Statistical Model of Segment-Specific Relationship Between Natural Gas Consumption and Temperature in Daily and Hourly Resolution

Brabec, Marek; Malý, Marek; Pelikán, Emil; Konár, Ondřej

doi:10.5772/9855

Cited by 3 publications

(4 citation statements)

References 28 publications

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“…12 We have been involved in the SLP model development and continuous update. Details of the SLP and similar models have been described previously, eg, in other works 13,14 and in a somewhat broader context also in the work of Brabec et al 15 The model can be viewed as a practically motivated and formalized extension of the heating degree model of Sarak and Satman. 8 The statistical model, which underlies the SLP, is semiparametric and has several physically interpretable structural parts.…”

Section: Datamentioning

confidence: 99%

Semiparametric modeling of the spatiotemporal trends in natural gas consumption: Methodology, results, and consequences

Brabec

Konár

Kasanický

et al. 2019

Appl Stoch Models Bus & Ind

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Household consumption of natural gas is usually considered to be quite stable as cooking, space, and water heating belong to basic needs. The improvement of technologies together with possibilities of switching to alternative sources can, however, lead to a decreasing consumption trend. Knowing more about such trend, especially of its spatial distribution, can be useful for strategic planning. In this paper, we describe a general statistical methodology allowing to study the spatiotemporal behavior of consumption. It is based on semiparametric modeling. Formalized error and sensitivity analyses are part of the methodology. Presented methods are illustrated on large‐scale data from the Czech Republic.

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Section: Datamentioning

confidence: 99%

Semiparametric modeling of the spatiotemporal trends in natural gas consumption: Methodology, results, and consequences

Brabec

Konár

Kasanický

et al. 2019

Appl Stoch Models Bus & Ind

Self Cite

View full text Add to dashboard Cite

show abstract

“…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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“…Taking a different approach, Brabec et al . () defined a ‘daytype’ factor with five levels corresponding to different classifications of the current, previous and following days as working days or otherwise. This allows public holidays and neighbouring days to have different effects depending on where they fall in the week.…”

Section: Introductionmentioning

confidence: 99%

“…For example, in their model for gas consumption in the Czech Republic, Brabec et al (2009) included Christmas and Easter effects which apply over a fixed range of consecutive days around Christmas Day and Good Friday. Taking a different approach, Brabec et al (2010) defined a 'daytype' factor with five levels corresponding to different classifications of the current, previous and following days as working days or otherwise. This allows public holidays and neighbouring days to have different effects depending on where they fall in the week.…”

Section: Introductionmentioning

confidence: 99%

Identifying the Effect of Public Holidays on Daily Demand for Gas

Heaps

Farrow

Wilson

2019

Journal of the Royal Statistical Society Series A: Statistics in Society

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Summary To reduce operational costs and to ensure security of supply, gas distribution networks require accurate forecasts of the demand for gas. Among domestic and commercial customers, demand relates primarily to the weather and patterns of life and work. Public holidays have a pronounced effect which often spreads into neighbouring days. We call this spread the ‘proximity effect’. Traditionally, the days over which the proximity effect is felt are prespecified in fixed windows around each holiday, allowing no uncertainty in their identification. We are motivated by an application to modelling daily gas demand in two large British regions. We introduce a novel model which does not fix the days on which the proximity effect is felt. Our approach uses a four‐state, non‐homogeneous hidden Markov model, with cyclic dynamics, where the classification of days as public holidays is observed, but the assignment of days as ‘pre‐holiday’, ‘post‐holiday’ or ‘normal’ days is unknown. The number of days to the preceding and succeeding holidays guide transitions between states. We apply Bayesian inference and illustrate the benefit of our modelling approach. A version of the model is now being used by one of the UK's regional distribution networks.

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Statistical Model of Segment-Specific Relationship Between Natural Gas Consumption and Temperature in Daily and Hourly Resolution

Cited by 3 publications

References 28 publications

Semiparametric modeling of the spatiotemporal trends in natural gas consumption: Methodology, results, and consequences

Semiparametric modeling of the spatiotemporal trends in natural gas consumption: Methodology, results, and consequences

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

Identifying the Effect of Public Holidays on Daily Demand for Gas

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