A new methodology for analyzing and predicting U.S. liquefied natural gas imports using neural networks
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Liquefied Natural Gas (LNG) is becoming an increasing factor in the U.S. natural gas market. For 30 years LNG imports into the U.S. have remained fairly flat. There are currently 18 permit applications being filed in the U.S. and another 10 permit applications being filed in Canada and Mexico for LNG import terminals. The EIA (Energy Information Agency) estimates by 2025 that LNG will make up 21% of the total U.S. Natural Gas Supply. This study developed a neural network approach to forecast LNG imports into the U.S. Various input variables were gathered, organized into groups based on similarity, and then a correlation matrix was generated to screen out redundant variables. Since a limited number of data points were available I used a restricted number of input variables. Based on this restriction, I grouped the input variables into four different scenarios and then generated a forecast for each scenario. These four different scenarios were the $/MMBTU model, natural gas energy consumption model, natural gas consumption model and the energy stack model. The standard neural network approach was also used to screen the input variables. First, a correlation matrix determined which variables had a high correlation with the output, U.S. LNG imports. The ten most correlated input variables were then put into correlation matrix to determine if there were any redundant variables. Due to the lack of data points only the five most highly correlated input variables were used in the neural network simulation. A number of interesting results were obtained from this study. The energy stack model and the consumption of natural gas forecasted a non-linear trend in U.S. LNG imports, compared to the linear trend forecasted by the EIA. The energy stack model and consumption of natural gas model predicted that in 2025 U.S. LNG imports will be about 6.5 TCF, while the other three models prediction is about three times as less. The energy stack model is the most realistic model due its non-linear trend, when the rapid increase of LNG imports is going to occur, and the quantity of U.S. LNG imports predicted in 2025.
Bolen, Matthew Scott (2005). A new methodology for analyzing and predicting U.S. liquefied natural gas imports using neural networks. Master's thesis, Texas A&M University. Texas A&M University. Available electronically from