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

Oliver, Ronan; Duffy, Aidan; Enright, Bernard; O'Connor, Rodger

doi:10.1016/j.jup.2016.10.006

Cited by 13 publications

(6 citation statements)

References 13 publications

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“…The second approach fits a distribution to historical weather [9,14,15]. These methods can use the entire history of recorded weather to determine the design day conditions.…”

Section: Methods For Determining Design Day Conditionsmentioning

confidence: 99%

“…Oliver et al [9] set out three ways that utilities define design day conditions: the quantifiable metric of extreme weather (i.e., temperature); the odds of the event occurring (i.e., 1-in-30 years); and the period over which the extreme weather occurs (i.e., day, week).…”

Section: Design Day Conditionsmentioning

confidence: 99%

“…Other weather variables such as wind do not have as high a correlation with natural gas consumption with an r 2 of 0.22. However, the wind can be used as an adjustment for temperature [12], as can prior day temperature [13], and solar radiation [9]. Figure 2 below illustrates the lack of data at extremely low temperatures and flows, as seen on the right-hand side of the figure.…”

Section: Design Day Conditionsmentioning

confidence: 99%

“…Table 1, below, describes the 1-in-N year probability used by different utilities according to four surveys. The Oliver survey counts the number of European countries using each range of N [9]. The other three surveys account for utilities in the United States.…”

Section: Design Day Conditionsmentioning

confidence: 99%

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A Surrogate Weather Generator for Estimating Natural Gas Design Day Conditions

et al. 2021

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Natural gas customers rely upon utilities to provide gas for heating in the coldest parts of winter. Heating capacity is expensive, so utilities and end users (represented by commissions) must agree on the coldest day on which a utility is expected to meet demand. The return period of such a day is long relative to the amount of weather data that are typically available. This paper develops a weather resampling method called the Surrogate Weather Resampler, which creates a large dataset to support analysis of extremely infrequent events. While most current methods for generating weather data are based on simulation, this method resamples the deviations from typical weather. The paper also shows how extreme temperatures are strongly correlated to the demand for natural gas. The Surrogate Weather Resampler was compared in-sample and out-of-sample to the WeaGETS weather generator using both the Kolmogorov–Smirnov test and an exceedance-based test for cold weather generation. A naïve benchmark was also examined. These methods studied weather data from the National Oceanic and Atmospheric Administration and AccuWeather. Weather data were collected for 33 weather stations across North America, with 69 years of data from each weather station. We show that the Surrogate Weather Resampler can reproduce the cold tail of distribution better than the naïve benchmark and WeaGETS.

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“…The second approach fits a distribution to historical weather [9,14,15]. These methods can use the entire history of recorded weather to determine the design day conditions.…”

Section: Methods For Determining Design Day Conditionsmentioning

confidence: 99%

Section: Design Day Conditionsmentioning

confidence: 99%

Section: Design Day Conditionsmentioning

confidence: 99%

Section: Design Day Conditionsmentioning

confidence: 99%

See 2 more Smart Citations

A Surrogate Weather Generator for Estimating Natural Gas Design Day Conditions

et al. 2021

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“…Oliver [51] developed a model with an adjusted network degree day for variable climate conditions (NDDCA). Solar radiation has also been shown to affect consumption significantly.…”

Section: Prediction Accuracy Of Selected Applied Models In the Periodmentioning

confidence: 99%

Application of Predictive Models for Natural Gas Needs - Current State and Future Trends Review

et al. 2020

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Nowadays, in terms of trading on the world scale, to foresee a natural gas consumption represents an essential activity. In the first part, the paper examines the current state of the Serbian natural gas sector and methodology applied for prediction and capacity planning. In addition, the study intends to give a comprehensive assessment of predictive algorithms for natural gas needs involved in the last decade with projections and suggestions for future applications. The primary task is to evaluate used predictive models with an emphasis on the accuracy of the predictions obtained. Additionally, the paper will analyse used parameters, consumption scale, prediction scope, forecast algorithms, and other related information. The main objective of this study is to review the new-fangled information related analyses data from peer-reviewed journals, international conferences, and books.

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What is the short-term outlook for the EU’s natural gas demand? Individual differences and general trends based on monthly forecasts

Wang

et al. 2022

Environ Sci Pollut Res

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

Cited by 13 publications

References 13 publications

A Surrogate Weather Generator for Estimating Natural Gas Design Day Conditions

A Surrogate Weather Generator for Estimating Natural Gas Design Day Conditions

Application of Predictive Models for Natural Gas Needs - Current State and Future Trends Review

What is the short-term outlook for the EU’s natural gas demand? Individual differences and general trends based on monthly forecasts

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