Document Type : Original Article


1 School of Economics, Anyang Normal University, Anyang, 455000, China

2 School of Management, Universiti Sains Malaysia, Minden, Penang 11800, Malaysia

3 Department of Management, Sunway University Business School (SUBS)

4 Fakulti Ekonomi dan Pengurusan, Universiti Kebangsaan Malaysia (UKM)

5 Faculty of Economics and Business, Universiti Malaysia Sarawak



A new methodology is suggested in this study for providing an optimum energy demand forecasting for the future projections. The paper presents an improved version of manta ray foraging optimizer (iMRFO) for giving an optimum and suitable forecasting model. The model designing has been done on Taiwan as the case study. The optimized forecasting is performed based on three models, including linear, exponential, and quadratic models where their coefficients are optimized by the suggested iMRFO algorithm based on different affective factors containing yearly growth rate of the real GDP, yearly growth rate of the population, annual industry share in growth rate of GDP, annual rate of urbanization, and annual coal consumption. Simulation results showed that using the proposed -energy demand prediction technique based on iMRFO has higher accuracy and reliability prediction in the direction of the other compared methods from the literature, such as ACO, GA/PSO, basic MRFO-based, and multiple linear regression models. Two different scenarios have been measured for more analyzing the suggested method. The results finally show that energy intensity in Taiwan will decline in varying degrees based on both scenarios which indicates that additional growth of efficient strategies and actions is needed for ensuring that the target is accomplished.


1.    Akbary, P., et al., Extracting appropriate nodal marginal prices for all types of committed reserve. Computational Economics, 2019. 53(1): p. 1-26.
2.    Ghadimi, N., A method for placement of distributed generation (DG) units using particle swarm optimization. International Journal of Physical Sciences, 2013. 8(27): p. 1417-1423.
3.    Ghadimi, N., A. Afkousi-Paqaleh, and A. Emamhosseini, A PSO-based fuzzy long-term multi-objective optimization approach for placement and parameter setting of UPFC. Arabian Journal for Science and Engineering, 2014. 39(4): p. 2953-2963.
4.    Ghiasi, M., N. Ghadimi, and E. Ahmadinia, An analytical methodology for reliability assessment and failure analysis in distributed power system. SN Applied Sciences, 2019. 1(1): p. 44.
5.    Cao, Y., et al., Multi-objective optimization of a PEMFC based CCHP system by meta-heuristics. Energy Reports, 2019.
6.    Taiwan Electricity Consumption: Total. Bureau of Energy, Ministry of Economic Affairs 2020; Available from:
7.    Energy Statistical annual Reports    2020; Available from:
8.    Energy consumption in Taiwan in 2018. 2020; Available from:
9.    Tian, M.-W., et al., New optimal design for a hybrid solar chimney, solid oxide electrolysis and fuel cell based on improved deer hunting optimization algorithm. Journal of Cleaner Production, 2020. 249: p. 119414.
10.    Leandro Sopeletto Carreiro , V.V.E., Mikhail P. Vishnevski , Wilma D. Huacasi , Albany E. Herrmann, Hermes José Loschi, et al. Poemathics. in Proceedings of the 4th Brazilian Technology Symposium (BTSym’18). 2019. Springer Nature.
11.    Gollou, A.R. and N. Ghadimi, A new feature selection and hybrid forecast engine for day-ahead price forecasting of electricity markets. Journal of Intelligent & Fuzzy Systems, 2017. 32(6): p. 4031-4045.
12.    Hagh, M.T., H. Ebrahimian, and N. Ghadimi, Hybrid intelligent water drop bundled wavelet neural network to solve the islanding detection by inverter-based DG. Frontiers in Energy, 2015. 9(1): p. 75-90.
13.    Fei, X., et al., Optimal configuration and energy management for combined solar chimney, solid oxide electrolysis, and fuel cell: a case study in Iran. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2019: p. 1-21.
14.    Tran, D.-H., D.-L. Luong, and J.-S. Chou, Nature-inspired metaheuristic ensemble model for forecasting energy consumption in residential buildings. Energy, 2020. 191: p. 116552.
15.    Wu, W., et al., Forecasting short-term renewable energy consumption of China using a novel fractional nonlinear grey Bernoulli model. Renewable energy, 2019. 140: p. 70-87.
16.    Wang, Z.-X., L.-Y. He, and H.-H. Zheng, Forecasting the residential solar energy consumption of the United States. Energy, 2019. 178: p. 610-623.
17.    Yan, K., et al., A hybrid LSTM neural network for energy consumption forecasting of individual households. IEEE Access, 2019. 7: p. 157633-157642.
18.    Somu, N., G.R. MR, and K. Ramamritham, A hybrid model for building energy consumption forecasting using long short term memory networks. Applied Energy, 2020. 261: p. 114131.
19.    Fan, X., et al., Multi-objective optimization for the proper selection of the best heat pump technology in a fuel cell-heat pump micro-CHP system. Energy Reports, 2020. 6: p. 325-335.
20.    Liu, Y., W. Wang, and N. Ghadimi, Electricity load forecasting by an improved forecast engine for building level consumers. Energy, 2017. 139: p. 18-30.
21.    Ghadimi, N., An adaptive neuro‐fuzzy inference system for islanding detection in wind turbine as distributed generation. Complexity, 2015. 21(1): p. 10-20.
22.    Hashemi, F., N. Ghadimi, and B. Sobhani, Islanding detection for inverter-based DG coupled with using an adaptive neuro-fuzzy inference system. International Journal of Electrical Power & Energy Systems, 2013. 45(1): p. 443-455.
23.    Mirzapour, F., et al., A new prediction model of battery and wind-solar output in hybrid power system. Journal of Ambient Intelligence and Humanized Computing, 2019. 10(1): p. 77-87.
24.    Yu, D., et al., System identification of PEM fuel cells using an improved Elman neural network and a new hybrid optimization algorithm. Energy Reports, 2019. 5: p. 1365-1374.
25.    Zhao, W., Z. Zhang, and L. Wang, Manta ray foraging optimization: An effective bio-inspired optimizer for engineering applications. Engineering Applications of Artificial Intelligence, 2020. 87: p. 103300.
26.    Liu, Q., et al., Computer-aided breast cancer diagnosis based on image segmentation and interval analysis. Automatika, 2020. 61(3): p. 496-506.
27.    Mir, M., et al., Employing a Gaussian Particle Swarm Optimization method for tuning Multi Input Multi Output‐fuzzy system as an integrated controller of a micro‐grid with stability analysis. Computational Intelligence, 2020. 36(1): p. 225-258.
28.    Xu, Z., et al., Computer-aided diagnosis of skin cancer based on soft computing techniques. Open Medicine, 2020. 15(1): p. 860-871.
29.    Dhiman, G. and V. Kumar, Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications. Advances in Engineering Software, 2017. 114: p. 48-70.
30.    Dhiman, G. and V. Kumar, Emperor penguin optimizer: A bio-inspired algorithm for engineering problems. Knowledge-Based Systems, 2018. 159: p. 20-50.
31.    Mirjalili, S., S.M. Mirjalili, and A. Hatamlou, Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Computing and Applications, 2016. 27(2): p. 495-513.
32.    Howarth, R.B., L. Schipper, and B. Andersson, The structure and intensity of energy use: trends in five OECD nations. The Energy Journal, 1993. 14(2).
33.    World Energy Outlook. 2010, International Energy Agency: France.
34.    Zhang, G., et al., Optimal parameter extraction of PEM fuel cells by meta-heuristics. International Journal of Ambient Energy, 2020: p. 1-10.
35.    Lee, C.-M. and E.R. Rosalez, Economic growth, carbon abatement technology and decoupling strategy-The case of Taiwan. Aerosol and Air Quality Research, 2017. 17(6): p. 1649-1657.
36.    Taiwan Coal. 2020; Available from:,total%20consumption%20of%201%2C139%2C471%2C430%20tons.
37.    Global economy. 2020; Available from:
38.    Taiwan GDP Annual Growth Rate. 2020; Available from:
39.    Taiwan Population Growth Rate. 2020; Available from:,a%200.24%25%20increase%20from%202016.
40.    Energy Transition in Taiwan. Webinar Series: Sustainable Energy in Humanitarian Settings 2020; Available from: