Forecasting the primary energy demand in Turkey and analysis of cyclic patterns

Date

2002-03-01

Author

Ediger, VS
Tatlidil, H

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

136
views

0
downloads

The planning and estimation of future energy demand via modern statistical methods have been officially used in Turkey since 1984. However, almost all previous forecasts proved significantly higher than actual observations because of several reasons discussed here. The cycle analysis, which is a semi-statistical technique that makes use of any cyclicity in the historical data of annual additional amounts of energy demand, appears to give better results than the other techniques for forecasting energy demand in Turkey. This method suggests that the energy demand will be around 130 million toe in 2010. This figure is very close to the estimates obtained by the Winter's exponential smoothing method. To increase the scientific validity of the method, it should be applied in other similar countries.

Subject Keywords

Forecasting, Energy demand, Cyclic patterns, Turkey

URI

https://hdl.handle.net/11511/65088

Journal

ENERGY CONVERSION AND MANAGEMENT

DOI

https://doi.org/10.1016/s0196-8904(01)00033-4

Collections

Department of Geological Engineering, Article

Suggestions

OpenMETU
Core

Forecasting Turkey's sectoral energy demand Oğuz, Mustafa Efe; Ekici, Tufan; Sarı, Ramazan; Sustainable Environment and Energy Systems (2013-6) This study forecasts the sectoral energy demand of Turkey in the agriculture, industry, transportation, residence and services sectors for 2023 by means of the ARIMA, Vector Autoregressive and Decomposition statistical methods, and their products are then combined to arrived at a composite, or ensemble forecast. Each of these methods has their own merits and compliments each other. Two scenarios are considered; either the use of entire, unedited data (scenario one), or the absence of the last 3 years of the...
Forecasting Turkish real GDP growth in a data-rich environment Sen Dogan, Bahar; Midilic, Murat (2019-01-01) This study generates nowcasts and forecasts for the growth rate of the gross domestic product in Turkey using 204 daily financial series with mixed data sampling (MIDAS) framework. The daily financial series include commodity prices, equity indices, exchange rates, and global and domestic corporate risk series. Forecasting exercises are also carried out with the daily factors extracted from separate financial data classes and from the whole dataset. The findings of the study suggest that MIDAS regression mo...
Predicting intensity and damage distribution during the 1995 Dinar, Turkey, earthquake with generated strong motion accelerograms Sucuoğlu, Haluk; Zeng, YH (2003-06-01) A source model for the 1995 Dinar earthquake that is capable of reproducing reasonably well the statistical characteristics of observed strong motion records is used to generate synthetic ground-motion accelerograms at 29 different sites along five east-west cross sections in the city. Each section crosses a distribution of topographical, geotechnical, and damage characteristics. All synthetic stations are located within a circle of approximately 4-km diameter, and a segment of the ruptured fault extends in...
Forecasting production of fossil fuel sources in Turkey using a comparative regression and ARIMA model Ediger, Volkan S.; Akar, Sertac; Ugurlu, Berkin (Elsevier BV, 2006-12-01) This study aims at forecasting the most possible curve for domestic fossil fuel production of Turkey to help policy makers to develop policy implications for rapidly growing dependency problem on imported fossil fuels. The fossil fuel dependency problem is international in scope and context and Turkey is a typical example for emerging energy markets of the developing world. We developed a decision support system for forecasting fossil fuel production by applying a regression, ARIMA and SARIMA method to the ...
Artificial Neural Network Models for Forecasting Tourist Arrivals to Barcelona Alptekin, Bülent; ALADAĞ, ÇAĞDAŞ HAKAN (2016-09-09) In order to reach accurate tourism demand forecasts, various forecasting methods have been proposed in the literature [1]. These approaches can be divided into two subclasses. One of them is conventional methods such as autoregressive moving average (ARIMA) or exponential smoothing. And, the other one is advanced forecasting techniques such as fuzzy time series, artificial neural networks (ANN) or hybrid approaches. The main purpose of this study is to develop some efficient forecasting models based on ANN ...

Citation Formats

V. Ediger and H. Tatlidil, “Forecasting the primary energy demand in Turkey and analysis of cyclic patterns,” ENERGY CONVERSION AND MANAGEMENT, pp. 473–487, 2002, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/65088.