Volume 17 • Issue 2 • PP: 250-259 • 2025
An Optimized Artificial Neural Network Model Using JAYA Algorithm for Energy Consumption Forecasting in Oman
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© 2025 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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The Accurate energy forecasting is vital for strategic planning, particularly in de-veloping economies with rapidly evolving demand patterns. This study pro-poses a hybrid Artificial Neural Network (ANN) model optimized using a modified JAYA algorithm to forecast energy consumption in Oman. The JAYA algorithm’s parameter-free, metaheuristic search improves ANN train-ing by enhancing convergence speed and reducing the risk of local minima. Historical data from 2017–2021—comprising GDP, population, and oil and gas production—were used as inputs. Model performance was benchmarked against an ANN trained with the Artificial Bee Colony (ABC) algorithm using mean square error (MSE), mean absolute error (MAE), relative error (RE), and root mean square error (RMSE) as evaluation metrics. Results show that ANN–JAYA consistently outperformed ANN–ABC, achieving lower error rates and greater robustness. The proposed approach offers a reliable deci-sion-support tool for policymakers and energy authorities, enabling more ef-fective resource allocation and long-term planning. Future research will ex-tend the framework to integrate renewable energy indicators and real-time data for adaptive, sustainable forecasting.
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References
[1] J. H. Yousif, H. A. Al-Balushi, H. A. Kazem, and M. T. Chaichan, “Analysis and forecasting of weather conditions in Oman for renewable energy applications,” Case Stud. Therm. Eng., vol. 13, p. 100355, 2019.
[2] M. Črepinšek, S.-H. Liu, M. Mernik, and M. Ravber, “Long term memory assistance for evolutionary algorithms,” Mathematics, vol. 7, no. 11, pp. 1–29, 2019.
[3] R. K. Gupta, R. K. Gupta, and M. Kumar, “Optimization of Heat Exchanger Design Using Hybrid Genetic Algorithm,” Appl. Energy, vol. 275, p. 115433, 2020, doi: 10.1016/j.apenergy.2020.115433.
[4] M. Mehrabi, B. Pradhan, H. Moayedi, and A. Alamri, “Optimizing an adaptive neuro-fuzzy inference system for spatial prediction of landslide susceptibility using four state-of-the-art metaheuristic techniques,” Sensors, vol. 20, no. 6, p. 1723, 2020.
[5] K. C. More and R. V. Rao, “Design optimization of plate-fin heat exchanger by using modified Jaya algorithm,” in Advanced Engineering Optimization Through Intelligent Techniques, Singapore: Springer, 2020, pp. 165–172.
[6] R. V. Rao, “Applications of jaya algorithm and its modified versions to different disciplines of engineering and sciences,” in Jaya: An Advanced Optimization Algorithm and its Engineering Applications, Cham, Switzerland: Springer, 2019, pp. 291–310.
[7] S. Roudari, A. Sadeghi, S. Gholami, W. Mensi, and K. H. Al-Yahyaee, “Dynamic spillovers among natural gas, liquid natural gas, trade policy uncertainty, and stock market,” Resour. Policy, vol. 83, p. 103688, 2023.
[8] P. Kumari and D. Toshniwal, “Deep learning models for solar irradiance forecasting: A comprehensive review,” J. Clean. Prod., vol. 318, p. 128566, 2021.
[9] N. Van Song et al., “The influence of economic and non-economic determinants on the sustainable energy consumption: evidence from Vietnam economy,” Environ. Sci. Pollut. Res., pp. 1–14, 2023.
[10] Rahimikhoob, “Estimating global solar radiation using artificial neural network and air temperature data in a semi-arid environment,” Renew. Energy, vol. 35, no. 9, pp. 2131–2135, 2010.
[11] M. Miljanovic, “Comparative analysis of recurrent and finite impulse response neural networks in time series prediction,” Indian J. Comput. Sci. Eng., vol. 3, no. 1, pp. 180–191, 2012.
[12] D. Karaboga, “An idea based on honey bee swarm for numerical optimization,” Erciyes Univ., Kayseri, Turkey, Tech. Rep. TR06, 2005.
[13] C. Ozkan, O. Kisi, and B. Akay, “Neural networks with artificial bee colony algorithm for modeling daily reference evapotranspiration,” Irrig. Sci., vol. 29, no. 5, pp. 431–441, 2011.
[14] J. Wang and X. Liang, “The forecast of energy demand on artificial neural network,” in 2009 International Conference on Artificial Intelligence and Computational Intelligence, Shanghai, China, 2009, vol. 3, pp. 31–35.
[15] L. G. Swan and V. I. Ugursal, “Modeling of end-use energy consumption in the residential sector: A review of modeling techniques,” Renew. Sustain. Energy Rev., vol. 13, no. 8, pp. 1819–1835, 2009.
[16] Y. S. Murat and H. Ceylan, “Use of artificial neural networks for transport energy demand modeling,” Energy Policy, vol. 34, no. 17, pp. 3165–3172, 2006.
[17] Economou, “Renewable energy resources and sustainable development in Mykonos (Greece),” Renew. Sustain. Energy Rev., vol. 14, no. 5, pp. 1496–1501, 2010.
[18] J. Wang and X. Liang, “The forecast of energy demand on artificial neural network,” in 2009 International Conference on Artificial Intelligence and Computational Intelligence, Shanghai, China, 2009, vol. 3, pp. 31–35.
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