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Selecting the appropriate scenario for forecasting energy demands of residential and commercial sectors in Iran using two metaheuristic algorithms | ||
| Interdisciplinary Journal of Management Studies (Formerly known as Iranian Journal of Management Studies) | ||
| مقاله 6، دوره 9، شماره 1، فروردین 2016، صفحه 101-123 اصل مقاله (1.13 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/ijms.2016.55037 | ||
| نویسندگان | ||
| Hesam Nazari* 1؛ Aliyeh Kazemi2؛ Mohammad-Hossein Hashemi3 | ||
| 1Faculty of Management, University of Tehran, Tehran, Iran | ||
| 2Faculty of Management, University of Tehran | ||
| 3Faculty of Power and Water (Shahid Abbaspour), Shahid Beheshti University | ||
| چکیده | ||
| This study focuses on the forecasting of energy demands of residential and commercial sectors using linear and exponential functions. The coefficients were obtained from genetic and particle swarm optimization (PSO) algorithms. Totally, 72 different scenarios with various inputs were investigated. Consumption data in respect of residential and commercial sectors in Iran were collected from the annual reports of the central bank, Ministry of Energy and the Petroleum Ministry of Iran (2010). The data from 1967 to 2010 were considered for the case of this study. The available data were used partly to obtain the optimal, or near optimal values of the coefficient parameters (1967–2006) and for testing the models (2007–2010). Results show that the PSO energy demand estimation exponential model with inputs, including value addition of all economic sectors, value of constructed buildings, population, and price indices of electrical and fuel appliances using the mean absolute percentage error on tests data were 1.97%, was considered the most suitable model. Finally, basing on the best scenario, the energy demand of residential and commercial sectors is estimated at 1718 mega barrels of oil equivalent up to the year 2032. | ||
| کلیدواژهها | ||
| energy demand؛ forecasting؛ Genetic Algorithm؛ Particle Swarm Optimization Algorithm؛ Residential and commercial sectors | ||
| مراجع | ||
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