prikaz prve stranice dokumenta Short-term forecasting of natural gas consumption by determining the statistical distribution of consumption data
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Scientific paper - Original scientific paper
Short-term forecasting of natural gas consumption by determining the statistical distribution of consumption data
Energy Reports, 10 (2023); 2352-2360. https://doi.org/10.1016/j.egyr.2023.09.051
Smajla, Ivan; Vulin, Domagoj; Karasalihović Sedlar, Daria

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Smajla, I., Vulin, D. & Karasalihović Sedlar, D. (2023). Short-term forecasting of natural gas consumption by determining the statistical distribution of consumption data. Energy Reports, 10., 2352-2360. doi: 10.1016/j.egyr.2023.09.051

Smajla, Ivan, et al. "Short-term forecasting of natural gas consumption by determining the statistical distribution of consumption data." Energy Reports, vol. 10, 2023, pp. 2352-2360. https://doi.org/10.1016/j.egyr.2023.09.051

Smajla, Ivan, Domagoj Vulin and Daria Karasalihović Sedlar. "Short-term forecasting of natural gas consumption by determining the statistical distribution of consumption data." Energy Reports 10 (2023): 2352-2360. https://doi.org/10.1016/j.egyr.2023.09.051

Smajla, I., Vulin, D. and Karasalihović Sedlar, D. (2023) 'Short-term forecasting of natural gas consumption by determining the statistical distribution of consumption data', Energy Reports, 10, pp. 2352-2360. doi: 10.1016/j.egyr.2023.09.051

Smajla I, Vulin D, Karasalihović Sedlar D. Short-term forecasting of natural gas consumption by determining the statistical distribution of consumption data. Energy Reports [Internet]. 2023 November [cited 2025 February 27];10:2352-2360. doi: 10.1016/j.egyr.2023.09.051

I. Smajla, D. Vulin and D. Karasalihović Sedlar, "Short-term forecasting of natural gas consumption by determining the statistical distribution of consumption data", Energy Reports, vol. 10, pp. 2352-2360, November 2023. [Online]. Available at: https://urn.nsk.hr/urn:nbn:hr:169:607561. [Accessed: 27 February 2025]

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Metadata
Title (english)Short-term forecasting of natural gas consumption by determining the statistical distribution of consumption data
AuthorIvan Smajla
AuthorDomagoj Vulin
AuthorDaria Karasalihović Sedlar
Author's institutionUniversity of Zagreb
Faculty of Mining, Geology and Petroleum Engineering
(Department of Petroleum and Gas Engineering and energy)
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Mining, Petroleum and Geology Engineering
Abstract (english)
The development of gas smart meters has enabled the collection of data on daily natural gas consumption which can be used to develop and improve methods and models for natural gas consumption forecasting. This paper presents the development of a model for the short-term forecasting of total natural gas consumption, which is applicable in different distribution areas where smart meters are installed in large numbers. The advantages of this model are the use of only two input parameters (daily natural gas consumption and average daily temperature), forecasting the total consumption in the determined area by analyzing the consumption data of less than 10% of the total consumers as well as robustness to consumer types. Daily natural gas consumption data collected from the more than 3300 gas smart meters over a period of six months was used for the determination of correlations between lognormal distribution variables and temperature. The defined correlations between distribution variables and temperature were used for upscaling consumption to a specific number of final consumers, i.e., to obtain the total consumption of natural gas in the observed area. Best results were achieved using the ‘‘two-day rolling average temperature’’ in the consumption scenario up to 250 m3 per day (MAPE was 7.26%). When compared to using ‘‘average temperature’’ as an input parameter, ‘‘two-day rolling average temperature’’ and ‘‘shaving peaks temperature’’ produced better results due to the mitigated impact of sudden temperature changes that significantly affected the simulated consumption in the model while the actual consumption is a little more inert. Also, consumption scenarios up to 250 m3 can be considered the most representative for forecasting total natural gas consumption since it achieved the best results.
Keywords (english)
Languageenglish
Publication typeScientific paper - Original scientific paper
Publication statusPublished
Peer reviewPeer review - international
Publication versionPublished version
Journal titleEnergy Reports
Numberingvol. 10, pp. 2352-2360
p-ISSN2352-4847
DOIhttps://doi.org/10.1016/j.egyr.2023.09.051
URN:NBNurn:nbn:hr:169:607561
Publication2023-11
Document URLhttps://linkinghub.elsevier.com/retrieve/pii/S235248472301288X
Type of resourceText
Access conditionsOpen access
Terms of useLicence Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) icon
Created on2025-02-27 13:46:51