Integrating AI and optimization techniques for sustainable Energy Exchange-Traded Funds (ETFs) forecasting in support of the SDG 7, 9, & 13

¹ Graduate Institute of Automation and Control, National Taiwan University of Science and Technology, Taipei, Taiwan
² Department of Physics, State University of Surabaya, Indonesia

^* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

Optimization using artificial intelligence (AI) is the latest technology for predicting market movements. The implementation of AI in energy exchange-traded funds (ETFs) is a solution for the energy market in the next few years to calculate market risk and formulate appropriate market movements. Optimization reduces risks for investors, especially in the renewable energy market, to ensure the sustainability of the Sustainable Development Goals (SDGs) for affordable and clean energy to support an environmentally friendly industry and greening. The public dataset of energy and alternative energy ETFs from 2013 to 2024 was used as a database for basic energy optimization calculations. Particle Swarm Optimization (PSO) and four other models were compared to obtain a combination of formulation categories that are appropriate for energy market predictions in the following years. The optimization results obtained the highest objective function value of 999941.51, with detailed formulation recommendations using PSO for four alternative energy sources. Energy optimization using AI can be a solution for the market, especially investors, to reduce the worst-case risks of the energy market.