Abstract
Coal as a fossil and non-renewable fuel is one of the most valuable energy minerals in the world with the largest volume reserves. Artificial neural networks (ANN), despite being one of the highest breakthroughs in the field of computational intelligence, has some significant disadvantages, such as slow training, susceptibility to falling into a local optimal points, sensitivity of initial weights, and bias. To overcome these shortcomings, this study presents an improved ANN structure, that is optimized by a proposed hybrid method. The aim of this study is to propose a novel hybrid method for predicting coal consumption in Iran based on socio-economic variables using the bat and grey wolf optimization algorithm with an artificial neural network (BGWAN). For this purpose, data from 1981 to 2019 have been used for modelling and testing the method. The available data are partly used to find the optimal or near-optimal values of the weighting parameters (1980–2014) and partly to test the model (2015–2019). The performance of the BGWAN is evaluated by mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE), standard deviation error (STD), and correlation coefficient (R^2) between the output of the method and the actual dataset. The result of this study showed that BGWAN performance was excellent and proved its efficiency as a useful and reliable tool for monitoring coal consumption or energy demand in Iran.
Highlights
Rising economic growth in developing countries and continued growth in industrialized countries have increased energy demand
A novel hybrid computational intelligence approach for predicting coal consumption in Iran is coded with MATLAB 2019 software
Data on Iran’s population, GDP, import, export, and coal consumption were collected during the years 1981–2019
Summary
Rising economic growth in developing countries and continued growth in industrialized countries have increased energy demand. Various energy sources are used in power plants, which can include fossil fuels (oil, gas, coal) or new energy sources (solar, wind, geothermal, etc.). Environmental issues related to its extraction, processing, and combustion, threaten the sustainability of coal use. Environmental issues, including the factors that have led to climate change through the emission of carbon dioxide, are of global concern, and this is one of the serious problems that coal will face in the future and threaten its sustainability [1,2]. Coal will continue to be a major player in the global energy spectrum for at least the two to three decades, coal’s sustainable future still depends on reducing its pollution
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