In this study, the estimation of price, income and cross elasticities of GAS DEMAND in the selected group of high income and middle -income countries in the period of 1990-2020 has been done using the panel data method. The results of the estimation of the model using the fixed effects method showed that GAS is an inelastic commodity in the group of selected countries. The price elasticity of natural GAS in the group of selected high income and middle-income countries is 0.256077 and 0.196553 respectively. The reaction of GAS consumers in the group of selected high-income countries is higher than the group of selected middle-income countries to the same change in the price of GAS and also, the results of income elasticity showed that GAS is an essential commodity in the group of selected countries. The income elasticity of GAS in the group of selected high-income and middle-income countries is 0.355611 and 0.490959, respectively, and finally, the results of cross-elasticity estimation showed that electricity is a substitute for GAS in the group of selected countries. The cross-elasticity of GAS compared to the price of electricity in the group of selected high-income and middle-income countries is 0.029562 and 0.091683, respectively. The reaction of GAS consumers in the group of selected middle-income countries to the same change in income and price of electricity is more than that of the group of selected high-income countries.

Energy is one of the most important factors for economic development and progress. Natural GAS having many advantages and providing 43 percent of primary energy in the country has a particular role among other energy sources. Analysis of energy market, in general, and energy DEMAND, in particular, is very important in recognizing the role of energy and its usage in different areas of the country. The study of natural GAS DEMAND as one of the energy carriers in household sector is done in this work to find how the structure of consumer's behaviour is. Analysis of the household DEMAND elasticity to natural GAS prices, per capita income, and average weather temperature help policy makers to provide required natural GAS in an optimum form. Achieving this purpose, total and average DEMAND functions of Tehran citizens are estimated on the basis of data during 1985 -1999 seasonally. Finally, income and price elasticities for natural GAS are determined.

It is essential to study the DEMANDs for this energy carrier in different and separate sectors, by considering the effective factors in each sector. Meanwhile residential sector has the greatest consumption rate with near 25% in recent years. Effective factors of DEMAND in this sector divide into observable factors, like carrier price, consumer income, and temperature on the one hand and unobservable factors like consumers' habits and taste, GAS burners technology on the other hand. Regarding the impact of temperature on the consumption of residential sector, estimation of the multipliers in a specific region (like Tehran) will help the explanation of this variable to improve. Furthermore, in this study, Kalman filter technique is used to prevent the bias estimation of multipliers while considering the impact of unobservable factors and other variables. Finally we came to the conclusion that price and income elasticity of DEMANDs are-0.098 and 0.114 respectively.

Safeguarding the energy security is an important energy policy goal of every country. Assuring sufficient and reliable resources of energy at affordable prices is the main objective of energy security. Due to such reasons as special geopolitical position, terrorist attacks and other unrest in the Middle East, securing Iran’ s energy DEMAND and increasing her natural GAS exports have turned into a critical issue. The aim of this paper is to develop a composite index for evaluating the GAS DEMAND security. To this purpose, six individual indices (i. e. GAS exports dependency, transportation cost, economic dependency, political stability of the exporting country, political stability of the importing country, and the purchasing power of the importing country) are identified and the Principle Component Analysis (PCA) method is employed to weigh and combine the indices into GDSI (GAS DEMAND Security Index). The results show an interesting counter-intuitive phenomenon that the political stability of the importing and exporting countries have respectively the most and the least effects on the obtained composite index.

The ability of Artificial Neural Network (ANN) for estimating the natural GAS DEMAND load for the next day and month of the populated cities has shown to be a real concern.As the most applicable network, the ANN with multi-layer back propagation perceptron’s is used to approximate functions. Throughout the current work, the daily effective temperature is determined, and then the weather data with the GAS consumption data of the last days are used for network training. It is shown that nearly 93% and 98.9% of the result is in a good agreement with the real data for the daily GAS load forecasting and those of the monthly respectively. These results clearly show the capability of the presented networks. The method, however, can further be developed for prediction of other required information in various industries.

GAS refineries are usually located in remote and non residential areas. For that it takes quite a while to reach to the end users. Moreover, in the peak seasons such as winter, the consumption rate is normally very high. Such circumstances make the supply management to be very complex, particularly for the large and populated cities like Tehran. In order to take a reliable control on the DEMAND and to know how the extraction and/or the production rate can match the consumption rate at different regional places especially those of the rural areas, one should think of an off-side un-classical method. Among these, the Artificial Neural Network, ANN; is one of the most common methods which are currently used for different functional goals. In this research an investigation is carried out for applying ANN method for in-advance forecasting of GAS DEMAND load. The method is based on weather parameters with multilayer back propagation, BP algorithm. Throughout the current work, the effective daily temperature is determined, after which the data of the last days is used for network training. Finally it is shown that up to nearly 93% & 99% of the result is in a good agreement with the real data for daily and monthly GAS load forecasting, respectively. The method however can further be developed for prediction of other necessary information in any industries.