Prediction of natural gas demand by considering implications of energy-related policies: The case of Türkiye

Abstract

ABSTRACTThe most important key to determining sustainable energy policies is reliable and accurate energy demand forecasting. Grey prediction, one of the demand forecasting methods, makes successful forecasts with limited data and without the need for any prior knowledge. In this study, GM (1,1) and Grey Verhulst from time series models and GM (1, N) model based on cause–effect relationship were used to forecast Türkiye’s natural gas demand. Dynamic grey forecasting models have been developed in order to obtain robust and reliable predictions. All developed models were evaluated according to performance criteria, and the superior model was obtained to be GM (1,5). Within the scope of the study, it was determined that models based on cause–effect relationship should be preferred instead of time series models. For this, population, the amount of electricity generated from natural gas, industrial production index, and building area are determined as independent variables. Three different scenarios were created taking into account the specified independent variables, and natural gas demand until 2025 was obtained. According to the forecasting results, Türkiye’s natural gas demand for 2025 will happen as an interval between 29.61 and 53.62 mtoe based on the low and high scenarios, respectively. According to the expected scenario, this demand would be realized around 40 mtoe for Türkiye in the year 2025. Finally, Grey Dynamic Decision Support System was designed and introduced to easily apply the dynamic grey models by end-users.KEYWORDS: Energy policiesforecastingGrey Dynamic decision support systemGrey prediction modelsNatural gas Disclosure statementNo potential conflict of interest was reported by the author(s).Ethical approvalThis article does not include any studies with human participants or animals performed by any of the authors.