Journal of Economic Modeling Research

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Developing models for accurate natural gas spot price forecasting is critical because these forecasts are useful in determining a range of regulatory decisions covering both supply and demand of natural gas or for market participants. A price forecasting modeler needs to use trial and error to build mathematical models (such as ANN) for different input combinations. This is very time consuming since the modeler needs to calibrate and test different model structures with all the likely input combinations. In addition, there is no guidance about how many data points should be used in the calibration and what accuracy the best model is able to achieve. In this study, the Gamma Test has been used for the first time as a mathematically nonparametric nonlinear smooth modeling tool to choose the best input combination before calibrating and testing models. Then, several nonlinear models have been developed efficiently with the aid of the Gamma test, including regression models Local Linear Regression (LLR), Dynamic Local Linear Regression (DLLR) and Artificial Neural Networks (ANN) models. We used daily, weekly and monthly spot prices in Henry Hub from Jan 7, 1997 to Mar 20, 2012 for modeling and forecasting. Comparing the results of regression models show that DLLR model yields higher correlation coefficient and lower MSError than LLR and will make steadily better predictions. The calibrated ANN models specify the shorter the period of forecasting, the more accurate results will be. Therefore, the forecasting model of daily spot prices with ANN can interpret a fine view. Moreover, the ANN models have superior performance compared with LLR and DLLR. Although ANN models present a close up view and a high accuracy of natural gas spot price trend forecasting in different timescales, its ability in forecasting price shocks of the market is not notable.

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This paper aims at estimating Geometric Brownian Motion (GBM) Model, based on two central parameters in this model (volatility and drift), and forecasting Henry Hub natural gas daily spot prices (07/01/1997-20/03/2012). Researches reveal that two mentioned parameters estimation can be satisfied with different approaches and in various time scales. Therefore, two approaches of backward looking and forward looking have been used in different time scales and sub-periods. Results show that the volatility and drift values are highly dependent on the time scale and backward results are lower than the forward ones. Moreover, along with increasing the number of random runs of the model although the fluctuating range decreases, the predicted line slope is very close to the actual line. Ultimately, the performance evaluation criteria yields that forward method, clearly in 2009, has the best performance. The sub-periods of 2001-2004 in backward and forward methods have the next best performances, respectively. These sub-periods can be used as a basis for calculating the central parameters of the model. In addition, the results suggest that relying on data used in the most recent period is not sufficiently accurate. Also, it is observed that sub-periods or time scales with higher volatility show better performance evaluation criteria, therefore they can be applied in price forecasting with GBM model.

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One of the greatest challenges of renewable resources is unpredictable nature of these resources. Nevertheless use of fossil-renewable integrated hybrid system, which uses some renewable resources rather than a single source, for the supply of power, is the most affordable and the most reliable method. In this study by use of analytical programming approach and 2012 base year statistics, production system of Khorasan Regional Electricity CO. has been simulated and the maximum renewable electricity potential, entered into power generation system. Results show that among all of solar, wind, biomass, geothermal and hydro, only solar power have enough capacity and potential to be substituted with fossil power. The comprehensive system, which uses all renewable potential power capacity, causes 6.38 TWh reduction in fossil power, 4.28 million tons emission, 10-fold increase in spot-hourly price and 21% reduction in grid stability which shows necessity of using stabilizer and storage equipment in the hybrid integrated production system and Technical and financial support from the government to reduce the cost of solar equipment.