Prediction of natural gas demand by considering implications of energy-related policies: The case of Türkiye

IF 3.1 4区 工程技术 Q3 ENERGY & FUELS Energy Sources Part B-Economics Planning and Policy Pub Date : 2023-11-14 DOI:10.1080/15567249.2023.2274865
Huseyin Avni ES, Pınar Baban, Coskun Hamzacebi
{"title":"Prediction of natural gas demand by considering implications of energy-related policies: The case of Türkiye","authors":"Huseyin Avni ES, Pınar Baban, Coskun Hamzacebi","doi":"10.1080/15567249.2023.2274865","DOIUrl":null,"url":null,"abstract":"ABSTRACTThe most important key to determining sustainable energy policies is reliable and accurate energy demand forecasting. Grey prediction, one of the demand forecasting methods, makes successful forecasts with limited data and without the need for any prior knowledge. In this study, GM (1,1) and Grey Verhulst from time series models and GM (1, N) model based on cause–effect relationship were used to forecast Türkiye’s natural gas demand. Dynamic grey forecasting models have been developed in order to obtain robust and reliable predictions. All developed models were evaluated according to performance criteria, and the superior model was obtained to be GM (1,5). Within the scope of the study, it was determined that models based on cause–effect relationship should be preferred instead of time series models. For this, population, the amount of electricity generated from natural gas, industrial production index, and building area are determined as independent variables. Three different scenarios were created taking into account the specified independent variables, and natural gas demand until 2025 was obtained. According to the forecasting results, Türkiye’s natural gas demand for 2025 will happen as an interval between 29.61 and 53.62 mtoe based on the low and high scenarios, respectively. According to the expected scenario, this demand would be realized around 40 mtoe for Türkiye in the year 2025. Finally, Grey Dynamic Decision Support System was designed and introduced to easily apply the dynamic grey models by end-users.KEYWORDS: Energy policiesforecastingGrey Dynamic decision support systemGrey prediction modelsNatural gas Disclosure statementNo potential conflict of interest was reported by the author(s).Ethical approvalThis article does not include any studies with human participants or animals performed by any of the authors.","PeriodicalId":51247,"journal":{"name":"Energy Sources Part B-Economics Planning and Policy","volume":"9 7","pages":"0"},"PeriodicalIF":3.1000,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Energy Sources Part B-Economics Planning and Policy","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/15567249.2023.2274865","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0

Abstract

ABSTRACTThe most important key to determining sustainable energy policies is reliable and accurate energy demand forecasting. Grey prediction, one of the demand forecasting methods, makes successful forecasts with limited data and without the need for any prior knowledge. In this study, GM (1,1) and Grey Verhulst from time series models and GM (1, N) model based on cause–effect relationship were used to forecast Türkiye’s natural gas demand. Dynamic grey forecasting models have been developed in order to obtain robust and reliable predictions. All developed models were evaluated according to performance criteria, and the superior model was obtained to be GM (1,5). Within the scope of the study, it was determined that models based on cause–effect relationship should be preferred instead of time series models. For this, population, the amount of electricity generated from natural gas, industrial production index, and building area are determined as independent variables. Three different scenarios were created taking into account the specified independent variables, and natural gas demand until 2025 was obtained. According to the forecasting results, Türkiye’s natural gas demand for 2025 will happen as an interval between 29.61 and 53.62 mtoe based on the low and high scenarios, respectively. According to the expected scenario, this demand would be realized around 40 mtoe for Türkiye in the year 2025. Finally, Grey Dynamic Decision Support System was designed and introduced to easily apply the dynamic grey models by end-users.KEYWORDS: Energy policiesforecastingGrey Dynamic decision support systemGrey prediction modelsNatural gas Disclosure statementNo potential conflict of interest was reported by the author(s).Ethical approvalThis article does not include any studies with human participants or animals performed by any of the authors.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
考虑能源相关政策影响的天然气需求预测:以 rkiye为例
摘要可靠、准确的能源需求预测是制定可持续能源政策的关键。灰色预测是需求预测的一种方法,它在数据有限的情况下,不需要任何先验知识,就能成功地进行预测。本研究采用时间序列模型GM(1,1)和Grey Verhulst模型以及基于因果关系的GM (1, N)模型对 rkiye天然气需求进行预测。为了获得稳健可靠的预测结果,建立了动态灰色预测模型。根据性能标准对所开发的模型进行评价,优选GM模型(1,5)。在研究范围内,确定了基于因果关系的模型,而不是时间序列模型。为此,确定人口、天然气发电量、工业生产指数、建筑面积为自变量。考虑到指定的自变量,创建了三种不同的情景,并获得了到2025年的天然气需求。根据预测结果,根据低情景和高情景,2025年 rkiye的天然气需求将分别发生在29.61至53.62百万吨油当量之间。根据预期的情景,到2025年,这种需求将在4000万吨左右实现。最后,设计并引入灰色动态决策支持系统,方便终端用户应用动态灰色模型。关键词:能源政策预测灰色动态决策支持系统灰色预测模型天然气披露声明作者未报告潜在利益冲突伦理批准本文不包括任何作者进行的任何人类参与者或动物研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
6.80
自引率
12.80%
发文量
42
审稿时长
6-12 weeks
期刊介绍: 12 issues per year Abstracted and/or indexed in: Applied Science & Technology Index; API Abstracts/Literature; Automatic Subject Index Citation; BIOSIS Previews; Cabell’s Directory of Publishing Opportunities in Economics and Finance; Chemical Abstracts; CSA Aquatic Science & Fisheries Abstracts; CSA Environmental Sciences & Pollution Management Database; CSA Pollution Abstracts; Current Contents/Engineering, Technology & Applied Sciences; Directory of Industry Data Sources; Economic Abstracts; Electrical and Electronics Abstracts; Energy Information Abstracts; Energy Research Abstracts; Engineering Index Monthly; Environmental Abstracts; Environmental Periodicals Bibliography (EPB); International Abstracts in Operations Research; Operations/Research/Management Science Abstracts; Petroleum Abstracts; Physikalische Berichte; and Science Citation Index. Taylor & Francis make every effort to ensure the accuracy of all the information (the "Content") contained in our publications. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor & Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to, or arising out of the use of the Content. Terms & Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions .
期刊最新文献
Prediction of natural gas demand by considering implications of energy-related policies: The case of Türkiye Towards 2050 net zero carbon infrastructure: a critical review of key decarbonization challenges in the domestic heating sector in the UK The impact of the oil price on mineable and non-mineable cryptocurrencies A comprehensive model to explain consumers’ purchasing intention of energy-efficient household appliances: A case study in China Techno-economic assessment of low-carbon hydrogen exports from the Middle East to the Asia-Pacific, and Europe
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1