The Application of Trend Analysis for Coal Demand Modeling

By Gökhan Aydın. 2015

115 citations recorded by the source corpus.

In the current modeling, trend analysis is used to model coal consumption (CC) and forecast the future coal demand. For modeling, trends in the CC of the world and the major coal consuming countries during 1965–2010 are identified. According to the trend observed, the appropriate regression analysis is employed to catch the best model concerning the relations between the CC and the years. The developed models are tested by t-distribution, F-distribution and residual analysis. Statistically significant trends are determined for the United States, India, Japan, South Africa, South Korea, Australia, Taiwan, Turkey, Thailand, and the world. The results show that the proposed models can be used for the forecasting of future coal demand. It is also concluded that differences between forecasted CC by proposed models and forecasted CC by British Petroleum and International Energy Agency are also in the acceptable range.

Subfields: Economics and Econometrics, Electrical and Electronic Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/b6b960015fb3a6a22c0fa4aa8e71e9377054a8dd

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In Economics and Econometrics

1. Hyongdoo Jang
2. Erkan Topal
3. Gokhan Aydin
4. Gökhan Aydın

In Electrical and Electronic Engineering

1. Hyongdoo Jang
2. Erkan Topal
3. Gokhan Aydin
4. Gökhan Aydın

In Management Science and Operations Research

1. Hyongdoo Jang
2. Erkan Topal
3. Gokhan Aydin
4. Gökhan Aydın

Citations in the corpus

• Forecasting Natural Gas Production Using Various Regression Models (2015, background): “The main advantage of this approach is its simplicity, and forecasts are based on whatever data are available (Kone and Buke, 2010; Aydin, 2015a, 2015b).”
• Regression Models for Forecasting Global Oil Production (2015, methodology): “In other words, the forecasts are based on whatever data are available (Kone and Buke, 2010; Aydin, 2015a, 2015b).”
• Energy consumption modeling using artificial neural networks: The case of the world’s highest consumers (2016, background): “Under- and overestimation of energy consumption could lead to economic and environmental problems (Aydin, 2015a, b).”