Designing Quantum-Enabled Intelligent Systems for Sustainable Energy Solutions

Abstract

The increasing demand for energy, in addition to the problems posed by conventional energy systems to the environment, necessitate the implementation of energy systems which are more efficient and sustainable. The introduction of alternative energy sources such as solar, wind and hydro-energy has become increasingly important in addressing such challenges; however, due to the nature of renewable energy resources, current energy systems face inefficiencies and inconsistencies in incorporating them into their operations. In this paper, a new Quantum-Enabled Intelligent Energy System will be introduced. The system makes use of quantum computing and artificial intelligence capabilities in order to maximize the performance of the energy system. In particular, the system aims to address the most significant issues of energy distribution, load balancing and prediction of renewable energy sources. By taking advantage of the computational power of quantum computing and prediction capabilities of artificial intelligence, the proposed system can greatly enhance energy efficiency and energy system performance. However, the outputs of the proposed model can be considered rather impressive – for example, a    15 % reduction in energy consumption, 5 % improvement in system reliability, and a 20 % improvement in throughput and renewable energy usage. Even though the quantum-enabled approach takes 25% more time to optimize (from 40 seconds to 50 seconds), the advantages of the overall performance in regard to energy management and sustainability are significantly higher. In addition, issues related to implementation (hardware limitations, computing speed, compatibility with the existing energy systems) are highlighted, and future research is suggested for improvement of scale, privacy of the data, and energy systems optimization with the use of quantum technology.