Forecasting Natural Gas Consumption in Turkey Using Grey Prediction

Boran, Fatih Emre

doi:10.1080/15567249.2014.893040

Cited by 64 publications

(39 citation statements)

References 15 publications

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“…Natural gas, used for heating and electricity generation, has become one of the important energy sources in the world since it provides low-level emission of greenhouse gases and has significant economic and other environmental benefits [1]. After the 1980s, Turkey has started to grow in production so its energy needs for production has dramatically grown.…”

Section: Introductionmentioning

confidence: 99%

“…Melikoğlu [11] used a logistic equation for long term natural gas demand forecasting and used a linear equation for medium-term demand forecasting. Boran [1] studied a grey prediction with a rolling mechanism to predict Turkey's NGD. Akpınar and Yumusak [12] studied time series decomposition, Holt-Winters exponential smoothing and autoregressive integrated moving average methods to forecast NGD of Sakarya province in Turkey.…”

Section: Introductionmentioning

confidence: 99%

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Artificial bee colony algorithm to forecast natural gas consumption of Turkey

Arık

2019

SN Appl. Sci.

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Natural gas is one of the most important energy sources in the world because it is cheaper and cleaner than most of its alternatives. Therefore, natural gas is used for residential and industrial purposes such as heating, electricity generation and manufacturing. Although Turkey has its own resources to meet the demand for natural gas, most of the needed natural gas amount is imported from other countries. The produced or imported natural gas has been used for heating, electricity generation and manufacturing in Turkey. Therefore approximate forecasting with a less statistical error of vital energy sources such as natural gas is extremely significant for countries having an imbalance between export and import values. The main purpose of this study is to determine the best coefficients with less statistical error for the forecasting equation for natural gas demand of Turkey. This paper suggests an Artificial Bee Colony (ABC) algorithm for forecasting natural gas consumption in billion cubic meters (bcm) for the years between 2018 and 2030 by using some historical indicators between 1998 and 2017. ABC algorithm is firstly used for determining coefficients of a forecasting equation for natural gas demand of Turkey. ABC algorithm was originally designed for continuous optimization problems and with its simple structure and applicability can be useful for decision or policymakers. Therefore such a strong metaheuristic algorithm in the literature should be used for a vital problem of the countries having current account deficit like Turkey. Annual gross domestic product at chained volume index, population, export and import of Turkey are selected as historical indicators and independent variables for forecasting in this study. Furthermore, a linear regression model is also created to compare the proposed forecasting model. Both models are used to forecast Turkey future natural gas consumption for two different scenarios. The proposed algorithm outperforms the linear regression model in view of total absolute relative errors from previous natural gas consumptions of Turkey.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Artificial bee colony algorithm to forecast natural gas consumption of Turkey

Arık

2019

SN Appl. Sci.

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“…The remainder of this paper is organized as follows: Section 2 introduces the traditional GM(1,1) model, as well as the theoretical concept and modeling procedure of EP-GM (1,1). Section 3 examines the forecasting performance and describes a comparison of EP-GM (1,1) through the application of one real case. Finally, Section 4 discusses the outcomes and presents conclusions.…”

mentioning

confidence: 99%

Extrapolation-Based Grey Model for Small-Data-Set Forecasting

Che-jung

Li²,

Chen³

et al. 2019

ECECSR

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Product life cycles have become increasingly shorter owing to the rise of global competition in recent decades. Competitive tension is especially high in electronics-related industries. It is usually difficult for most enterprises to collect sufficient quantities of samples with which to obtain useful information when making decisions in such a highly competitive environment. Grey system theory plays a vital role in addressing the issue of insufficient sample quantities. The traditional GM(1,1) model is well known for its ability to generate useful forecasts with a small quantity of samples; however, the newest datum is always weakened to alleviate the randomness of data in the traditional GM(1,1) model, causing it to output higher prediction errors. To overcome such imperfections, this study proposes a modified grey forecasting model named EP-GM(1,1), in which a new equation for calculating the background values in the traditional GM(1,1) model is developed based on linear extrapolation to emphasize the importance of the newest datum. To evaluate the forecasting ability of EP-GM(1,1), the monthly demand of thin-film-transistor liquid-crystal display panels were employed for experimentation. The results indicate that EP-GM(1,1) can engender a favorable prediction result, demonstrating that the model is a feasible tool for small-sample forecasting.

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“…Xie and Li [16] predicted natural gas consumption based on the grey model optimized using the genetic algorithm. Wang et al [17] and Boran [18] applied the rolling GM (1,1) model to predict natural gas consumption. Ma and Liu [19] developed a novel time-delayed polynomial grey prediction model (TDPGM (1,1)) and compared it with other common prediction models.…”

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 Natural Gas Consumption in Turkey Using Grey Prediction

Cited by 64 publications

References 15 publications

Artificial bee colony algorithm to forecast natural gas consumption of Turkey

Artificial bee colony algorithm to forecast natural gas consumption of Turkey

Extrapolation-Based Grey Model for Small-Data-Set Forecasting

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

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