A Surrogate Weather Generator for Estimating Natural Gas Design Day Conditions

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...

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Autores principales:	David Kaftan, George F. Corliss, Richard J. Povinelli, Ronald H. Brown
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	weather generator design day conditions extreme cold temperatures Technology T
Acceso en línea:	https://doaj.org/article/628e3f358c6e4789bf3ceaf9a6c61992
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spelling	oai:doaj.org-article:628e3f358c6e4789bf3ceaf9a6c619922021-11-11T15:54:36ZA Surrogate Weather Generator for Estimating Natural Gas Design Day Conditions10.3390/en142171181996-1073https://doaj.org/article/628e3f358c6e4789bf3ceaf9a6c619922021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/21/7118https://doaj.org/toc/1996-1073Natural 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.David KaftanGeorge F. CorlissRichard J. PovinelliRonald H. BrownMDPI AGarticleweather generatordesign day conditionsextreme cold temperaturesTechnologyTENEnergies, Vol 14, Iss 7118, p 7118 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	weather generator design day conditions extreme cold temperatures Technology T
spellingShingle	weather generator design day conditions extreme cold temperatures Technology T David Kaftan George F. Corliss Richard J. Povinelli Ronald H. Brown A Surrogate Weather Generator for Estimating Natural Gas Design Day Conditions
description	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.
format	article
author	David Kaftan George F. Corliss Richard J. Povinelli Ronald H. Brown
author_facet	David Kaftan George F. Corliss Richard J. Povinelli Ronald H. Brown
author_sort	David Kaftan
title	A Surrogate Weather Generator for Estimating Natural Gas Design Day Conditions
title_short	A Surrogate Weather Generator for Estimating Natural Gas Design Day Conditions
title_full	A Surrogate Weather Generator for Estimating Natural Gas Design Day Conditions
title_fullStr	A Surrogate Weather Generator for Estimating Natural Gas Design Day Conditions
title_full_unstemmed	A Surrogate Weather Generator for Estimating Natural Gas Design Day Conditions
title_sort	surrogate weather generator for estimating natural gas design day conditions
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/628e3f358c6e4789bf3ceaf9a6c61992
work_keys_str_mv	AT davidkaftan asurrogateweathergeneratorforestimatingnaturalgasdesigndayconditions AT georgefcorliss asurrogateweathergeneratorforestimatingnaturalgasdesigndayconditions AT richardjpovinelli asurrogateweathergeneratorforestimatingnaturalgasdesigndayconditions AT ronaldhbrown asurrogateweathergeneratorforestimatingnaturalgasdesigndayconditions AT davidkaftan surrogateweathergeneratorforestimatingnaturalgasdesigndayconditions AT georgefcorliss surrogateweathergeneratorforestimatingnaturalgasdesigndayconditions AT richardjpovinelli surrogateweathergeneratorforestimatingnaturalgasdesigndayconditions AT ronaldhbrown surrogateweathergeneratorforestimatingnaturalgasdesigndayconditions
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A Surrogate Weather Generator for Estimating Natural Gas Design Day Conditions

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