Forecasting natural gas demand in Istanbul by artificial neural networks method and planning of city gate stations
| dc.authorid | 0000-0002-2563-3707 | |
| dc.authorid | 0000-0002-0716-9234 | |
| dc.authorid | 0000-0002-3065-1942 | |
| dc.authorid | 0000-0002-5743-3937 | |
| dc.contributor.author | Balıkçı, Vedat | |
| dc.contributor.author | Gemici, Zafer | |
| dc.contributor.author | Taner, Tolga | |
| dc.contributor.author | Dalkılıç, Ahmet Selim | |
| dc.date.accessioned | 2024-07-23T10:21:25Z | |
| dc.date.available | 2024-07-23T10:21:25Z | |
| dc.date.issued | 2024 | |
| dc.department | Teknik Bilimler Meslek Yüksekokulu | |
| dc.description.abstract | In this study, daily and hourly natural gas demand for Istanbul’s Anatolian and European sides are estimated by using Artificial Neural Networks. Parameters affecting natural gas usage such as the number of consumers, average daily temperature, minimum daily temperature, official holidays, and heating degree days have been determined. By means of the data obtained from the year 2008 to the end of 2018, the forecasting model created by the MATLAB software estimates the natural gas demands up to 2027 according to the coldest day of Istanbul in the last century, which occurred on 9 February 1929, with the minimum daily temperature of -16? and the average daily temperature of -7?. As a result of this study, it is decided which natural gas city gate station will be constructed with natural gas demand forecast. When we view it from the perspective of a natural gas distributor, correct predictive values reduce the errors and make gas distribution planning correctly. In this way, gas systems become much more realistic and profitable. Also, from the customer’s point of view, because the correct predictive values reduce the errors that may occur in the system, the model minimizes the probability of being out of gas. Moreover, with Synergi Gas Software, new solutions can be produced for possible bad scenarios in advance, taking into account the velocity and pressure criteria of the distribution network located on İstanbul’s Anatolian and European sides where the natural gas city gate station will be constructed. | |
| dc.identifier.doi | 10.17341/gazimmfd.1165734 | |
| dc.identifier.endpage | 1027 | en_US |
| dc.identifier.issn | 1300-1884 | |
| dc.identifier.issue | 2 | en_US |
| dc.identifier.scopusquality | Q2 | |
| dc.identifier.startpage | 1017 | en_US |
| dc.identifier.uri | https:/dx.doi.org/10.17341/gazimmfd.1165734 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12451/12206 | |
| dc.identifier.volume | 39 | en_US |
| dc.identifier.wosquality | N/A | |
| dc.indekslendigikaynak | Web of Science | |
| dc.indekslendigikaynak | Scopus | |
| dc.language.iso | en | |
| dc.publisher | Gazi Universitesi | |
| dc.relation.ispartof | Journal of the Faculty of Engineering and Architecture of Gazi University | |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.subject | Artificial Neural Networks | |
| dc.subject | Energy | |
| dc.subject | Forecasting | |
| dc.subject | Natural Gas | |
| dc.subject | Security of Supply | |
| dc.title | Forecasting natural gas demand in Istanbul by artificial neural networks method and planning of city gate stations | |
| dc.type | Article |
