Natural gas demand in Turkey

Erdoğdu, Erkan

doi:10.1016/j.apenergy.2009.07.006

Cited by 170 publications

(81 citation statements)

References 34 publications

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“…The historical publication of Box and Jenkins [24] and the later sequels of the same publication provides the stepwise procedure for the ARMA analysis [8,69]. The step followed in this are same as of followed in various studies.…”

Section: Autoregressive Integrated Moving Average (Arima) and Holt-wimentioning

confidence: 99%

“…One of the most important tools is the visualization of time series graphs and then observing the correlogram specifically. This correlogram of auto-correlation and partial auto-correlation provided information about the appropriate orders of MA and AR respectively drawn on the basis of lags involved [6,8]. The resulting correlograms are simply the plots of ACF and PACF against the lag length.…”

Section: Autoregressive Integrated Moving Average (Arima) and Holt-wimentioning

confidence: 99%

“…The resulting correlograms are simply the plots of ACF and PACF against the lag length. The ACF at lag k, denoted by ρ k , is defined as [7,8]:…”

Section: Autoregressive Integrated Moving Average (Arima) and Holt-wimentioning

confidence: 99%

“…(1) Time series data is differentiated in order to make it stationary first which is achieved by making both variances and mean constant [10]; (2) Determining p and q orders by studying the autocorrelation and partial autocorrelation coefficients [13,14]; (3) Validating the selected models by applying diagnostic tests that provide information about the residuals being white noise [2,8]. Detail of each of these steps is given under:…”

Section: Autoregressive Integrated Moving Average (Arima) and Holt-wimentioning

confidence: 99%

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An Integrated Modeling Approach for Forecasting Long-Term Energy Demand in Pakistan

et al. 2017

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Abstract:Energy planning and policy development require an in-depth assessment of energy resources and long-term demand forecast estimates. Pakistan, unfortunately, lacks reliable data on its energy resources as well do not have dependable long-term energy demand forecasts. As a result, the policy makers could not come up with an effective energy policy in the history of the country. Energy demand forecast has attained greatest ever attention in the perspective of growing population and diminishing fossil fuel resources. In this study, Pakistan's energy demand forecast for electricity, natural gas, oil, coal and LPG across all the sectors of the economy have been undertaken. Three different energy demand forecasting methodologies, i.e., Autoregressive Integrated Moving Average (ARIMA), Holt-Winter and Long-range Energy Alternate Planning (LEAP) model were used. The demand forecast estimates of each of these methods were compared using annual energy demand data. The results of this study suggest that ARIMA is more appropriate for energy demand forecasting for Pakistan compared to Holt-Winter model and LEAP model. It is estimated that industrial sector's demand shall be highest in the year 2035 followed by transport and domestic sectors. The results further suggest that energy fuel mix will change considerably, such that oil will be the most highly consumed energy form (38.16%) followed by natural gas (36.57%), electricity (16.22%), coal (7.52%) and LPG (1.52%) in 2035. In view of higher demand forecast of fossil fuels consumption, this study recommends that government should take the initiative for harnessing renewable energy resources for meeting future energy demand to not only avert huge import bill but also achieving energy security and sustainability in the long run.

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Section: Autoregressive Integrated Moving Average (Arima) and Holt-wimentioning

confidence: 99%

Section: Autoregressive Integrated Moving Average (Arima) and Holt-wimentioning

confidence: 99%

“…The resulting correlograms are simply the plots of ACF and PACF against the lag length. The ACF at lag k, denoted by ρ k , is defined as [7,8]:…”

Section: Autoregressive Integrated Moving Average (Arima) and Holt-wimentioning

confidence: 99%

Section: Autoregressive Integrated Moving Average (Arima) and Holt-wimentioning

confidence: 99%

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An Integrated Modeling Approach for Forecasting Long-Term Energy Demand in Pakistan

et al. 2017

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“…Unakıtan and Türkekul [15] also assessed annual time series data for energy consumption using the ARIMA model. Erdoğdu [16] studied on the future growth of gas demand in Turkey and developed a forecasting model using an ARIMA framework. However, time-series ARIMA forecasting considers only the past values without showing determinants explicitly.…”

Section: Introductionmentioning

confidence: 99%

A Framework for Sustainable Urban Water Management through Demand and Supply Forecasting: The Case of Istanbul

et al. 2015

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Abstract:The metropolitan city of Istanbul is becoming overcrowded and the demand for clean water is steeply rising in the city. The use of analytical approaches has become more and more critical for forecasting the water supply and demand balance in the long run. In this research, Istanbul's water supply and demand data is collected for the period during 2006 and 2014. Then, using an autoregressive integrated moving average (ARIMA) model, the time series water supply and demand forecasting model is constructed for the period between 2015 and 2018. Three important sustainability metrics such as water loss to supply ratio, water loss to demand ratio, and water loss to residential demand ratio are also presented. The findings show that residential water demand is responsible for nearly 80% of total water use and the consumption categories including commercial, industrial, agriculture, outdoor, and others have a lower share in total water demand. The results also show that there is a considerable water loss in the water distribution system which requires significant investments on the water supply networks. Furthermore, the forecasting results indicated that pipeline projects will be critical in the near future due to expected increases in the total water demand of Istanbul. The authors suggest that sustainable management of water can be achieved by reducing the residential water use through the use of water efficient OPEN ACCESSSustainability 2015, 7 11051 technologies in households and reduction in water supply loss through investments on distribution infrastructure.

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Deep learning for spatio‐temporal supply and demand forecasting in natural gas transmission networks

Petković

Koch

Zittel

2021

Energy Science & Engineering

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Germany is the largest market for natural gas in the European Union, with an annual consumption of approx. 95 billion cubic meters. The German high‐pressure gas pipeline network's length is roughly 40 000 km, which enables highly fluctuating quantities of gas to be transported safely over long distances. Considering that similar amounts of gas are also transshipped through Germany to other EU states, it is clear that Germany's gas transport system is essential to the European energy supply. Since the average velocity of gas in a pipeline is only 25 km/h, an adequate high‐precision, high‐frequency forecasting of supply and demand is crucial for efficient control and operation of such a transmission network. We propose a deep learning model based on spatio‐temporal convolutional neural networks (DLST) to tackle the problem of gas flow forecasting in a complex high‐pressure transmission network. Experiments show that our model effectively captures comprehensive spatio‐temporal correlations through modeling gas networks and consistently outperforms state‐of‐the‐art benchmarks on real‐world data sets by at least 15%. The results demonstrate that the proposed model can deal with complex nonlinear gas network flow forecasting with high accuracy and effectiveness.

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Natural gas demand in Turkey

Cited by 170 publications

References 34 publications

An Integrated Modeling Approach for Forecasting Long-Term Energy Demand in Pakistan

An Integrated Modeling Approach for Forecasting Long-Term Energy Demand in Pakistan

A Framework for Sustainable Urban Water Management through Demand and Supply Forecasting: The Case of Istanbul

Deep learning for spatio‐temporal supply and demand forecasting in natural gas transmission networks

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