product, import and export data. Then, two scenarios are designed for forecasting each socioeconomic indicator in the years 2006–2030. Finally, these two scenarios are used to forecast energy demand up to year 2030 using demand estimation models. Energy consumption in Iran from 1981–2005 is considered as the case of this study. The available data is partly used for finding the optimal, or near optimal, values of the weighting parameters (1981–1999) and partly for testing the models (2000–2005). In order to show the accuracy of the BA, obtained results of BA-DEM models are compared with particle swarm optimization and genetic algorithm demand estimation models which are developed for the same problem.
- 18 March 2011
- Published by Taylor & Francis in Applied Artificial Intelligence
- Vol. 25 (4) , 292-304
ASSESSMENT OF ELECTRICITY DEMAND IN IRAN'S INDUSTRIAL SECTOR USING DIFFERENT INTELLIGENT OPTIMIZATION TECHNIQUES
This study presents application of particle swarm optimization (PSO) and genetic algorithm (GA) methods to estimate electricity demand in Iran's industrial sectors, based on economic indicators. The economic indicators used in this study are number of customers, gross domestic product (GDP), electricity production and price. The models developed in two forms (exponential and linear). Electricity consumption in Iran's industrial sector from 1981 to 2005 is considered as the case of this study. The available data is partly used for finding the optimal, or near optimal, values of the weighting parameters (1981–1999) and partly for testing the models (2000–2005). For the best results of GA, the relative error averages were 1.13% and 1.29% for GA − DE M exponential and GA − DE M linear and for PSO were 1.03% and 1.69% for PSO − DE M exponential and PSO − DEMlinear. Electricity consumption is forecasted up to year 2030.
- 18 March 2011
- Published by Taylor & Francis in Applied Artificial Intelligence
- Vol. 25 (4) , 292-304
ASSESSMENT OF ELECTRICITY DEMAND IN IRAN'S INDUSTRIAL SECTOR USING DIFFERENT INTELLIGENT OPTIMIZATION TECHNIQUES
This study presents application of particle swarm optimization (PSO) and genetic algorithm (GA) methods to estimate electricity demand in Iran's industrial sectors, based on economic indicators. The economic indicators used in this study are number of customers, gross domestic product (GDP), electricity production and price. The models developed in two forms (exponential and linear). Electricity consumption in Iran's industrial sector from 1981 to 2005 is considered as the case of this study. The available data is partly used for finding the optimal, or near optimal, values of the weighting parameters (1981–1999) and partly for testing the models (2000–2005). For the best results of GA, the relative error averages were 1.13% and 1.29% for GA − DE M exponential and GA − DE M linear and for PSO were 1.03% and 1.69% for PSO − DE M exponential and PSO − DEMlinear. Electricity consumption is forecasted up to year 2030.