98/02864 Natural gas consumption and climate: a comprehensive set of predictive state-level models for the United States

1998 ◽  
Vol 39 (4) ◽  
pp. 265
Keyword(s):  
United States ◽  
Natural Gas ◽  
State Level ◽  
The United States ◽  
Gas Consumption ◽  
Natural Gas Consumption

scholarly journals Forecasting Natural Gas Consumption in the US Power Sector by a Randomly Optimized Fractional Grey System Model

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yubin Cai ◽  
Xin Ma ◽  
Wenqing Wu ◽  
Yanqiao Deng
Keyword(s):  
United States ◽  
Natural Gas ◽  
The United States ◽  
Grey Model ◽  
System Model ◽  
Grey System ◽  
Power Sector ◽  
Gas Consumption ◽  
Natural Gas Consumption ◽  
Grey System Model

Natural gas is one of the main energy resources for electricity generation. Reliable forecasting is vital to make sensible policies. A randomly optimized fractional grey system model is developed in this work to forecast the natural gas consumption in the power sector of the United States. The nonhomogeneous grey model with fractional-order accumulation is introduced along with discussions between other existing grey models. A random search optimization scheme is then introduced to optimize the nonlinear parameter of the grey model. And the complete forecasting scheme is built based on the rolling mechanism. The case study is executed based on the updated data set of natural gas consumption of the power sector in the United States. The comparison of results is analyzed from different step sizes, different grey system models, and benchmark models. They all show that the proposed method has significant advantages over the other existing methods, which indicates the proposed method has high potential in short-term forecasting for natural gas consumption of the power sector in United States.

Information Processing for Analysis of Consumption Flexibility of Global Natural Gas Demand

2014 ◽  
Vol 707 ◽  
pp. 514-519
Author(s):  
Xin Min Zhang ◽  
Kuang Cen ◽  
Wan Li Xing
Keyword(s):  
United States ◽  
Economic Development ◽  
Natural Gas ◽  
Price Elasticity ◽  
Income Elasticity ◽  
The United States ◽  
Gas Consumption ◽  
Natural Gas Consumption ◽  
Natural Gas Demand ◽  
The Impact

Gas consumption exist great regional difference, price and income are the main factors affecting consumption .Global gas consumption has slow growth, but the price in 2008 there was a twist. We analyze the global natural gas consumption and price points using the data from the BP. The level of economic development and natural gas reserves determine the differences in the levels of consumption. In order to eliminate the impact per unit, the regression model uses the data in the log. This paper studied the influence factors of natural gas consumption in North America using of consumer income elasticity and price elasticity. The results show that the gas consumption have a low income elasticity and price elasticity is higher .Law of "S" shape can explain the income elasticity is low, the reason is that the stage of economic development. Price elasticity is higher lies in the different between Canada and the United States, the United States is a net importer of natural gas, and Canada is a net exporter. Keywords: Consumption Flexibility; Natural Gas Demand; income; price

scholarly journals Relations between Temperature and Residential Natural Gas Consumption in the Central and Eastern United States

2007 ◽  
Vol 46 (11) ◽  
pp. 1993-2013 ◽  
Author(s):  
Reed P. Timmer ◽  
Peter J. Lamb
Keyword(s):  
United States ◽  
Natural Gas ◽  
Time Scales ◽  
The United States ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
The South ◽  
Northern Regions ◽  
Gas Consumption

Abstract The increased U.S. natural gas price volatility since the mid-to-late-1980s deregulation generally is attributed to the deregulated market being more sensitive to temperature-related residential demand. This study therefore quantifies relations between winter (November–February; December–February) temperature and residential gas consumption for the United States east of the Rocky Mountains for 1989–2000, by region and on monthly and seasonal time scales. State-level monthly gas consumption data are aggregated for nine multistate subregions of three Petroleum Administration for Defense Districts of the U.S. Department of Energy. Two temperature indices [days below percentile (DBP) and heating degree-days (HDD)] are developed using the Richman–Lamb fine-resolution (∼1° latitude–longitude) set of daily maximum and minimum temperatures for 1949–2000. Temperature parameters/values that maximize DBP/HDD correlations with gas consumption are identified. Maximum DBP and HDD correlations with gas consumption consistently are largest in the Great Lakes–Ohio Valley region on both monthly (from +0.89 to +0.91) and seasonal (from +0.93 to +0.97) time scales, for which they are based on daily maximum temperature. Such correlations are markedly lower on both time scales (from +0.62 to +0.80) in New England, where gas is less important than heating oil, and on the monthly scale (from +0.55 to +0.75) across the South because of low January correlations. For the South, maximum correlations are for daily DBP and HDD indices based on mean or minimum temperature. The percentiles having the highest DBP index correlations with gas consumption are slightly higher for northern regions than across the South. This is because lower (higher) relative (absolute) temperature thresholds are reached in warmer regions before home heating occurs. However, these optimum percentiles for all regions are bordered broadly by surrounding percentiles for which the correlations are almost as high as the maximum. This consistency establishes the robustness of the temperature–gas consumption relations obtained. The reference temperatures giving the highest HDD correlations with gas consumption are lower for the colder northern regions than farther south where the temperature range is truncated. However, all HDD reference temperatures greater than +10°C (+15°C) yield similar such correlations for northern (southern) regions, further confirming the robustness of the findings. This robustness, coupled with the very high correlation magnitudes obtained, suggests that potentially strong gas consumption predictability would follow from accurate seasonal temperature forecasts.

EXPERIENCE, PROSPECTS AND PROBLEMS OF COOPERATION BETWEEN CHINA AND TURKMENISTAN IN THE FIELDOF NATURAL GAS

2019 ◽  
pp. 323-329
Author(s):  
Y. JIA
Keyword(s):  
Natural Gas ◽  
Central Asia ◽  
Oil And Gas ◽  
The European Union ◽  
Multilateral Cooperation ◽  
Industrial Chain ◽  
The Usa ◽  
Gas Consumption ◽  
Slow Progress ◽  
Natural Gas Consumption

Since 2007, the use of natural gas in China depends on the import, and with an increase in natural gas consumption, gas imports are also constantly growing. In 2018, Chinas natural gas imports approached 100 billion cubic meters, which is 70 times more than in 2006. In recent years, increasing attention has been paid to the use of natural gas in China. Turkmenistan is Chinas main source of pipeline gas imports, and China is Turkmenistans largest exporter of natural gas. In the framework of the traditional model of oil and gas cooperation, China and Turkmenistan are facing such problems as the uniform content of cooperation, lack of close ties in the field of multilateral cooperation and slow progress in the development of the entire industrial chain. Cooperation between China and Central Asia in the field of oil and gas is increasingly affecting the nerves of other countries, except the five countries of Central Asia, but including Russia, Afghanistan, Pakistan, India, Iran and other countries of the Middle East, Japan, South Korea, etc. and even the European Union and the USA. Despite the favorable trading environment for both parties, there are also problems in the domestic market of Turkmenistan and the risks of international competition.

Can increases in Twitter posts predict increases in cumulative incidence of COVID-19 in the United States? Evidence that social media can inform epidemic surveillance. (Preprint)

2020 ◽  
Author(s):  
Ruoyan Sun ◽  
Henna Budhwani
Keyword(s):  
Public Health ◽  
United States ◽  
Cumulative Incidence ◽  
Hot Spots ◽  
Adult Population ◽  
State Level ◽  
The United States ◽  
Education Attainment ◽  
Public Health Systems ◽  
Per Capita

BACKGROUND Though public health systems are responding rapidly to the COVID-19 pandemic, outcomes from publicly available, crowd-sourced big data may assist in helping to identify hot spots, prioritize equipment allocation and staffing, while also informing health policy related to “shelter in place” and social distancing recommendations. OBJECTIVE To assess if the rising state-level prevalence of COVID-19 related posts on Twitter (tweets) is predictive of state-level cumulative COVID-19 incidence after controlling for socio-economic characteristics. METHODS We identified extracted COVID-19 related tweets from January 21st to March 7th (2020) across all 50 states (N = 7,427,057). Tweets were combined with state-level characteristics and confirmed COVID-19 cases to determine the association between public commentary and cumulative incidence. RESULTS The cumulative incidence of COVID-19 cases varied significantly across states. Ratio of tweet increase (p=0.03), number of physicians per 1,000 population (p=0.01), education attainment (p=0.006), income per capita (p = 0.002), and percentage of adult population (p=0.003) were positively associated with cumulative incidence. Ratio of tweet increase was significantly associated with the logarithmic of cumulative incidence (p=0.06) with a coefficient of 0.26. CONCLUSIONS An increase in the prevalence of state-level tweets was predictive of an increase in COVID-19 diagnoses, providing evidence that Twitter can be a valuable surveillance tool for public health.

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