A Novel Hybrid Artificial Intelligence Approach to the Future of Global Coal Consumption Using Whale Optimization Algorithm and Adaptive Neuro-Fuzzy Inference System
Abstract
:1. Introduction
Related Works
2. Materials and Methods
2.1. Adaptive Neuro-Fuzzy Inference System
2.2. Whale Optimization Algorithm
3. Proposed Method
4. Experiments
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Acknowledgments
Conflicts of Interest
References
- Jalaee, S.A.; Ghaseminejad, A.; Lashkary, M.; Jafari, M.R. Forecasting Iran’s Energy Demand Using Cuckoo Optimization Algorithm. Math. Probl. Eng. 2019, 2019, 2041756. [Google Scholar] [CrossRef]
- Conti, J.; Holtberg, P.; Diefenderfer, J.; LaRose, A.; Turnure, J.T.; Westfall, L. International Energy Outlook 2016 with Rojections to 2040; DOE/EIA-0484; EIA: Washington, DC, USA, 2016. [CrossRef] [Green Version]
- Mehrabi, B.A.; Derakhshani, R.; Nilfouroushan, F.; Rahnamarad, J.; Azarafza, A.M. Spatiotemporal subsidence over Pabdana coal mine Kerman Province, central Iran using time-series of Sentinel-1 remote sensing imagery. Episodes 2022. [Google Scholar] [CrossRef]
- Derakhshani, R.; Raoof, A.; Mahvi, A.H.; Chatrouz, H. Similarities in the Fingerprints of Coal Mining Activities, High Ground Water Fluoride, and Dental Fluorosis in Zarand District, Kerman Province, Iran. Fluoride 2020, 53, 257–267. [Google Scholar]
- Jalaee, S.A.; Lashkary, M.; GhasemiNejad, A. The Phillips curve in Iran: Econometric versus artificial neural networks. Heliyon 2019, 5, e02344. [Google Scholar] [CrossRef] [Green Version]
- Jalaee, S.A.; Shakibaei, A.; Akbarifard, H.; Horry, H.R.; GhasemiNejad, A.; Robati, F.N.; Zarin, N.A.; Derakhshani, R. A novel hybrid method based on Cuckoo optimization algorithm and artificial neural network to forecast world’s carbon dioxide emission. MethodsX 2021, 8, 101310. [Google Scholar] [CrossRef]
- Hourmand, M.; Sarhan, A.A.D.; Farahany, S.; Sayuti, M. Microstructure characterization and maximization of the material removal rate in nano-powder mixed EDM of Al-Mg2Si metal matrix composite—ANFIS and RSM approaches. Int. J. Adv. Manuf. Technol. 2018, 101, 2723–2737. [Google Scholar] [CrossRef]
- Karaboga, D.; Kaya, E. Adaptive network based fuzzy inference system (ANFIS) training approaches: A comprehensive survey. Artif. Intell. Rev. 2019, 52, 2263–2293. [Google Scholar] [CrossRef]
- Adedeji, P.; Madushele, N.; Akinlabi, S. Adaptive Neuro-fuzzy Inference System (ANFIS) for a multi-campus institution energy consumption forecast in South Africa. In Proceedings of the International Conference on Industrial Engineering and Operation Management, Washington, DC, USA, 27–29 September 2018. [Google Scholar]
- Ejaz, N.; Abbasi, S. Wheat yield prediction using neural network and integrated svm-nn with regression. Pak. J. Eng. Technol. Sci. 2020, 8, 77–97. [Google Scholar]
- Bhojani, S.H.; Bhatt, N. Wheat crop yield prediction using new activation functions in neural network. Neural Comput. Appl. 2020, 32, 13941–13951. [Google Scholar] [CrossRef]
- Gopal, P.M.; Bhargavi, R. A novel approach for efficient crop yield prediction. Comput. Electron. Agric. 2019, 165, 104968. [Google Scholar] [CrossRef]
- Samuel, O.D.; Okwu, M.O.; Oyejide, O.J.; Taghinezhad, E.; Afzal, A.; Kaveh, M. Optimizing biodiesel production from abundant waste oils through empirical method and grey wolf optimizer. Fuel 2020, 281, 118701. [Google Scholar] [CrossRef]
- Amenaghawon, A.N.; Evbarunegbe, N.I.; Obahiagbon, K. Optimum biodiesel production from waste vegetable oil using functionalized cow horn catalyst: A comparative evaluation of some expert systems. Clean. Eng. Technol. 2021, 4, 100184. [Google Scholar] [CrossRef]
- Yu, T.; Zhu, H. Hyper-parameter optimization: A review of algorithms and applications. arXiv 2020, arXiv:2003.05689. [Google Scholar] [CrossRef]
- Sinha, T.; Haidar, A.; Verma, B. Particle Swarm Optimization Based Approach for Finding Optimal Values of Convolutional Neural Network Parameters. In Proceedings of the 2018 IEEE Congress on Evolutionary Computation (CEC), Rio de Janeiro, Brazil, 8–13 July 2018; IEEE: Piscataway, NJ, USA, 2018; pp. 1–6. [Google Scholar]
- Moayedi, H.; Nguyen, H.; Foong, L.K. Nonlinear evolutionary swarm intelligence of grasshopper optimization algorithm and gray wolf optimization for weight adjustment of neural network. Eng. Comput. 2021, 37, 1265–1275. [Google Scholar] [CrossRef]
- Bacanin, N.; Bezdan, T.; Tuba, E.; Strumberger, I.; Tuba, M. Optimizing Convolutional Neural Network Hyperparameters by Enhanced Swarm Intelligence Metaheuristics. Algorithms 2020, 13, 67. [Google Scholar] [CrossRef] [Green Version]
- Elaziz, M.A.; Elsheikh, A.H.; Sharshir, S.W. Improved prediction of oscillatory heat transfer coefficient for a thermoacoustic heat exchanger using modified adaptive neuro-fuzzy inference system. Int. J. Refrig. 2019, 102, 47–54. [Google Scholar] [CrossRef]
- Jang, J.-S.R. ANFIS: Adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 1993, 23, 665–685. [Google Scholar] [CrossRef]
- Ahmed, K.; Ewees, A.A.; El Aziz, M.A.; Hassanien, A.E.; Gaber, T.; Tsai, P.-W.; Pan, J.-S. A Hybrid Krill-ANFIS Model for Wind Speed Forecasting. In Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016, Cairo, Egypt, 24–26 November 2016; Advances in Intelligent Systems and Computing; Springer Science and Business Media LLC: Berlin/Heidelberg, Germany, 2016; Volume 533, pp. 365–372. [Google Scholar]
- An, S.; Shi, H.; Hu, Q.; Li, X.; Dang, J. Fuzzy rough regression with application to wind speed prediction. Inf. Sci. 2014, 282, 388–400. [Google Scholar] [CrossRef]
- El Aziz, M.A.; Hemdan, A.M.; Ewees, A.A.; Elhoseny, M.; Shehab, A.; Hassanien, A.E.; Xiong, S. Prediction of biochar yield using adaptive neuro-fuzzy inference system with particle swarm optimization. In Proceedings of the 2017 IEEE PES PowerAfrica, Accra, Ghana, 27–30 June 2017; Institute of Electrical and Electronics Engineers (IEEE): Piscataway, NJ, USA, 2017; pp. 115–120. [Google Scholar]
- Ewees, A.A.; El Aziz, M.A.; Elhoseny, M. Social-spider optimization algorithm for improving ANFIS to predict biochar yield. In Proceedings of the 2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Delhi, India, 3–5 July 2017; IEEE: Piscataway, NJ, USA, 2017; pp. 1–6. [Google Scholar]
- Ramedani, Z.; Omid, M.; Keyhani, A.; Khoshnevisan, B.; Saboohi, H. A comparative study between fuzzy linear regression and support vector regression for global solar radiation prediction in Iran. Sol. Energy 2014, 109, 135–143. [Google Scholar] [CrossRef]
- Hossain, M.; Mekhilef, S.; Afifi, F.; Halabi, L.M.; Olatomiwa, L.; Seyedmahmoudian, M.; Horan, B.; Stojcevski, A. Application of the hybrid ANFIS models for long term wind power density prediction with extrapolation capability. PLoS ONE 2018, 13, e0193772. [Google Scholar] [CrossRef] [Green Version]
- Olatunji, O.; Akinlabi, S.; Madushele, N.; Adedeji, P.A. Estimation of Municipal Solid Waste (MSW) combustion enthalpy for energy recovery. EAI Endorsed Trans. Energy Web 2019, 6, 6. [Google Scholar] [CrossRef] [Green Version]
- Olatunji, O.O.; Akinlabi, S.; Madushele, N.; Adedeji, P.A. Estimation of the Elemental Composition of Biomass Using Hybrid Adaptive Neuro-Fuzzy Inference System. BioEnergy Res. 2019, 12, 642–652. [Google Scholar] [CrossRef]
- Adedeji, P.A.; Masebinu, S.O.; Akinlabi, S.A.; Madushele, N. Adaptive Neuro-fuzzy Inference System (ANFIS) Modelling in Energy System and Water Resources. In Optimization Using Evolutionary Algorithms and Metaheuristics; CRC Press: Boca Raton, FL, USA, 2019; pp. 117–133. [Google Scholar]
- Karaboga, D.; Kaya, E. An adaptive and hybrid artificial bee colony algorithm (aABC) for ANFIS training. Appl. Soft Comput. 2016, 49, 423–436. [Google Scholar] [CrossRef]
- Liu, P.; Leng, W.; Fang, W. Training ANFIS Model with an Improved Quantum-Behaved Particle Swarm Optimization Algorithm. Math. Probl. Eng. 2013, 2013, 595639. [Google Scholar] [CrossRef]
- Mirjalili, S.; Lewis, A. The Whale Optimization Algorithm. Adv. Eng. Softw. 2016, 95, 51–67. [Google Scholar] [CrossRef]
- Ghahremani-Nahr, J.; Kian, R.; Sabet, E. A robust fuzzy mathematical programming model for the closed-loop supply chain network design and a whale optimization solution algorithm. Expert Syst. Appl. 2019, 116, 454–471. [Google Scholar] [CrossRef] [Green Version]
- Maroufpoor, S.; Maroufpoor, E.; Bozorg-Haddad, O.; Shiri, J.; Yaseen, Z.M. Soil moisture simulation using hybrid artificial intelligent model: Hybridization of adaptive neuro fuzzy inference system with grey wolf optimizer algorithm. J. Hydrol. 2019, 575, 544–556. [Google Scholar] [CrossRef]
- Donyaii, A.; Sarraf, A.; Ahmadi, H. A Novel Approach to Supply the Water Reservoir Demand Based on a Hybrid Whale Optimization Algorithm. Shock Vib. 2020, 2020, 8833866. [Google Scholar] [CrossRef]
- El-Hasnony, I.M.; Barakat, S.I.; Mostafa, R.R. Optimized ANFIS Model Using Hybrid Metaheuristic Algorithms for Parkinson’s Disease Prediction in IoT Environment. IEEE Access 2020, 8, 119252–119270. [Google Scholar] [CrossRef]
- Bui, D.T.; Bui, Q.-T.; Nguyen, Q.-P.; Pradhan, B.; Nampak, H.; Trinh, P.T. A hybrid artificial intelligence approach using GIS-based neural-fuzzy inference system and particle swarm optimization for forest fire susceptibility modeling at a tropical area. Agric. For. Meteorol. 2017, 233, 32–44. [Google Scholar] [CrossRef]
- BritishPetroleum. bp Energy Outlook. 2020. Available online: https://www.bp.com/energyoutlook (accessed on 27 October 2020).
- Jalaee, M.S.; Shakibaei, A.; GhasemiNejad, A.; Jalaee, S.A.; Derakhshani, R. A Novel Computational Intelligence Approach for Coal Consumption Forecasting in Iran. Sustainability 2021, 13, 7612. [Google Scholar] [CrossRef]
Variable | Minimum | Maximum |
---|---|---|
World population (millions) | 5012 | 7673 |
GDP (billion USD) | 17,200 | 87,697 |
Coal consumption (million tons oil equivalent) | 2162 | 3867 |
Primary energy consumption (million tons oil equivalent) | 7581.30 | 14,072.87 |
Northwest Europe coal price (USD) | 28.79 | 147.67 |
Network Process | MSE | RMSE | MAE | STD Error | Mean Error | R |
---|---|---|---|---|---|---|
Training | 6.5577 × 10−11 | 8.098 × 10−6 | 1.63 × 10−8 | 8.2316 × 10−6 | 6.3215 × 10−8 | 0.9991 |
Testing | 2.3028 × 10−5 | 0.0047987 | 1.13 × 10−6 | 0.004478 | −0.0023408 | 0.9850 |
Years | Actual Data | WOANFIS—Output Predicted | Relative Error (%) |
---|---|---|---|
2015 | 3769 | 3745 | 0.6367 |
2016 | 3710 | 3702 | 0.2156 |
2017 | 3718 | 3716 | 0.0645 |
2018 | 3772 | 3759 | 0.3472 |
2019 | 3798 | 3788 | 0.2765 |
Average | - | - | 0.3081 |
Years | 2020 | 2025 | 2030 |
---|---|---|---|
WOANFIS method | 3894.80 | 4065.80 | 4071.09 |
BP (2019) | 3896.84 | 4066.99 | 4072.32 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Jalaee, M.s.; GhasemiNejad, A.; Jalaee, S.A.; Amani Zarin, N.; Derakhshani, R. A Novel Hybrid Artificial Intelligence Approach to the Future of Global Coal Consumption Using Whale Optimization Algorithm and Adaptive Neuro-Fuzzy Inference System. Energies 2022, 15, 2578. https://doi.org/10.3390/en15072578
Jalaee Ms, GhasemiNejad A, Jalaee SA, Amani Zarin N, Derakhshani R. A Novel Hybrid Artificial Intelligence Approach to the Future of Global Coal Consumption Using Whale Optimization Algorithm and Adaptive Neuro-Fuzzy Inference System. Energies. 2022; 15(7):2578. https://doi.org/10.3390/en15072578
Chicago/Turabian StyleJalaee, Mahdis sadat, Amin GhasemiNejad, Sayyed Abdolmajid Jalaee, Naeeme Amani Zarin, and Reza Derakhshani. 2022. "A Novel Hybrid Artificial Intelligence Approach to the Future of Global Coal Consumption Using Whale Optimization Algorithm and Adaptive Neuro-Fuzzy Inference System" Energies 15, no. 7: 2578. https://doi.org/10.3390/en15072578