Açık Akademik Arşiv Sistemi

Year Ahead Demand Forecast of City Natural Gas Using Seasonal Time Series Methods

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dc.contributor.authors Akpinar, M; Yumusak, N;
dc.date.accessioned 2020-01-13T07:57:00Z
dc.date.available 2020-01-13T07:57:00Z
dc.date.issued 2016
dc.identifier.citation Akpinar, M; Yumusak, N; (2016). Year Ahead Demand Forecast of City Natural Gas Using Seasonal Time Series Methods. ENERGIES, 9, -
dc.identifier.issn 1996-1073
dc.identifier.uri https://hdl.handle.net/20.500.12619/2453
dc.identifier.uri http://doi.org/10.3390/en9090727
dc.description.abstract Consumption of natural gas, a major clean energy source, increases as energy demand increases. We studied specifically the Turkish natural gas market. Turkey's natural gas consumption increased as well in parallel with the world's over the last decade. This consumption growth in Turkey has led to the formation of a market structure for the natural gas industry. This significant increase requires additional investments since a rise in consumption capacity is expected. One of the reasons for the consumption increase is the user-based natural gas consumption influence. This effect yields imbalances in demand forecasts and if the error rates are out of bounds, penalties may occur. In this paper, three univariate statistical methods, which have not been previously investigated for mid-term year-ahead monthly natural gas forecasting, are used to forecast natural gas demand in Turkey's Sakarya province. Residential and low-consumption commercial data is used, which may contain seasonality. The goal of this paper is minimizing more or less gas tractions on mid-term consumption while improving the accuracy of demand forecasting. In forecasting models, seasonality and single variable impacts reinforce forecasts. This paper studies time series decomposition, Holt-Winters exponential smoothing and autoregressive integrated moving average (ARIMA) methods. Here, 2011-2014 monthly data were prepared and divided into two series. The first series is 2011-2013 monthly data used for finding seasonal effects and model requirements. The second series is 2014 monthly data used for forecasting. For the ARIMA method, a stationary series was prepared and transformation process prior to forecasting was done. Forecasting results confirmed that as the computation complexity of the model increases, forecasting accuracy increases with lower error rates. Also, forecasting errors and the coefficients of determination values give more consistent results. Consequently, when there is only consumption data in hand, all methods provide satisfying results and the differences between each method is very low. If a statistical software tool is not used, time series decomposition, the most primitive method, orWinters exponential smoothing requiring little mathematical knowledge for natural gas demand forecasting can be used with spreadsheet software. A statistical software tool containing ARIMA will obtain the best results.
dc.description.uri https://doi.org/10.3390/en9090727
dc.language English
dc.publisher MDPI
dc.subject Energy & Fuels
dc.title Year Ahead Demand Forecast of City Natural Gas Using Seasonal Time Series Methods
dc.type Article
dc.identifier.volume 9
dc.contributor.department Sakarya Üniversitesi/Bilgisayar Ve Bilişim Bilimleri Fakültesi/Yazılım Mühendisliği Bölümü
dc.contributor.saüauthor Akpınar, Mustafa
dc.contributor.saüauthor Yumuşak, Nejat
dc.relation.journal ENERGIES
dc.identifier.wos WOS:000383547900064
dc.identifier.doi 10.3390/en9090727
dc.contributor.author Akpınar, Mustafa
dc.contributor.author Yumuşak, Nejat


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