Mohsen Pourebadollahan Covich; Firouz Fallahi; Kioumars Heydari; Pouyan Kiani
Abstract
Since electricity distribution companies operate in various environmental conditions, their relative efficiency scores used for regulation purposes, should be corrected for environmental factors that could influence the underlying efficiency of them. This paper conducts efficiency correction for 39 Iranian ...
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Since electricity distribution companies operate in various environmental conditions, their relative efficiency scores used for regulation purposes, should be corrected for environmental factors that could influence the underlying efficiency of them. This paper conducts efficiency correction for 39 Iranian electricity distribution companies applying two stage (DEA and Tobit) analysis in 2015. Accordingly, first to the efficiency performances of the electricity distribution companies are determined using DEA. In the second stage, the Tobit model is emploied to determine the environment factors which may explain the calculated efficiency scores. Based on the results, the rainfall level and the customer mix have negative and positive effects on efficiency of electricity distribution companies, respectively. Hence, the primary efficiency scores of DEA are corrected for environmental influences. The comparison of the primary and the corrected efficiencies indicate a significant changes in the efficiency scores and the ranking of the Iranian electricity distribution companies, such that the efficiency of companies with higher rainfall and lower industrial customers relative to their average, have increased, and vice versa.
Jafar Haghighat; Mohammad Saleh Ansari lari; Pouyan Kiani
Volume 4, Issue 13 , January 2015, , Pages 89-116
Abstract
Enjoying the twelfth largest coal reserve in the world, only one percent of Iran’s energy consumption basket is supplied by coal. Now, Iran’s energy economy is under the influence of natural oil and gas resources, causing other more profitable energy resources to be neglected. The Underground ...
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Enjoying the twelfth largest coal reserve in the world, only one percent of Iran’s energy consumption basket is supplied by coal. Now, Iran’s energy economy is under the influence of natural oil and gas resources, causing other more profitable energy resources to be neglected. The Underground Coal Gasification (UCG) technology is a procedure to transform the underground coal into gas, resulting in improving the recovery of coal layers with different thicknesses and depths. This technology may be considered as a strategy to feed the domestic gas network with the synthetic gas of UCG. Therefore, Iran’s gas export capacity will be improved, helping domestic and foreign economy of energy. Implementing and using the UCG technology in Iran will help us take a leap toward the goals of upstream documents and orders of the Supreme Leader in the fields of oil and gas.
Mansour Zaraanjad; pouyan kiani; Salah Ebrahimi; Ali Raoofi
Volume 2, Issue 5 , January 2013, , Pages 107-207
Abstract
Crude oil prices are influenced by many factors. Inclusion of all these determinants in a single model is complex and inefficient. In this case, using time series approach might be appropriate. In the later method past behavior of oil prices is used to forecast its future volatility. Several time series ...
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Crude oil prices are influenced by many factors. Inclusion of all these determinants in a single model is complex and inefficient. In this case, using time series approach might be appropriate. In the later method past behavior of oil prices is used to forecast its future volatility. Several time series studies were conducted to forecast oil prices using methods such as autoregressive integrated moving average (ARIMA) models and artificial neural networks (ANN). All these methods need a large volume of data to have accurate forecasting. One way to overcome this limitation is to use fuzzy regression (FA) models which can give more accurate forecasting with less data. In this study, the three methods, fuzzy regression, ARIMA and fuzzy autoregressive integrated moving average (FARIMA) were applied using the daily oil price in order to forecast oil prices. To compare the forecast accuracy of the model, the prediction error criteria was used. The results showed that the performance of FARIMA is much better than the other two models.
