Forecasting Residential Natural Gas Demand

Aras, Haydar; Aras, Nil

doi:10.1080/00908310490429740

Cited by 76 publications

(35 citation statements)

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“…In the gas industry, alternative methods can be found to model gas consumption for general forecasting and gas management purposes, including: regression models (Gas Networks Ireland, 2007); auto-regressive integrated moving-average or autoregressive models including exogenous variables (Aras and Aras, 2004;Brabec et al, 2008); neural networks (Brown et al, 1994;Kizilaslan and Karlik, 2008;Peharda et al, 2001;Khotanzad et al, 2000); and generalised additive models (Brabec et al, 2010). In relation to peak consumption estimation, regression-based models are considered to offer a more transparent methodology compared to the alternatives.…”

Section: Modelling Techniquesmentioning

confidence: 99%

Forecasting peak-day consumption for year-ahead management of natural gas networks

Oliver¹,

Duffy²,

Enright³

et al. 2017

Utilities Policy

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a b s t r a c tSecurity of supply is at the forefront of energy policy in the EU and elsewhere. This paper develops a methodology to forecast peak-day gas consumption for the consideration of gas transmission system operators. It is developed for the domestic and small-to-medium enterprise (SME) gas market, based on a review of current practice. From this assessment, a climate-adjusted network degree day (NDD CA ) variable is developed to estimate the weather-dependent gas consumption of such markets. We show that solar radiation significantly affects gas consumption and should be considered in consumption models. The study also quantifies the difference in capacity required under alternative gas supply standards.

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Section: Modelling Techniquesmentioning

confidence: 99%

Forecasting peak-day consumption for year-ahead management of natural gas networks

Oliver¹,

Duffy²,

Enright³

et al. 2017

Utilities Policy

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“…Gorucu et al (2004) proposed an approach based on artificial neural network for natural gas demand. Aras and Aras (2004) have estimated residential natural gas consumption in Turkey using inferential autoregressive time series models. Kaboudan and Liu (2004) have developed regression models based on genetic algorithms for natural gas demand in U.S. A.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting Residential Consumption of Natural Gas Using Genetic Algorithms

Aras

2008

Energy Exploration & Exploitation

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This paper presents an application of genetic algorithms to forecast short-term demand of natural gas in residences. Residential demand is assumed to be a function of time, heating degree-day value, and consumer price index. A genetic algorithm is designed to estimate parameters of a multiple nonlinear regression model which mathematically represents the relationship between natural gas consumption and influential variables. Genetic algorithms have recently received attention as robust stochastic search algorithms to solve various forecasting problems since they have several significant advantages over conventional methods. Without requiring assumptions need to be made about the underlying function or model, genetic algorithms can attain proper solutions by scanning solution space from many different starting point. To show the applicability and superiority of the described approach, it is considered the monthly data of the residential sector which consumes 23% of imported natural gas in Turkey. The results have revealed that genetic algorithms can be used as an alternative solution approach to forecast the future demand of natural gas.

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“…This makes natural gas the preferred fuel of choice for three reasons. First, natural gas is cheaper than petroleum and cleaner than both coal and petroleum (Aras and Aras, 2004). In addition, due to its efficiency and attractive qualities, natural gas is suitable for power generation.…”

Section: Introductionmentioning

confidence: 99%

Accounting for the Effect of Exogenous Non-Economic Variables on Natural Gas Demand in Oil Producing African Countries

Ackah

2017

SSRN Journal

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With less than 50% of the population of Sub-Saharan Africa having access to modern forms of energy and the desire to minimize the impact of energy consumption on the environment, there is a need to invest in an energy source that is affordable, available and environmentally clean. Natural gas is therefore the preferred choice of energy since it has minimum impact on the environment among the fossil fuels and is relatively affordable. In this study, the generalized methods of moments (GMM), a dynamic panel two stage least squares and the general unrestricted model (GUM) through Autometrics TM are employed to estimate the demand for natural gas in oil producing African countries. The GUM suggests that oil producing African countries have been consuming excessive natural gas that seems to offset improved technical efficiency of appliances. The study finds that both economic and technical factors are important drivers of natural gas consumption. In particular, output exerts a positive and significant influence on natural gas demand, whereas energy price and energy resource depletion have a negative and significant effect. It is recommended that energy efficiency measures should be implemented in these countries.

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Forecasting Residential Natural Gas Demand

Cited by 76 publications

References 0 publications

Forecasting peak-day consumption for year-ahead management of natural gas networks

Forecasting peak-day consumption for year-ahead management of natural gas networks

Forecasting Residential Consumption of Natural Gas Using Genetic Algorithms

Accounting for the Effect of Exogenous Non-Economic Variables on Natural Gas Demand in Oil Producing African Countries

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