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.
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