Forecasting Energy Consumption of Turkey by Arima Model

Öztürk, Suat; Öztürk, Feride

doi:10.18488/journal.2.2018.82.52.60

Cited by 52 publications

(39 citation statements)

References 18 publications

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“…Turkey"s energy demand expected to increase due to the growing economy and the demographic impact of a young population. Supporting these facts, Ozturk and Ozturk (2018) forecast that in the next 25 years, total coal consumption will increase at an annual average rate of 4.87%.…”

Section: Introductionmentioning

confidence: 96%

Exploring the Nexus of Coal Consumption, Economic Growth, Energy Prices and Technological Innovation in Turkey

Öztürk

2018

Asian Economic and Financial Review

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This study mainly attempts to cast light into the dynamic relationships between coal consumption, economic growth, energy price and technological innovation in Turkey over the period of 1980-2015. Based on the results drawing from an autoregressive distributed lag (ARDL) model, coal consumption, economic growth, energy price and technological innovation are cointegrated. Specifically, the empirical results indicate that economic growth positively affects coal consumption, whereas technological innovation negatively affects it over a long-run. Regarding short-run dynamic relationships, economic growth and technological innovation have a positive impact on coal consumption. The results from the autoregressive integrated moving average (ARIMA) model suggest that an annual average growth rate of coal consumption will be 2.02% between 2016 and 2025. Regarding policy implications, the results of this study suggest that policy makers should allocate more resources to research and development on energy technologies to improve energy efficiency in Turkey. Contribution/ Originality: This study is one of the few studies which have investigated coal consumptiongrowth nexus in a multivariate setting. Considering that technological innovation has not been incorporated by previous coal consumption-growth studies, this study contributes to the existing literature by incorporating technological innovation in the analysis. next three decades. The largest coal producing countries are China, the USA, India, Indonesia, Australia and South Africa (World Energy Council, 2016). Among other countries, Turkey is evaluated as being at medium levels in terms of the reserves and production amounts of coal and lignite in the world. Nearly, 3.2% (1.3 billion tons of coal and 17.3 billion tons of lignite) of the total world reserves of coal and lignite are in Turkey (Republic of Turkey Ministry of Energy and Natural Resources, 2018). European Association for Coal and Lignite reports that in 2015,

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Section: Introductionmentioning

confidence: 96%

Exploring the Nexus of Coal Consumption, Economic Growth, Energy Prices and Technological Innovation in Turkey

Öztürk

2018

Asian Economic and Financial Review

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“…There are many studies dealing with forecasts of energy consumption from renewables based on various quantitative methods and on the data of the EU-28 countries [43][44][45][46][47][48][49][50] Considering the European Commission principal objective to extend the share of renewable energy production in electricity, the aim of this research is to explain the renewable energy in electricity based on GDP per capita, seen as a measure of standard of living and income inside the EU, in the period of 2007-2017. Knowing the advantages of RES related to environmental protection and reduction of GHG emissions, this paper checks whether the increase in renewable energy in electricity has a positive effect on the EU countries' economies.…”

Section: Introductionmentioning

confidence: 99%

Renewable Energy in the Electricity Sector and GDP per Capita in the European Union

et al. 2019

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Considering that the European Directive has imposed that at least 20% of the total energy should come from renewable energy sources (RES) by 2020 already and the specific targets for each European Union Member State, this paper attempts to assess the importance of GDP per capita in realizing these targets and also the effects of the RES share in electricity. Contrary to previous research, this paper does not consider the connection between economic growth and RES, but rather the potential connection between the share of RES in electricity and the real GDP per capita. The panel data models indicated to a positive, but very low impact of GDP per capita on the share of RES in electricity in the period of 2007–2017 in the case of the EU countries, except Luxembourg that has outlier values of GDP per capita. However, causality between the two variables was not identified. Some groups of countries were described according to these variables using cluster analysis. Future research should focus on the extension of this model by including other important variables such as RES potential available in the countries with specific geographical conditions.

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“…The application of wavelet transform further reduces the error of predictions performed by the model. In a similar way, other works related to the forecast of energy have been discussed in [9], where the authors described an ARIMA model for forecast coal's consumption in China, and in [10] where authors used it to forecast energy consumption of Turkey. One more example can be found in [11] where authors describe the use of exogenous variables for residential low voltage distribution networks.…”

Section: Related Workmentioning

confidence: 99%

“…Similar studies using ARIMA for price forecasting have been done in [4][5][6][7][8][9][10][11]. Other works focused on the forecasting of e-commerce prices have been proposed in [12] where authors proposed LSTM neural networks to obtain better prediction performances in daily phone prices in one particular marketplace, amazon.fr.…”

Section: Introductionmentioning

confidence: 98%

Forecasting E-Commerce Products Prices by Combining an Autoregressive Integrated Moving Average (ARIMA) Model and Google Trends Data

et al. 2018

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E-commerce is becoming more and more the main instrument for selling goods to the mass market. This led to a growing interest in algorithms and techniques able to predict products future prices, since they allow us to define smart systems able to improve the quality of life by suggesting more affordable goods and services. The joint use of time series, reputation and sentiment analysis clearly represents one important approach to this research issue. In this paper we present Price Probe, a suite of software tools developed to perform forecasting on products’ prices. Its primary aim is to predict the future price trend of products generating a customized forecast through the exploitation of autoregressive integrated moving average (ARIMA) model. We experimented the effectiveness of the proposed approach on one of the biggest E-commerce infrastructure in the world: Amazon. We used specific APIs and dedicated crawlers to extract and collect information about products and their related prices over time and, moreover, we extracted information from social media and Google Trends that we used as exogenous features for the ARIMA model. We fine-estimated ARIMA’s parameters and tried the different combinations of the exogenous features and noticed through experimental analysis that the presence of Google Trends information significantly improved the predictions.

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Forecasting Energy Consumption of Turkey by Arima Model

Cited by 52 publications

References 18 publications

Exploring the Nexus of Coal Consumption, Economic Growth, Energy Prices and Technological Innovation in Turkey

Exploring the Nexus of Coal Consumption, Economic Growth, Energy Prices and Technological Innovation in Turkey

Renewable Energy in the Electricity Sector and GDP per Capita in the European Union

Forecasting E-Commerce Products Prices by Combining an Autoregressive Integrated Moving Average (ARIMA) Model and Google Trends Data

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