David Patruyo
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Data analysis Operations Project management

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Demand forecasting 1 Economic development 1 Economic growth 1 Economics 1 Energy consumption 1 Energy production 1 Energy supply 1 Mathematical modeling 1 Natural gas 1 Urbanization 1

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Mac Ewan
Mac Ewan
Edmonton, Alberta, Canada

Dynamics of Energy Demand in Bangladesh

The total primary energy consumption in Bangladesh increased from 0.06 Exajoules in 1972 to 1.64 Exajoules in 2019, a huge expansion. Per capita energy use increased even more, from 0.09 Gigajoules in 1972 to 10.1 Gigajoules in 2019, as the graph shows below. More interestingly, the per capita energy use increased faster than total energy consumption and is increasing at an increasing rate. The sudden decrease in 2020 is due to the reduced economic activity resulting from the Covid 19 pandemic, which does not reflect the long-run trend. With the rapid economic growth in Bangladesh, the energy requirement is expected to keep going up. The challenge of adequate energy commensurate with the requirement of economic growth is a cause of concern for a country like Bangladesh as it is highly dependent on imported energy and is increasingly becoming more dependent on imports. Currently, nearly two-thirds of the county’s energy supply comes from domestic production of natural gas the reserve of which is declining rapidly. Almost a quarter of the energy is met by oil, which is entirely imported. The use of energy is an important indicator for social, economic, and infrastructural development as well as standard of living. The extent of energy consumption often gets related to the economic development [growth of GDP] and lifestyle of a society. Although the relationship between energy consumption and economic growth has not been without dispute, it is well accepted in the scientific community that they are interlinked. It is likely that the demand for energy will continue to increase with the economic development of Bangladesh. During the past couple of years, total energy consumption in Bangladesh increased by over 10 percent a year. Along with the increase in GDP, energy demand has increased, and the mode of energy production and consumption has changed. Mujeri et al (2014) observe in other developing countries that consumption of energy rises quickly when per capita income reaches between the US $1000 and $10000. A quick increase in energy demand in Bangladesh is expected as its per capita income is within this range. This situation is compounded by rising population density and rapid urbanization. The population density in Bangladesh is 1123 people per square km compared to 25 in the world and 36 in the USA. Bangladesh is certainly way behind the amount of energy it needs to offer a lifestyle of a middle-income country. In 2019-20, nearly 92.2 percent of the total population could access electricity. However, the analysis shows that the forecasted and actual energy demand is consistent with little gaps in years from 2005 to 2014. The purpose of this project is to: Forecast the consumption of energy [energy demand] up to the end of the next decade 2035. There are different methods of energy demand forecasting. Our objective is to: Develop a mathematical model of the annual energy consumption of Bangladesh since its independence in 1971 [data available from 1972]. Develop a mathematical model based on data on column C [Energy consumption] and predict the energy consumption of the country up to 2035. Develop a mathematical model for energy consumption [Column C] based on itself and all other variables provided and then predict the energy consumption up to 2035. Our objective is to develop two models as described above. The first one is a univariate model with the assumption that energy consumption is independent and depends on past consumption only. The second model assumes that energy consumption depends on many variables [use all the variables provided – first use GDP and population and then add all the rest of the variables]. Of course, predict the energy consumption of the country up to 2035 based on this model. Compare and contrast the model and its prediction. Prepare a report describing the entire methods and procedures.

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