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Forecasting Natural Gas: a Literature Survey International Journal of Energy Economics and Policy ISSN: 2146-4553 available at http: www.econjournals.com International Journal of Energy Economics and Policy, 2018, 8(3), 216-249. Forecasting Natural Gas: A Literature Survey Jean Gaston Tamba1*, Salomé Ndjakomo Essiane2, Emmanuel Flavian Sapnken3, Francis Djanna Koffi4, Jean Luc Nsouandélé5, Bozidar Soldo6, Donatien Njomo7 1Department of Thermal and Energy Engineering, University Institute of Technology, University of Douala, PO Box 8698 Douala, Cameroon, 2Laboratory of Technologies and Applied Science, University Institute of Technology, University of Douala, PO Box 8698 Douala, Cameroon, 3Laboratory of Technologies and Applied Science, University Institute of Technology, University of Douala, PO Box 8698 Douala, Cameroon, 4Department of Thermal and Energy Engineering, University Institute of Technology, University of Douala, PO Box 8698 Douala, Cameroon, 5Department of Renewable Energy, Higher Institute of the Sahel, University of Maroua, PO Box 46, Maroua, Cameroon, 6HEP-Plin Ltd., Cara Hadrijana 7, HR-31000 Osijek, Croatia, 7Environmental Energy Technologies Laboratory, University of Yaoundé I, PO Box 812, Yaoundé, Cameroon. *Email: [email protected] ABSTRACT This work presents a state-of-the-art survey of published papers that forecast natural gas production, consumption or demand, prices and income elasticity, market volatility and hike in prices. New models and techniques that have recently been applied in the field of natural gas forecasting have discussed with highlights on various methodologies, their specifics, data type, data size, data source, results and conclusions. Moreover, we undertook the difficult task of classifying existing models that have been applied in this field by giving their performance for instance. Our objective is to provide a synthesis of published papers in the field of natural gas forecasting, insights on modeling issues to achieve usable results, and the future research directions. This work will help future researchers in the area of forecasting no matter the methodological approach and nature of energy source used. Keywords: Forecasting Natural Gas, Existing Forecasting Models, Models categorization JEL Classifications: C53, Q4, Q47 1. INTRODUCTION (Xiong et al., 2014; Dilaver et al., 2014). Around one-fifth of the globe’s primary energy is met from natural gas which is considered Energy in general is vital for sustainable development of any as the cleanest-burning fossil fuel (Selehnia et al., 2013; BP, 2009; nation: Be it social, economic or environment. Consumption of Imam et al., 2004; Xu and Wang, 2010; Agbonifo, 2016; Solarin clean energies like natural gas is also an important criterion to and Ozturk, 2016). evaluate the performance of the economy of any nation, for it is a crucial factor of production in all aspects of every economy. Aside from natural gas demand, the question of its availability Interest in forecasting natural gas has led to a tremendous surge quickly arises to know whether gas resources are large enough of research activities in the last decade (Soldo, 2012; Aydin, to support projected plans. For this, it is of crucial importance 2014; Voudouris et al., 2014; Fagiani et al., 2015; Khan, 2015; to be able to predict natural gas consumption with an acceptable Suganthi and Samuel, 2012). World energy demand has increased degree of accuracy in order to improve operational efficiency, save sharply as primary energy sources are required for sustainable energy, reduce costs at different levels, manage supply contracts, development (Fan and Xia, 2012; Panella et al., 2012; Gracias indigenous production and infrastructures planning. Failing one of et al., 2012; Aydin, 2015; Azadeh et al., 2015; Ozturk and these goals would certainly cause instabilities in a nation’s energy Al-Mulali, 2015; Szoplik, 2015; Rafindadi and Ozturk, 2015; system (Bianco et al., 2014). Zaman, 2016; Karacaer-Ulusoy and Kapusuzoglu, 2017). Energy is linked to industrial production, agricultural output, health, access Over the years, energy demand, particularly natural gas demand to water, population, education, quality of life, cooking, transport has attracted interest as much significance is attached to the issue 216 International Journal of Energy Economics and Policy | Vol 8 • Issue 3 • 2018 Tamba, et al.: Forecasting Natural Gas: A Literature Survey (Erdogdu, 2010; Gorucu, 2004; Gorucu and Gumrah, 2004). was probably the first to investigate on fossil fuels. With his Many studies have forecasted natural gas production, consumption model, he pointed out that the production curve of any fossil fuel or demand, prices and income elasticity, market volatility and will rise, pass through one or several maxima, before declining hike in prices in several different areas, on world level, regional asymptotically to zero. Later, Hubbert presented the mathematical level, national level, city level, industrial and residential sectors, foundation for his model where a logistic curve was used to fit and individual costumer level (Hubbert, 1949; 1956; Al-Jarri cumulative production versus time. His model later became a and Startzman, 1997; Sanchez-Ubeda and and Berzosa, 2007; reference in the field of forecasting with ups and downs. In 1950, Behrouznia et al., 2010; Tonkovic et al. (2009); Azadeh et al., Verhulst (1950), considering price and income of natural gas for 2010). These studies used various data which could be classified 46 firms in the French industry followed the move by constructing into three main categories: Meteorological data, historical data a set of equations: Demand equation, equation of production and and economic data. The first experimental study on natural gas equilibrium of production. It was not until 1966 that Balestra and demand prediction was conducted in 1966 by Balestra and Nerlove Nerlove (1966) made use of econometrics parameters by applying (1966) while the first theoretical study was that of Hubbert (1949). an ordinary least square (OLS) model. In 1973 Tinic et al. (1973) forecasted natural gas demand as a tool for evaluating rural Just as forecasting natural gas consumption and its availability is gasification. In 1977 Berndt and Watkins (1977), based on the important, forecasting natural gas production, price and income works of Balestra and Nerlove (1966), studied natural gas demand elasticity, hike in prices and market volatility are also inescapable in the residential and commercial sectors in British Columbia and in decision making at all levels of the economy. In energy Ontario. In 1981, Beierlein et al. (1981) estimated the demand sector, where decision environment is characterized by risks and for natural gas and electricity for the residential, commercial and uncertainty, decision makers need some information about possible industrial sectors in Northeastern USA. High resolution forecasting future outcomes Dalfard et al. (2013a). The oil shocks of the 1970s (very short term horizon forecasting) began in 1983 when Piggott are a clear example. Thus, developing models for accurate natural (1983) forecasted daily and weekly natural gas demand. In the gas price forecasting is crucial because these forecasts are relevant same year, Jager (1983) provided a discussion of the wintertime in determining a whole range of regulatory decisions covering decrease in urban energy supply and demand resulting from a both supply and demand of natural gas or for market participants warmer climate whereas regression analysis was used in 1987 by Selehnia et al. (2013). Herbert et al. (1987) to evaluate monthly industrial natural gas demand in the USA. Still in 1987, Herbert (1987) went further An overview of various forecasting approaches from 1949 to 2010 to analyze monthly sales of natural gas to residential customers in the field of natural gas consumption was published in Soldo in the USA. In 1988 Werbos (1988) reported the use of artificial (2012). At his time, Soldo (2012) presented a review of natural neural networks (ANNs) with generalization of back propagation gas consumption and production forecasts from 1949 to the end of (BP) to recurrent gas market model. 2010. He provided analysis and synthesis of published researches with insights on applied area, forecasting horizons, data and tools. In 1991, Liu and Lin (1991) explored the dynamic relationships This paper is a revisited form of the works of Soldo (2012). In the among several potentially relevant time series variables and present work, we include papers that also deal with price and income developed appropriate models for forecasting natural gas elasticity, market volatility and hike in prices of natural gas, and we consumption in Taiwan. In 1994, Lee and Singh (1994) used extend his works up till 2015. We equally include data type, data size, micro consumption data to analyze patterns in residential gas and data source, results of researches and corresponding conclusions. In electricity demand while Brown et al. (1994) developed feed- particular, we compare various models whenever it is possible, giving forward network models to predict daily gas consumption. In 1995, their performance measure, their advantages and shortcomings. Li and Sailor (1995) presented an overview of a methodology for developing regional assessments of the climate’s role in The objective of this paper is to provide a synthesis of published determining monthly energy consumption in the residential and papers in the field of natural gas, insights on modeling issues to commercial sectors.
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