Forecasting production of fossil fuel sources in Turkey using a comparative regression and ARIMA model

Ediger, Volkan Ş.; Akar, Sertaç; Uğurlu, Berkin

doi:10.1016/j.enpol.2005.08.023

Cited by 84 publications

(51 citation statements)

References 24 publications

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“…The historical publication of Box and Jenkins [24] and the later sequels of the same publication provides the stepwise procedure for the ARMA analysis [8,69]. The step followed in this are same as of followed in various studies.…”

Section: Autoregressive Integrated Moving Average (Arima) and Holt-wimentioning

confidence: 99%

“…In the final step, forecasting was carried out based on the developed and checked ARIMA model [7]. The Minitab tool (version 14) was used in this study, without seasonal variation, to forecast the time series [69] for the next 21 observations, i.e., up to 2035.…”

Section: Autoregressive Integrated Moving Average (Arima) and Holt-wimentioning

confidence: 99%

“…The abbreviation ARIMA has been derived from the two models involved in the methodology, i.e., Autoregressive model (AR) and the Moving Average model (MA) [12,69]. ARMA methodology has been used in this study to model the stationary time series with an appropriate number of p and q lags [2,11].…”

Section: Autoregressive Integrated Moving Average (Arima) and Holt-wimentioning

confidence: 99%

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An Integrated Modeling Approach for Forecasting Long-Term Energy Demand in Pakistan

et al. 2017

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Abstract:Energy planning and policy development require an in-depth assessment of energy resources and long-term demand forecast estimates. Pakistan, unfortunately, lacks reliable data on its energy resources as well do not have dependable long-term energy demand forecasts. As a result, the policy makers could not come up with an effective energy policy in the history of the country. Energy demand forecast has attained greatest ever attention in the perspective of growing population and diminishing fossil fuel resources. In this study, Pakistan's energy demand forecast for electricity, natural gas, oil, coal and LPG across all the sectors of the economy have been undertaken. Three different energy demand forecasting methodologies, i.e., Autoregressive Integrated Moving Average (ARIMA), Holt-Winter and Long-range Energy Alternate Planning (LEAP) model were used. The demand forecast estimates of each of these methods were compared using annual energy demand data. The results of this study suggest that ARIMA is more appropriate for energy demand forecasting for Pakistan compared to Holt-Winter model and LEAP model. It is estimated that industrial sector's demand shall be highest in the year 2035 followed by transport and domestic sectors. The results further suggest that energy fuel mix will change considerably, such that oil will be the most highly consumed energy form (38.16%) followed by natural gas (36.57%), electricity (16.22%), coal (7.52%) and LPG (1.52%) in 2035. In view of higher demand forecast of fossil fuels consumption, this study recommends that government should take the initiative for harnessing renewable energy resources for meeting future energy demand to not only avert huge import bill but also achieving energy security and sustainability in the long run.

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Section: Autoregressive Integrated Moving Average (Arima) and Holt-wimentioning

confidence: 99%

Section: Autoregressive Integrated Moving Average (Arima) and Holt-wimentioning

confidence: 99%

Section: Autoregressive Integrated Moving Average (Arima) and Holt-wimentioning

confidence: 99%

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An Integrated Modeling Approach for Forecasting Long-Term Energy Demand in Pakistan

et al. 2017

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“…Ediger et al used autoregressive moving average (ARIMA), seasonal ARIMA (SARIMA) and comparative regression techniques to forecast the production of fossil fuel sources in Turkey, which include natural gas [33]. They made annual forecasts from 2004 to 2038 and used different regression types such as linear, logarithmic, inverse, quadratic, cubic, compound, power, growth, exponential, and logistic.…”

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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“…Autoregressive Integrated Moving Average (ARIMA) Model is a notable model in time series data prediction [5] and is widely used in econometrics study. It fits the linear characteristics of non-stationary time series to some extent.…”

Section: Introductionmentioning

confidence: 99%

Predictive Analytics on CSI 300 Index Based on ARIMA and RBF-ANN Combined Model

Yang¹,

Cheng²

2015

JMF

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The time series of share prices is a highly noised, non-stationary chaotic system which possesses both linear and non-linear characteristics. The alternative of either linear or non-linear prediction models is of its inherent limitation. The paper establishes an ARIMA and RBF-ANN combined model and makes a short-term prediction on the time series of CSI 300 index by choosing various typical input variables. Results show that the combined model with multiple input indicators, compared with single ARIMA model, single RBF-ANN model, or models with single input variable, is of higher precision.

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Forecasting production of fossil fuel sources in Turkey using a comparative regression and ARIMA model

Cited by 84 publications

References 24 publications

An Integrated Modeling Approach for Forecasting Long-Term Energy Demand in Pakistan

An Integrated Modeling Approach for Forecasting Long-Term Energy Demand in Pakistan

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

Predictive Analytics on CSI 300 Index Based on ARIMA and RBF-ANN Combined Model

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