Forecasting natural gas consumption

Soldo, Božidar

doi:10.1016/j.apenergy.2011.11.003

Cited by 203 publications

(101 citation statements)

References 59 publications

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“…Investigation of these studies showed that, mostly independent variable included regional-based natural gas consumption prediction is done. The summary of this research is published by Soldo [32].…”

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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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%

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

Akpınar

Yumuşak

2016

Energies

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“…Natural gas was produced from coal when it was first used in commercial quantities in Britain in 1785 (Soldo, 2012). Soon after, natural gas was obtained by drilling and attracted less attention since associated gas was mostly flared.…”

Section: Literature Reviewmentioning

confidence: 99%

Accounting for the Effect of Exogenous Non-Economic Variables on Natural Gas Demand in Oil Producing African Countries

Ackah

2017

SSRN Journal

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With less than 50% of the population of Sub-Saharan Africa having access to modern forms of energy and the desire to minimize the impact of energy consumption on the environment, there is a need to invest in an energy source that is affordable, available and environmentally clean. Natural gas is therefore the preferred choice of energy since it has minimum impact on the environment among the fossil fuels and is relatively affordable. In this study, the generalized methods of moments (GMM), a dynamic panel two stage least squares and the general unrestricted model (GUM) through Autometrics TM are employed to estimate the demand for natural gas in oil producing African countries. The GUM suggests that oil producing African countries have been consuming excessive natural gas that seems to offset improved technical efficiency of appliances. The study finds that both economic and technical factors are important drivers of natural gas consumption. In particular, output exerts a positive and significant influence on natural gas demand, whereas energy price and energy resource depletion have a negative and significant effect. It is recommended that energy efficiency measures should be implemented in these countries.

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“…Degree days have been vastly used by researchers for the purpose of normalization of energy consumption in different sectors [17][18][19][20][21]. Data of Heating Degree Days (HDD)s for the London region were available from [22].…”

Section: Weather Normalization Of Energy Usagementioning

confidence: 99%

The Significance of a Building’s Energy Consumption Profiles for the Optimum Sizing of a Combined Heat and Power (CHP) System—A Case Study for a Student Residence Hall

et al. 2018

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University buildings, such as student residence halls with year-round consistent energy demands, offer strong opportunities for Combined Heat and Power (CHP) systems. The economic and environmental feasibility of a CHP project is strongly linked with its optimum sizing. This study aims to undertake such an assessment for a CHP system for a student residence hall located in London, the United Kingdom (UK). The study also aims to undertake a sensitivity analysis to investigate the effect of different parameters on the project's economics. Necessary data are collected via interviews with the University's Energy Manager. Modeling of the CHP system is performed using the London South Bank University (LSBU, London, the UK) CHP model. Results demonstrate that optimum sizing of CHP is crucial for achieving higher economic and environmental benefits and strongly depends on the authenticity of the energy consumption data, based on which the CHP is being sized. Use of incorrect energy data could result in an undersized or oversized CHP system, where an oversized system will result in higher negative results compared to an undersized system. Finally, Monto Carlo statistical analysis shows that electricity price is the significant factor that could affect the project's economics. With an increasing spark gap, the payback period decreases, and vice versa.

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Forecasting natural gas consumption

Cited by 203 publications

References 59 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

Accounting for the Effect of Exogenous Non-Economic Variables on Natural Gas Demand in Oil Producing African Countries

The Significance of a Building’s Energy Consumption Profiles for the Optimum Sizing of a Combined Heat and Power (CHP) System—A Case Study for a Student Residence Hall

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