Estimation of natural-gas consumption in Poland based on the logistic-curve interpretation

Siemek, J.; Nagy, Stanisław; Rychlicki, S.

doi:10.1016/s0306-2619(03)00013-8

Cited by 56 publications

(25 citation statements)

References 5 publications

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“…In the second group, publications can be grouped as regional [8][9][10][11][12]15,18,19,[21][22][23][24] or national [6,7,13,14,20,[24][25][26][27][28][29][30][31] consumptions are investigated. In the third group, papers are investigated by consumer types.…”

Section: Related Workmentioning

confidence: 99%

“…This group includes household consumers [6][7][8][9][10][11][12], commercial consumers [11,13,25] and consumers where all consumption sectors are included [14][15][16][17][18][24][25][26][27][28][29][30][31]. In the fourth group, where studies are categorized by data used, papers are divided with respect to the use of only consumption data using univariate approaches [28][29][30][31] or independent variable [6][7][8][9][10][11][12][13][14][15][16][17][18]20,[22][23][24][25][26][27] included studies. Investigation of these studies showed that, mostly independent variable included regional-based natural gas consumption prediction is done.…”

Section: Related Workmentioning

confidence: 99%

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

Akpınar

Yumuşak

2016

Energies

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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, or Winters 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.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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

Akpınar

Yumuşak

2016

Energies

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“…In 2007, industrial use accounted for 54% of the Brazilian natural gas demand [15]. The participation of home consumption in Brazil's energy matrix is very small when compared with countries such as Poland [16], Iran [9] and Turkey [17]. Aydinalp et al [18] used neural network (NN) to model residential energy consumption in Canada.…”

Section: Consumption Of Natural Gasmentioning

confidence: 99%

Estimation of Natural Gas Production, Import and Consumption in Brazil Based on Three Mathematical Models

Gracias¹,

Lourenço²,

Rafikov³

2012

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A mathematical model capable of providing a forecast of future consumption and import of natural gas is essential for the planning of the Brazilian energy matrix. The aim of this study is to compare three mathematical models, logistic model or model of Verhulst, exponential model or the model of Malthus and the model of von Bertalanffy to analyze the possibilities of these models to describe the evolution of production, import and consumption of natural gas in Brazil, from data provided by the energy balance of the Ministry of Mines and Energy (MME) from 1970 to 2009. A projection of the production and the import of natural gas up to 2017 is made with the models studied in this article and compared with the Brazilian Ten-Year Plan for Expansion of Energy (PDE). At the end of this paper a comparison with the Hubbert model for Brazilian natural gas production is made. These data were adjusted to use the differential equations which describe the models of population growth. All the computer work used in this article: graphics, resolution of differential equations, calculations of linearization and the least squares fitting was prepared in the software MatLab. The results obtained by means of graphs show that the population dynamics models (logistic, exponential and von Bertalanffy) can be applied in modeling the production, import and consumption of natural gas in Brazil.

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“…Among these routines are NLP and GA. These are indeed the routines used in this paper, for different data sets, to minimize the cost function given by (6) subject to the constraints given by (7). The results are presented in the next section.…”

Section: The Proposed Proceduresmentioning

confidence: 99%

“…Eltony [5] has forecasted natural gas demand in Kuwait by means of econometric models [5]. Siemek [6] have estimated natural gas consumption in Poland based on the logistic curve interpretation [6]. Sarak and Satman [7] have described a deterministic model to forecast natural gas consumption for residential heating in certain areas of Turkey [7] based on previous studies performed by Durmayaz et al [8].…”

Section: Introductionmentioning

confidence: 99%

Modeling and estimation of the natural gas consumption for residential and commercial sectors in Iran

et al. 2010

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Estimation of natural-gas consumption in Poland based on the logistic-curve interpretation

Cited by 56 publications

References 5 publications

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

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

Estimation of Natural Gas Production, Import and Consumption in Brazil Based on Three Mathematical Models

Modeling and estimation of the natural gas consumption for residential and commercial sectors in Iran

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