Ethanol Policy Effects on US Natural Gas Prices and Quantities

2010 ◽  
Vol 100 (2) ◽  
pp. 178-182 ◽  
Author(s):  
Jarrett Whistance ◽  
Wyatt W Thompson ◽  
Seth D Meyer
Keyword(s):  
Natural Gas ◽  
Policy Effects ◽  
Natural Gas Prices ◽  
Ethanol Policy

Mexico and U.S. power systems under variations in natural gas prices

Energy Policy ◽  
2021 ◽  
Vol 156 ◽  
pp. 112378
Author(s):  
Luis Sarmiento ◽  
Anahi Molar-Cruz ◽  
Charalampos Avraam ◽  
Maxwell Brown ◽  
Juan Rosellón ◽  
...  
Keyword(s):  
Natural Gas ◽  
Power Systems ◽  
Natural Gas Prices

Can deployment of renewable energy put downward pressure on natural gas prices?

Energy Policy ◽  
2007 ◽  
Vol 35 (1) ◽  
pp. 295-306 ◽  
Author(s):  
Ryan Wiser ◽  
Mark Bolinger
Keyword(s):  
Renewable Energy ◽  
Natural Gas ◽  
Downward Pressure ◽  
Natural Gas Prices

scholarly journals Natural Gas Prices Forecast Comparison--AEO vs. Natural Gas Markets

2005 ◽  
Author(s):  
Gabrielle Wong-Parodi ◽  
Alex Lekov ◽  
Larry Dale
Keyword(s):  
Natural Gas ◽  
Natural Gas Markets ◽  
Natural Gas Prices ◽  
Forecast Comparison ◽  
Gas Markets

scholarly journals News-Based Research on Forecast of International Natural Gas Price Trend

2020 ◽  
Author(s):  
Tianxiang Li ◽  
Xiaosong Han ◽  
Aoqing Wang ◽  
Hui Li ◽  
Guosheng Liu ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Natural Gas ◽  
Predictive Accuracy ◽  
Price Trend ◽  
Learning Network ◽  
Natural Gas Prices ◽  
Deep Learning Network ◽  
Sentiment Score ◽  
Gas Price

In this paper, we build a deep learning network to predict the trends of natural gas prices. Given a time series, for each day, the gas price trend is classified as “up” and “down” according to the price compared to the last day. Meanwhile, we collect news articles as experimental materials from some natural gas related websites. Every article was then embedded into vectors by word2vec, weighted with its sentiment score, and labeled with corresponding day’s price trend. A CNN and LSTM fused network was then trained to predict price trend by these news vectors. Finally, the model’s predictive accuracy reached 62.3%, which outperformed most of other traditional classifiers.

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