TY - JOUR
T1 - A Novel Computational Intelligence Approach for Coal Consumption Forecasting in Iran
AU - Jalaee, Mahdis sadat
AU - Shakibaei, Alireza
AU - GhasemiNejad, Amin
AU - Jalaee, Sayyed Abdolmajid
AU - Derakhshani, Reza
N1 - Funding Information:
This research received no external funding, and the APC was funded by Utrecht Univer-
Funding Information:
This research received no external funding, and the APC was funded by Utrecht University. Acknowledgments: This work is the outcome of a joint research study with Shahid Bahonar University of Kerman, Iran, and Utrecht University, Netherlands. The authors appreciate the Department of Earth Sciences of Utrecht University for the research support. We also thank the Shahid Bahonar University of Kerman for its active collaboration in this research.
Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021/7/2
Y1 - 2021/7/2
N2 - 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.
AB - 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.
KW - Coal consumption
KW - Computational intelligence
KW - Optimization
KW - Socio-economic variables
UR - http://www.scopus.com/inward/record.url?scp=85110587914&partnerID=8YFLogxK
U2 - 10.3390/su13147612
DO - 10.3390/su13147612
M3 - Article
SN - 2071-1050
VL - 13
SP - 1
EP - 16
JO - Sustainability
JF - Sustainability
IS - 14
M1 - 7612
ER -