ICTACT Journal on Soft Computing

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Application of Time Series Analysis in Forecasting Coal Production and Consumption in the Philippines


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1 Master of Teacher Education, University of Mindanao Digos College, Philippines
     

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Energy is the backbone of a country’s economic and technological development. For a country to be competitive in the global market and secure sustainable development, energy must be efficiently used. The Philippines’s main source of energy is coal. Historical data shows that the country’s energy and coal consumption has been continuously increasing. With this, careful energy planning is required to develop policies that ensure sufficient energy supply in the future. This paper focuses on forecasting coal production and consumption in the Philippines. For the purpose of forecasting, the autoregressive integrated moving average (ARIMA) model is applied to reveal that ARIMA (1, 2, 0) is the best model to forecast coal production. For coal consumption, the best model identified was ARIMA (0, 2, 1). The models have undergone residual analyses and forecast evaluations to ensure that the ‘best’ models found are statistically appropriate models. The forecasts show that in the following years, Philippine coal production will decrease, while the coal consumption rate will increase. In addition, it is predicted that the Philippines will need to import a total of 133.1983 MMT of coal to meet the coal consumption from 2021 to 2025.

Keywords

ARIMA, Coal Consumption, Coal Production, Data Mining, Energy Forecasting
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  • Application of Time Series Analysis in Forecasting Coal Production and Consumption in the Philippines

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Authors

Samuel John E. Parreno
Master of Teacher Education, University of Mindanao Digos College, Philippines

Abstract


Energy is the backbone of a country’s economic and technological development. For a country to be competitive in the global market and secure sustainable development, energy must be efficiently used. The Philippines’s main source of energy is coal. Historical data shows that the country’s energy and coal consumption has been continuously increasing. With this, careful energy planning is required to develop policies that ensure sufficient energy supply in the future. This paper focuses on forecasting coal production and consumption in the Philippines. For the purpose of forecasting, the autoregressive integrated moving average (ARIMA) model is applied to reveal that ARIMA (1, 2, 0) is the best model to forecast coal production. For coal consumption, the best model identified was ARIMA (0, 2, 1). The models have undergone residual analyses and forecast evaluations to ensure that the ‘best’ models found are statistically appropriate models. The forecasts show that in the following years, Philippine coal production will decrease, while the coal consumption rate will increase. In addition, it is predicted that the Philippines will need to import a total of 133.1983 MMT of coal to meet the coal consumption from 2021 to 2025.

Keywords


ARIMA, Coal Consumption, Coal Production, Data Mining, Energy Forecasting

References