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             \author[1]{Sunil Kumar  Dhal}

             \affil[1]{  Sri Sri University,}

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\date{\small \em Received: 13 December 2017 Accepted: 1 January 2018 Published: 15 January 2018}

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\begin{abstract}
        


Natural Gas is considered to be one of the leading energy sources for India which provide pollution free, flexible to move to our industry. Due to limited Natural Gas resource, it is a challenge to conserve, utilize our resource in an optimal way. For this a number of authors are tried to predict the natural gas consumption in short and long term basis using mathematical and computational Techniques. The objective of he paper is to meta-analysis the papers published related to Natural Gas consumption for the year 2002-2017. This research helps to find out a better and accurate prediction techniques in short and long term basis for prediction of Natural Gas Consumption(NGC).

\end{abstract}


\keywords{NGC.}

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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
atural gas(NG) is an important energy resources that is becoming more and more popular because of its environmental benefits(lower impact on environmental pollution). All most all developed countries are concerned about the natural gas consumption due to low reserve. Omer Fahrettin Dem, IREL, Selim Zaim, 2011 predicted that china's natural gas consumption will continue to grow and expected to achieve 354.1bcm by 2020. Michael Ratner(India), 2017 on his research article, he found that the natural gas portion in india's energy mix is 7\% as it remains small compared to that of the US and other developed countries like Brazil, China. India's target it to double the proportion of natural gas consumption by 2022. To achieve this goal we would require major upstream, midstream and downstream investments as well as the continued political will to take necessary steps and to decrease reliance on coal and oil. Therefore, demand for this source of energy has in creased considerably in recent years. The largest increase in world's primary energy consumption is attributed to N.G as per U.S Energy Information Administration 2016.\par
It is projected that the Natural Gas consumption as primary energy source will increase to 2040 TCF compared to the recorded consumption 120 TCF in the year 2012-2013. As per British Petroleum(BP) global 2015 NG contributes 23.8\% of the primary energy consumption globally and remains as the main fuel in production of electricity and as a fuel for the industry. monthly, quarterly and yearly basis. It has reviewed that the computational models were suitable for natural gas consumption for a better input parameters. The model efficiency not only depends upon the algorithm but majorly depends upon the input parameters. Every natural gas distributor is obliged to make a nomination of natural gas by its supplier, which is the amount of gas needed for the future days. There is a certain regulated tolerance that is allowed. In case the actual consumption exceeds the nominated amount, the distributor must pay a certain penalty. On the other hand, if nominated amount exceed actual consumption, different type of penalty will be charged as well. Since the incorrect nominations lead to high costs, accurate predictions of natural gas consumption for the following day are very important due to financial reasons. 
\section[{II.}]{II.} 
\section[{Methodology}]{Methodology}\par
For the research purposes, literature overview analysis was conducted using PRQUEST database. The keywords "natural gas consumption", " "prediction of Natural Gas" OR "demand of Natural Gas", "Consumption of Natural Gas", "Prediction models in Natural Gas)", were used for searching articles. The articles were searched within three indexes: Science Citation Index Expanded (SCI-Expanded), Social Science Citation Index (SCI), and Arts and Humanities Citation Index (A\&HCI) for the period of 2002 to 2017.\par
This search resulted with 276 papers, including article (201), proceedings paper (28) and review (47). After reviewing the title, abstracts and keywords of all found articles, articles that are not related to models for prediction of CNG for residential or commercial use were eliminated. Thereafter, 72 articles remain that met posted criteria. Those papers were analysed according to several criteria: methods used for predictions of CNG, input variables used for modelling, prediction area and prediction horizon.\par
Similar literature review was conducted by Soldo (2012), who analysed natural gas consumption from the year 1949 to 2010 and Dario Sebalj, Josip Mesarie, Davor Dujak , 2017, he predicted the Natural Gas consumption from the year 2002-2017 using web of science core collection (WOSCC) database. Year 2018 J As it can be seen in Table \hyperref[tab_0]{1}, in the last three years 34 papers considering natural gas prediction were A number of researchers attempted to develop models for the prediction of NGC on daily, weekly, published, which is more than 55\% of all analysed papers.    {\ref 2016}),who fore casted NG consumption using time series methods.\par
There are four papers in which weekly prediction of NG consumption was reported. Those are papers writ in by potocnik at al. (  {\ref 2007}), who proposed a forecasting model in order to fore cast risk estimation, and  {\ref Kaynar et al. (2011)}, who used neural network and neuro fuzzy system for prediction of NG consumption on weekly basis. Dejan Ivezi? (  {\ref 2006}) predicts natural gas consumption on weekly basis by using ANN model in Belgrade, Serbia. Ma?gorzata Trojanowska (2014) also predict on weekly basis by using Regression model in Poland.\par
There are many number of authors predicted NG consumption on daily level.\par
Gil \& Deferrari (  {\ref 2004}) proposed a daily prediction model in Argentina, Steven R. Vitullo, Ronald H. Brown; George F. Corliss, Brian M. Marx.(  {\ref 2009} Tonkovic at al.), who created a prediction model of NG consumption by using neural networks on a regional level on hourly scale for predicting NG consumption, Sabo etal. (  {\ref 2011}), who proposed mathematical models of natural gas consumption, and Szoplik (2015), who forecasted NG consumption in Poland using neural network models. Krzysztof N?cka, Ma?gorzata \& Trojanowska who created a prediction model by using Regression model on a particular area on hourly basis to predict Natural Gas consumption. 
\section[{IV. Overview of Prediction Methods}]{IV. Overview of Prediction Methods}\par
There are only seven papers that predicted natural gas consumption on regional level.  {\ref Gil \& Deferrari (2004)} presented there sults for the case of Greater Buenos Aires region in Argentina. DejanIvezi?. (  {\ref 2006}) investigated the prediction of NG consumption in the region of Belgrade, Serbia to predict the Natural Gas consumption using Parameters of ANN are obtained from the historical data using a Levenberg-Marquardt training algorithm.\par
Nil Aras (  {\ref 2008}), Beyzanur Cayir Ervural Omer Faruk Beyca Selim Zaim(2016) used genetic algorithm to predict NGC of Turkey city Eskisehir. Istanbul, Omer Fahrettin DEM ? IREL, Selim ZAIM, proposed neural networks and multivariate time series models to predict Natural Gas consumption for the city of Istanbul. Ahmet Goncu Mehmet, Oguz Karahan \& Tolga Umut Kuzuba¸ (2013) propose a methodology which combines natural gas demand estimation with a stochastic temperature model. The model demand and temperature processes separately and derive the distribution of natural gas consumption with a conditional temperature. Hossein Iranmanesh, Majid Abdollahzade \& Arash Miranian (2011) predict natural gas consumption using PSO   {\ref (2017)}. This model hybrid computational intelligence model was tested for its robustness by prediction of day ahead natural gas demands.\par
Another hybrid model consisting of ANFIS and computer simulation was proposed by  {\ref Azadeh et al. (2015)} have presented ANFIS based techniques. The ANFIS is was also used by  {\ref Kaynar et al. (2011)} to predict weekly NG consumption in turkey.\par
Ma \& Li (2010) predicted NG consumption based on the Grey system model. The same approach was using Boran (  {\ref 2015}  
\section[{Conclusion}]{Conclusion}\par
The study was conducted with seventy two number of research articles of Natural Gas prediction for various Countries in different mathematical and scientific methods. It was found that China and other developed countries are focusing this type of study for weekly, monthly and yearly basis. ANN is the most appropriate techniques for the prediction of Natural Gas consumption. The different researchers applied genetic algorithm, feed forward, Back propagation \& PSO methods for this prediction. All most all researchers are agreed upon the other popular methods are neuro-fuzzy inference system, genetic algorithms, time series methods,, support vector machines/ regression, Grey system models, mathematical and statistical models orhybrid models based on several methods. Some researches use two or more methods in the same paper. But analysis has shown that for modeling, authors often use past NG consumption data and weather data (mostly temp.)as input variables. Other variables include month, days of the week, wind speed, temperature, humidity \& price number of natural gas subscribers, GDP, inflation rate etc. Speaking of prediction are as, it can be seen that most of the papers deal with the predictions on country level. Predictions can be made as well as on regional, city, or even house level.  \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.0437995469988675\textwidth}P{0.0380237825594564\textwidth}P{0.01058890147225368\textwidth}P{0.17134767836919593\textwidth}P{0.5862400906002265\textwidth}}
\tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{A Meta Analysis of Natural Gas Consumption}\\
\tabcellsep \multicolumn{2}{l}{Year Publication No.}\tabcellsep \%\tabcellsep Authors\\
\tabcellsep 2004\tabcellsep 1\tabcellsep \multicolumn{2}{l}{1.38\% Gil \& Deferrari.}\\
\tabcellsep 2005\tabcellsep 2\tabcellsep \multicolumn{2}{l}{2.76\% Aras; Gutierrezetal.}\\
\tabcellsep 2006\tabcellsep 2\tabcellsep \multicolumn{2}{l}{2.76\% Hillard G. Huntington, Dejan Ivezi?}\\
Year 2018\tabcellsep 2007 2008\tabcellsep 4 2\tabcellsep \multicolumn{2}{l}{5.52\% 2.76\% Brabecetal., Nil Aras. Potocnik et al, Hongjie Lu, Hongjun You , Reed P. Timmer, Peter J. Lamb; Syed Ali Naqi, Syed Jamil Hassan Kazmi, Jeong C. Seong}\\
22 ( ) Volume XVIII Issue I Version I J\tabcellsep 2009 2010 2011 2012\tabcellsep 3 7 7 7\tabcellsep 4.14\% 9.66\% 9.66\% 9.66\%\tabcellsep Tonkovic, Omer Fahrettin Dem? IREL, Selim Zaim; Steven R. Vitullo; Ronald H. Brown; George F. Corliss, Brian M. Marx. Azadeh.; Forouzanfar.; Ma\&Li; Mustafa Akkurt , Omer F. Demirel, Selim Zaim; Kaynar, Oguz Yilmaz, Isik Demirkoparan, Ferhan; F. B. Gouucu; Ebrahim Kamrani Kaynar; Saboe; Zia Wadud, Himadri S Dey, Md. Ashfanoor Kabir, Shahidul I Khan; Omer Fahrettin Dem, ? IREL, SelimZaim ; Junchen Li, Xiucheng Dong, Jianxin Shangguan, Mikael Höök; Hossein Iranmanesh, Majid Abdollahzade, Arash Miranian; Hossein Iranmanesh, Majid Abdollahzade, Arash Miranian Demirel.; Olgun, Mahbubur Rahman, Mohammad Tamim \& Lutfar Rahman; Fahim Faisal; Yi-Shian Lee, Lee-Ing Tong; Azari, Ahmad; Shariaty-Niassar, Mojtaba; Azari, Ahmad; Shariaty-Niassar, Mojtaba; Alborzi, Mahmoud\\
Global Journal of Researches in Engineering\tabcellsep 2013 2014 2015 2016\tabcellsep 4 5 11 10\tabcellsep 5.52\% 6.90\% 15.18\% 13.8\%\tabcellsep Taspinar; AhmetGoncu, Mehmet, Oguz Karahan, Tolga Umut Kuzuba; Hongjie Lu, Hongjun You; Mohsen Hajabdollahi, Mostafa Hosseinzadeh, M.M Ghanadi Arab Soldo, Nguyen Hoang Viet, Jacek Mandziuk; Mustafa Akpinar, Nejat Yumusak; Krzysztof N?cka, Ma?gorzata Trojanowska: Ma?gorzata Trojanowska Azadeh.; Boran; Izadyar; Szoplik; Wu.; Zhuetal.; Wei Zhang, Jun Yang; Halle Bakhteeyar, Abbas Maleki; Jolanta Szoplik; Junwei Miao; Junghwan Jin, Jinsoo Kim\end{longtable} \par
  {\small\itshape [Note: Akpinar \& Yumusak; Bai \& Li; Baldaccietal.; Zeng\&Li; Sergas Sergipe Gas S. A., Aracaju; Mustafa Akpinar, M. Fatih Adak, Nejat Yumusak; Beyzanur Cayir Ervural, Omer Faruk Beyca, Selim Zaim; Gaurav Bhattacharya; Hans-Holger, Rogner; Miha Kova?i?, Bo?idar ?arler, Uro? ?uperl 2017 8 11.04\% Akpinar \& Yumusak; Panapakidis \& Dagoumas; Almir; Beserra dos Santos, Erika Christina Ashton Nunes Chrisman; Xiaoyu Wang, Dongkun Luo, Jianye Liu, Wenhuan Wang, and GuixinJie; Zhenwu Zhang, Xiantao Liu; Michael Ratner; Dublin Sanjay Kumar Kar; Tim Boersma, Akos Losz, Astha Ummat Total 72 100.00\% a) Overview of prediction of various time horizon]} 
\caption{\label{tab_0}Table 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2} \par 
\begin{longtable}{P{0.01178801386825161\textwidth}P{0.16292719167904904\textwidth}P{0.02694403169886082\textwidth}P{0.040837048043585934\textwidth}P{0.5948736998514116\textwidth}P{0.007578008915304605\textwidth}P{0.0012630014858841009\textwidth}P{0.0037890044576523027\textwidth}}
Sl. No.\tabcellsep Author\tabcellsep Year\tabcellsep Region\tabcellsep \tabcellsep Remarks\tabcellsep \tabcellsep \\
1\tabcellsep S. Gil, J Deferrari\tabcellsep 2004\tabcellsep Argentina\tabcellsep \multicolumn{4}{l}{ANN is used to predict the maximum consumption in the intermediate range.}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{4}{l}{applications are classifications problems,}\\
2\tabcellsep DejanIvezi?\tabcellsep 2006\tabcellsep Belgrade\tabcellsep pattern\tabcellsep recognition\tabcellsep and\tabcellsep functions\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{approximation.}\tabcellsep \tabcellsep \\
3\tabcellsep Hongjie Lu, Hongjun You\tabcellsep 2007\tabcellsep China\tabcellsep \multicolumn{4}{l}{It results in good economic and social benefits in China.}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{4}{l}{ANN used as an alternative solution}\\
4\tabcellsep Nil ARAS\tabcellsep 2008\tabcellsep Turkey\tabcellsep \multicolumn{4}{l}{approach to forecast the future demand of}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{natural gas.}\tabcellsep \tabcellsep \\
\tabcellsep Omer Fahrettin\tabcellsep \tabcellsep \tabcellsep \multicolumn{4}{l}{China will be the number one natural gas}\\
5\tabcellsep DEM? IREL,\tabcellsep 2011\tabcellsep Istanbul\tabcellsep \multicolumn{4}{l}{consumption country in the asia pacific}\\
\tabcellsep Selim ZAIM,\tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{region by 2015.}\tabcellsep \tabcellsep \\
\tabcellsep Hossein Iranmanesh\tabcellsep \tabcellsep \tabcellsep \multicolumn{4}{l}{The optimized model (ANN) which is}\\
6\tabcellsep Majid Abdollahzade\tabcellsep 2011\tabcellsep Iran\tabcellsep \multicolumn{4}{l}{employed for prediction of annual natural}\\
\tabcellsep Arash Miranian\tabcellsep \tabcellsep \tabcellsep \multicolumn{4}{l}{gas consumption in Iran and Unites States.}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{4}{l}{The practical experimental values \& Natural}\\
7\tabcellsep Hongjie Lu Hongjun You\tabcellsep 2013\tabcellsep Canada\tabcellsep \multicolumn{4}{l}{gas consumption in China can be accurately estimated through prediction}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep models\tabcellsep \tabcellsep \tabcellsep \\
8\tabcellsep Ma?gorzata Trojanowska\tabcellsep 2014\tabcellsep Poland\tabcellsep \multicolumn{4}{l}{Predict the daily demand for natural gas by rural consumers.}\\
9\tabcellsep Nguyen Hoang Viet Jacek Mandziuk\tabcellsep 2014\tabcellsep Poland\tabcellsep \multicolumn{4}{l}{The neural network model is most efficient techniques and the result is acceptable by the natural gas company's viewpoints.}\\
10\tabcellsep Wei Zhang Jun Yang\tabcellsep 2015\tabcellsep China\tabcellsep \multicolumn{4}{l}{ANN model can be used as an effective tool to estimate natural gas consumption in different countries.}\\
11\tabcellsep Halle Bakhteeyar Abbas Maleki\tabcellsep 2015\tabcellsep Iran\tabcellsep \multicolumn{4}{l}{A trial-and-error procedure used to identify the suitable parameters for prediction of natural gas}\\
12\tabcellsep Jolanta Szoplik\tabcellsep 2015\tabcellsep Poland\tabcellsep \multicolumn{4}{l}{Focused to predict gas consumption on any day of the year and any hour of the day.}\\
13\tabcellsep Junghwan Jin Jinsoo Kim\tabcellsep 2015\tabcellsep Korea\tabcellsep \multicolumn{4}{l}{GARCH model is more suitable model than ANN techniques to forecast the detail components.}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{4}{l}{Daily the producer adjusts its production}\\
14\tabcellsep Sergas Sergipe Gas S. A., Aracaju\tabcellsep 2016\tabcellsep Brazil\tabcellsep \multicolumn{4}{l}{capacity considering the availability of transportation pipelines, gas pipelines and}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{demands from consumers.}\tabcellsep \tabcellsep \\
\tabcellsep Mustafa Akpinar\tabcellsep \tabcellsep \tabcellsep \multicolumn{4}{l}{The ANN model with two hidden layer gives}\\
15\tabcellsep M. Fatih Adak\tabcellsep 2016\tabcellsep Ukraine\tabcellsep \multicolumn{4}{l}{better results in demand forecasting than}\\
\tabcellsep Nejat Yumusak\tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{the other model.}\tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_1}Table 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2} \par 
\begin{longtable}{P{0.175\textwidth}P{0.049999999999999996\textwidth}P{0.175\textwidth}P{0.45\textwidth}}
numbers\tabcellsep of\tabcellsep authors\tabcellsep were predicting NG\end{longtable} \par
 
\caption{\label{tab_2}Table 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.2955197132616487\textwidth}P{0.5236758263639985\textwidth}P{0.030804460374352846\textwidth}}
\multicolumn{2}{l}{A Meta Analysis of Natural Gas Consumption}\tabcellsep \\
Yumusak (2016) predict NG consumption using\tabcellsep generate algorithm, ANFIS and feed forward neural\tabcellsep \\
technique hybrid neural networks in Ukraine. Beyzanur\tabcellsep network was proposed by Panapakidis \& Dagoumas\tabcellsep \\
Cayir Ervural, Omer Faruk Beyca \& Selim Zaim (2016)\tabcellsep Natural gas consumption is predicted by using\tabcellsep \\
predict natural gas consumption using methods Genetic\tabcellsep various predicting techniques and methods or even a\tabcellsep \\
algorithm in Turkey. Xiaoyu Wang, DongkunLuo, Jianye\tabcellsep combination of several methods. Soldo (2012)\tabcellsep \\
Liu, Wenhuan Wang, and Guixin Jie (2017) predict the\tabcellsep discovered that among the first tools for prediction of\tabcellsep \\
NG consumption using methods hybrid MVO-NNGBM\tabcellsep CNG was the Hubbert curve model usedin1950s. Since\tabcellsep \\
model in China. Zhenwu Zhang \& Xiantao Liu (2017)\tabcellsep 1960s, when statistical models were developed, various\tabcellsep \\
predict the natural gas consumption using the method\tabcellsep statistical models have been used for predictions of NG\tabcellsep \\
PSO \& Gray neural network in China. Sanjay Kumar\tabcellsep consumption. From the late 1970s and1980s,the\tabcellsep \\
Kar\& Michael Ratner (2017) predict the natural gas\tabcellsep artificial neural networks became very popular fore\tabcellsep \\
consumption using techniques ANN in India.\tabcellsep casting tool. Lately, there are new methods used in\tabcellsep \\
Techniques used for prediction of NG in this paper are Neural Network and Adaptive neural network(NNANN) based, Fuzzy Inference System (ANFIS). An interesting model which is a combination of\tabcellsep predictions of NG consumption such as Grey models or genetic algorithms. Optimized least squares in Iran. Hongjie Lu \& Hongjun You (2013) predicts NG consumption using the methods Back propagation \& Gray model in Canada. Ma?gorzata Trojanowska (2014) predict natural gas consumption using the methods Regression model in Poland. Nguyen Hoang Viet \& Jacek Mandziuk (2014) predict the NG consumption using methods Neural \& Fuzzy Neural network in Poland. Wei Zhang \&Jun Yang (2015) predict the consumption by using the techniques Bayesian model , Averaging model \& linear regression in China. Halle akhteeyar \& Abbas Maleki (2015) using PSO model to predict the NG consumption in Iran. Jolanta Szoplik (2015) \& Junghw an Jin Jinsoo Kim(2015) predict the NG consumption using ANN in Poland \& Korea. Sergas Sergipe Gas S. A., Aracaju (2016) predict the natural gas consumption using Arima model in Brazil. Mustafa Akpinar, M. Fatih Adak \& Nejat\tabcellsep Global Journal of Researches in Engineering ( ) Volume XVIII Issue I Version I 25 Year 2018\\
different techniques such as Wavelet transform,\tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_3}}\end{figure}
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