Forecasting of daily natural gas consumption on regional basis in Turkey using various computational methods

Taşpınar, Fatih; Çelebi, Numan; Tutkun, Nedim

doi:10.1016/j.enbuild.2012.10.023

Cited by 116 publications

(61 citation statements)

References 24 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%

“…In the first group, publications can be divided according to the use of timeframes which apply the time series method in daily periods [6][7][8][9][10][11][12][13][14][15][16][17][18][19] and monthly periods [20][21][22][23][24]. 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.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

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%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

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

Akpınar

Yumuşak

2016

Energies

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“…There are also related studies in Turkey, too. These are: BEP-TR, BEP-HY, ENVER, SmartSpaces (Saving Energy in Europe's Public Buildings using ICT) Project, Evaluation of Current Situation of Buildings Sector Report by Tülin Keskin (August 2010), TÜİK data related energy consuming [15]- [17].…”

Section: Turkey Applicationsmentioning

confidence: 99%

Commercial Buildings Energy Consumption Survey (CBECS) and Its Comparison with Turkey Applications

Boydak¹

2017

JOCET

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Abstract-This paper is about commercial buildings energy consumption survey (CBECS) benchmarking database consept, its applications and related applications in Turkey. CBECS is useful for energy efficiency in new commercial building constructions, which is a key target to control and lower U.S. nation-wide energy use.Determining energy consumption of buildings is a key target for energy efficiency. Building energy efficiency is crucial especially for economical and climate change concerns. Improving energy efficiency in commercial buildings is useful in order to decrease energy use, owner operating costs, and carbon footprint of the buildings.The aims are to estimate life-cycle energy savings, cost performance of energy efficiency and carbon emission reduction in new commercial buildings utilizing an integrated design, and estimate results. Obtaining energy consumption of buildings can be utilized to decrease energy usage in new commercial buildings by 20-30% on average and up to over 40%. Thus, improved efficiencies permit smaller, cheaper HVAC equipment usage.

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“…Akpinar and Yumusak [8] used the Holt-Winters exponential smoothing method and the autoregressive integrated moving average (ARIMA) model to forecast natural gas demand. Taşpinar et al [9] used a seasonal ARIMAX model to predict daily natural gas consumption.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Natural Gas Consumption in Different Regions of China Using a Hybrid MVO-NNGBM Model

Wang

Luo

Liu

et al. 2017

Mathematical Problems in Engineering

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The accurate and reasonable prediction of natural gas consumption is significant for the government to formulate energy planning. To this end, we use the multiverse optimizer (MVO) algorithm to optimize the parameters of the Nash nonlinear grey Bernoulli model (NNGBM (1,1)) and propose a hybrid MVO-NNGBM model to predict the natural gas consumption in 30 regions of China. The results indicate that the prediction precision of the hybrid MVO-NNGBM model is better than that of other grey-based models. According to the forecast results, China's natural gas consumption will grow rapidly over the next five years and reach 354.1 billion cubic meters (bcm) by 2020. Moreover, the spatial distribution of natural gas consumption will shift from being supply oriented towards being demand driven and will be mainly concentrated in coastal and developed provinces.

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Forecasting of daily natural gas consumption on regional basis in Turkey using various computational methods

Cited by 116 publications

References 24 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

Commercial Buildings Energy Consumption Survey (CBECS) and Its Comparison with Turkey Applications

Prediction of Natural Gas Consumption in Different Regions of China Using a Hybrid MVO-NNGBM Model

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